diff --git a/.gitignore b/.gitignore index 7909d367..0b468357 100755 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# SPISEA-generated isochrones from test functions +spisea/tests/isochrones/ + # Compiled files *.py[co] *.a @@ -51,4 +54,7 @@ distribute-*.tar.gz # OS Generated Files .DS_Store -._* \ No newline at end of file +._* + +# Test files generated +spisea/tests/isochrones diff --git a/.readthedocs.yml b/.readthedocs.yml index 40ae08cb..a86dc535 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -13,7 +13,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.7" + python: "3.10" # Build documentation in the docs/ directory with Sphinx diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000..9cecc1d4 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 2c409d1c..00000000 --- a/MANIFEST.in +++ /dev/null @@ -1,16 +0,0 @@ -include README.md -include CHANGES.rst -include setup.cfg -include LICENSE.rst -include pyproject.toml - -recursive-include spisea *.pyx *.c *.pxd -recursive-include docs * -recursive-include licenses * -recursive-include scripts * - -prune build -prune docs/_build -prune docs/api - -global-exclude *.pyc *.o diff --git a/docs/Cluster_w_COSMIC.ipynb b/docs/Cluster_w_COSMIC.ipynb index 3cec4912..00314a16 100755 --- a/docs/Cluster_w_COSMIC.ipynb +++ b/docs/Cluster_w_COSMIC.ipynb @@ -60,12 +60,1059 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "90c33d5c-7c52-4dd1-9545-25b00db26032", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/nsabrams/code/multiplicity/PyPopStar/spisea/atmospheres.py:1669: UserWarning: Only `temperature` keyword is used for black-body atmosphere\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB 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atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=2.50\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=3.00\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=3.50\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=4.00\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=4.50\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=5.00\n", + "Changing to met=0.50 for met=0.30 T= 1100 logg=5.50\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=2.50\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=3.00\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=3.50\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=4.00\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=4.50\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=5.00\n", + "Changing to met=0.50 for met=0.30 T= 1200 logg=5.50\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB 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atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "BB atmosphere\n", + "Atmosphere grid generation took 810.611348 s.\n", + "Making photometry for atmosphere grid: AKs = 0.00 dist = 4000\n", + " Starting at: 2026-06-10 22:10:31.613038 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1000.000 Msun T = 2 K m_ubv_U = 44.31\n", + "M = 1000.000 Msun T = 4 K m_ubv_U = 44.31\n", + "M = 1000.000 Msun T = 4 K m_ubv_U = 44.31\n", + "M = 1000.000 Msun T = 6 K m_ubv_U = 44.31\n", + "M = 1400.000 Msun T = 4 K m_ubv_U = 36.26\n", + "M = 2600.000 Msun T = 3 K m_ubv_U = 26.83\n", + "M = 2600.000 Msun T = 4 K m_ubv_U = 27.90\n", + "M = 3400.000 Msun T = 2 K m_ubv_U = 25.51\n", + "M = 4100.000 Msun T = 6 K m_ubv_U = 21.99\n", + "M = 4900.000 Msun T = 4 K m_ubv_U = 19.67\n", + "M = 3000.000 Msun T = 2 K m_ubv_U = 26.78\n", + "M = 3800.000 Msun T = 0 K m_ubv_U = 24.84\n", + "M = 4500.000 Msun T = 4 K m_ubv_U = 20.73\n", + "M = 2600.000 Msun T = 2 K m_ubv_U = 28.57\n", + "M = 3400.000 Msun T = 0 K m_ubv_U = 25.71\n", + "M = 4100.000 Msun T = 5 K m_ubv_U = 21.94\n", + "M = 4900.000 Msun T = 3 K m_ubv_U = 19.64\n", + "M = 3000.000 Msun T = 1 K m_ubv_U = 26.41\n", + "M = 3700.000 Msun T = 6 K m_ubv_U = 23.58\n", + "M = 4500.000 Msun T = 4 K m_ubv_U = 20.69\n", + "M = 2600.000 Msun T = 2 K m_ubv_U = 27.43\n", + "M = 3300.000 Msun T = 6 K m_ubv_U = 25.94\n", + "M = 4100.000 Msun T = 4 K m_ubv_U = 22.11\n", + "M = 4900.000 Msun T = 2 K m_ubv_U = 19.99\n", + "M = 3000.000 Msun T = 0 K m_ubv_U = 27.51\n", + "M = 3700.000 Msun T = 4 K m_ubv_U = 23.36\n", + "M = 4500.000 Msun T = 2 K m_ubv_U = 21.24\n", + "M = 2900.000 Msun T = 2 K m_ubv_U = 26.08\n", + "M = 4100.000 Msun T = 6 K m_ubv_U = 22.37\n", + "M = 4900.000 Msun T = 4 K m_ubv_U = 20.42\n", + "M = 3000.000 Msun T = 2 K m_ubv_U = 26.58\n", + "M = 3700.000 Msun T = 6 K m_ubv_U = 23.41\n", + "M = 4500.000 Msun T = 4 K m_ubv_U = 21.75\n", + "M = 2600.000 Msun T = 2 K m_ubv_U = 27.09\n", + "M = 3400.000 Msun T = 0 K m_ubv_U = 27.04\n", + "M = 4100.000 Msun T = 4 K m_ubv_U = 22.80\n", + "M = 4900.000 Msun T = 2 K m_ubv_U = 21.51\n", + "M = 5000.000 Msun T = 2 K m_ubv_U = 19.43\n", + "M = 10500.000 Msun T = 3 K m_ubv_U = 15.28\n", + "M = 18000.000 Msun T = 5 K m_ubv_U = 13.73\n", + "M = 47000.000 Msun T = 4 K m_ubv_U = 11.39\n", + "M = 7750.000 Msun T = 1 K m_ubv_U = 16.58\n", + "M = 11000.000 Msun T = 2 K m_ubv_U = 15.06\n", + "M = 21000.000 Msun T = 3 K m_ubv_U = 13.18\n", + "M = 5500.000 Msun T = 2 K m_ubv_U = 18.80\n", + "M = 8000.000 Msun T = 2 K m_ubv_U = 16.52\n", + "M = 11250.000 Msun T = 4 K m_ubv_U = 15.20\n", + "M = 23000.000 Msun T = 4 K m_ubv_U = 13.08\n", + "M = 5750.000 Msun T = 3 K m_ubv_U = 18.34\n", + "M = 8250.000 Msun T = 4 K m_ubv_U = 16.44\n", + "M = 11750.000 Msun T = 4 K m_ubv_U = 15.00\n", + "M = 25000.000 Msun T = 5 K m_ubv_U = 12.92\n", + "M = 6000.000 Msun T = 4 K m_ubv_U = 18.00\n", + "M = 8750.000 Msun T = 2 K m_ubv_U = 16.02\n", + "M = 12250.000 Msun T = 4 K m_ubv_U = 14.87\n", + "M = 28000.000 Msun T = 4 K m_ubv_U = 12.59\n", + "M = 6250.000 Msun T = 4 K m_ubv_U = 17.70\n", + "M = 9000.000 Msun T = 4 K m_ubv_U = 16.08\n", + "M = 12750.000 Msun T = 4 K m_ubv_U = 14.57\n", + "M = 31000.000 Msun T = 4 K m_ubv_U = 12.15\n", + "M = 6750.000 Msun T = 0 K m_ubv_U = 17.54\n", + "M = 9500.000 Msun T = 3 K m_ubv_U = 15.66\n", + "M = 14000.000 Msun T = 3 K m_ubv_U = 14.20\n", + "M = 35000.000 Msun T = 4 K m_ubv_U = 11.81\n", + "M = 7000.000 Msun T = 2 K m_ubv_U = 17.34\n", + "M = 10000.000 Msun T = 2 K m_ubv_U = 15.29\n", + "M = 16000.000 Msun T = 3 K m_ubv_U = 13.75\n", + "M = 39000.000 Msun T = 4 K m_ubv_U = 11.60\n", + "M = 7250.000 Msun T = 2 K m_ubv_U = 17.16\n", + "M = 50000.000 Msun T = 8 K m_ubv_U = 11.28\n", + "M = 14000.000 Msun T = 8 K m_ubv_U = 14.38\n", + "M = 13750.000 Msun T = 8 K m_ubv_U = 14.44\n", + "M = 10750.000 Msun T = 7 K m_ubv_U = 15.32\n", + "M = 16500.000 Msun T = 8 K m_ubv_U = 13.90\n", + "M = 6750.000 Msun T = 9 K m_ubv_U = 17.40\n", + "M = 7000.000 Msun T = 7 K m_ubv_U = 17.18\n", + "M = 17250.000 Msun T = 10 K m_ubv_U = 13.78\n", + "M = 13250.000 Msun T = 8 K m_ubv_U = 14.56\n", + "M = 7750.000 Msun T = 8 K m_ubv_U = 16.67\n", + "M = 8500.000 Msun T = 7 K m_ubv_U = 16.26\n", + "M = 2601.716 Msun T = 7 K m_ubv_U = 27.24\n", + "M = 2601.716 Msun T = 8 K m_ubv_U = 27.24\n", + "M = 14736.126 Msun T = 10 K m_ubv_U = 13.75\n", + "M = 14736.126 Msun T = 11 K m_ubv_U = 13.75\n", + "Starting filter: ubv,V Elapsed time: 53.84 seconds\n", + "Starting synthetic photometry\n", + "M = 1000.000 Msun T = 2 K m_ubv_V = 40.30\n", + "M = 1000.000 Msun T = 4 K m_ubv_V = 40.30\n", + "M = 1000.000 Msun T = 4 K m_ubv_V = 40.30\n", + "M = 1000.000 Msun T = 6 K m_ubv_V = 40.30\n", + "M = 1400.000 Msun T = 4 K m_ubv_V = 32.80\n", + "M = 2600.000 Msun T = 3 K m_ubv_V = 26.27\n", + "M = 2600.000 Msun T = 4 K m_ubv_V = 22.75\n", + "M = 3400.000 Msun T = 2 K m_ubv_V = 22.10\n", + "M = 4100.000 Msun T = 6 K m_ubv_V = 19.88\n", + "M = 4900.000 Msun T = 4 K m_ubv_V = 18.94\n", + "M = 3000.000 Msun T = 2 K m_ubv_V = 22.76\n", + "M = 3800.000 Msun T = 0 K m_ubv_V = 21.43\n", + "M = 4500.000 Msun T = 4 K m_ubv_V = 19.46\n", + "M = 2600.000 Msun T = 2 K m_ubv_V = 24.08\n", + "M = 3400.000 Msun T = 0 K m_ubv_V = 22.06\n", + "M = 4100.000 Msun T = 5 K m_ubv_V = 20.04\n", + "M = 4900.000 Msun T = 3 K m_ubv_V = 18.84\n", + "M = 3000.000 Msun T = 1 K m_ubv_V = 23.21\n", + "M = 3700.000 Msun T = 6 K m_ubv_V = 20.82\n", + "M = 4500.000 Msun T = 4 K m_ubv_V = 19.36\n", + "M = 2600.000 Msun T = 2 K m_ubv_V = 25.31\n", + "M = 3300.000 Msun T = 6 K m_ubv_V = 21.87\n", + "M = 4100.000 Msun T = 4 K m_ubv_V = 20.07\n", + "M = 4900.000 Msun T = 2 K m_ubv_V = 18.85\n", + "M = 3000.000 Msun T = 0 K m_ubv_V = 25.66\n", + "M = 3700.000 Msun T = 4 K m_ubv_V = 20.88\n", + "M = 4500.000 Msun T = 2 K m_ubv_V = 19.38\n", + "M = 2900.000 Msun T = 2 K m_ubv_V = 25.37\n", + "M = 4100.000 Msun T = 6 K m_ubv_V = 20.11\n", + "M = 4900.000 Msun T = 4 K m_ubv_V = 18.79\n", + "M = 3000.000 Msun T = 2 K m_ubv_V = 25.65\n", + "M = 3700.000 Msun T = 6 K m_ubv_V = 21.12\n", + "M = 4500.000 Msun T = 4 K m_ubv_V = 19.40\n", + "M = 2600.000 Msun T = 2 K m_ubv_V = 26.99\n", + "M = 3400.000 Msun T = 0 K m_ubv_V = 24.07\n", + "M = 4100.000 Msun T = 4 K m_ubv_V = 20.39\n", + "M = 4900.000 Msun T = 2 K m_ubv_V = 18.84\n", + "M = 5000.000 Msun T = 2 K m_ubv_V = 18.80\n", + "M = 10500.000 Msun T = 3 K m_ubv_V = 15.55\n", + "M = 18000.000 Msun T = 5 K m_ubv_V = 14.54\n", + "M = 47000.000 Msun T = 4 K m_ubv_V = 12.82\n", + "M = 7750.000 Msun T = 1 K m_ubv_V = 16.40\n", + "M = 11000.000 Msun T = 2 K m_ubv_V = 15.49\n", + "M = 21000.000 Msun T = 3 K m_ubv_V = 14.24\n", + "M = 5500.000 Msun T = 2 K m_ubv_V = 18.12\n", + "M = 8000.000 Msun T = 2 K m_ubv_V = 16.31\n", + "M = 11250.000 Msun T = 4 K m_ubv_V = 15.43\n", + "M = 23000.000 Msun T = 4 K m_ubv_V = 14.13\n", + "M = 5750.000 Msun T = 3 K m_ubv_V = 17.92\n", + "M = 8250.000 Msun T = 4 K m_ubv_V = 16.25\n", + "M = 11750.000 Msun T = 4 K m_ubv_V = 15.37\n", + "M = 25000.000 Msun T = 5 K m_ubv_V = 14.00\n", + "M = 6000.000 Msun T = 4 K m_ubv_V = 17.74\n", + "M = 8750.000 Msun T = 2 K m_ubv_V = 16.04\n", + "M = 12250.000 Msun T = 4 K m_ubv_V = 15.29\n", + "M = 28000.000 Msun T = 4 K m_ubv_V = 13.78\n", + "M = 6250.000 Msun T = 4 K m_ubv_V = 17.59\n", + "M = 9000.000 Msun T = 4 K m_ubv_V = 15.97\n", + "M = 12750.000 Msun T = 4 K m_ubv_V = 15.11\n", + "M = 31000.000 Msun T = 4 K m_ubv_V = 13.45\n", + "M = 6750.000 Msun T = 0 K m_ubv_V = 16.97\n", + "M = 9500.000 Msun T = 3 K m_ubv_V = 15.76\n", + "M = 14000.000 Msun T = 3 K m_ubv_V = 14.91\n", + "M = 35000.000 Msun T = 4 K m_ubv_V = 13.18\n", + "M = 7000.000 Msun T = 2 K m_ubv_V = 16.81\n", + "M = 10000.000 Msun T = 2 K m_ubv_V = 15.61\n", + "M = 16000.000 Msun T = 3 K m_ubv_V = 14.62\n", + "M = 39000.000 Msun T = 4 K m_ubv_V = 13.00\n", + "M = 7250.000 Msun T = 2 K m_ubv_V = 16.65\n", + "M = 50000.000 Msun T = 8 K m_ubv_V = 12.73\n", + "M = 14000.000 Msun T = 8 K m_ubv_V = 14.94\n", + "M = 13750.000 Msun T = 8 K m_ubv_V = 14.97\n", + "M = 10750.000 Msun T = 7 K m_ubv_V = 15.46\n", + "M = 16500.000 Msun T = 8 K m_ubv_V = 14.65\n", + "M = 6750.000 Msun T = 9 K m_ubv_V = 17.09\n", + "M = 7000.000 Msun T = 7 K m_ubv_V = 16.93\n", + "M = 17250.000 Msun T = 10 K m_ubv_V = 14.58\n", + "M = 13250.000 Msun T = 8 K m_ubv_V = 15.04\n", + "M = 7750.000 Msun T = 8 K m_ubv_V = 16.49\n", + "M = 8500.000 Msun T = 7 K m_ubv_V = 16.11\n", + "M = 2601.716 Msun T = 7 K m_ubv_V = 23.84\n", + "M = 2601.716 Msun T = 8 K m_ubv_V = 23.84\n", + "M = 14736.126 Msun T = 10 K m_ubv_V = 14.69\n", + "M = 14736.126 Msun T = 11 K m_ubv_V = 14.69\n", + "Starting filter: ubv,R Elapsed time: 105.93 seconds\n", + "Starting synthetic photometry\n", + "M = 1000.000 Msun T = 2 K m_ubv_R = 36.49\n", + "M = 1000.000 Msun T = 4 K m_ubv_R = 36.49\n", + "M = 1000.000 Msun T = 4 K m_ubv_R = 36.49\n", + "M = 1000.000 Msun T = 6 K m_ubv_R = 36.49\n", + "M = 1400.000 Msun T = 4 K m_ubv_R = 28.95\n", + "M = 2600.000 Msun T = 3 K m_ubv_R = 24.72\n", + "M = 2600.000 Msun T = 4 K m_ubv_R = 21.53\n", + "M = 3400.000 Msun T = 2 K m_ubv_R = 21.02\n", + "M = 4100.000 Msun T = 6 K m_ubv_R = 19.22\n", + "M = 4900.000 Msun T = 4 K m_ubv_R = 18.47\n", + "M = 3000.000 Msun T = 2 K m_ubv_R = 21.60\n", + "M = 3800.000 Msun T = 0 K m_ubv_R = 20.51\n", + "M = 4500.000 Msun T = 4 K m_ubv_R = 18.90\n", + "M = 2600.000 Msun T = 2 K m_ubv_R = 22.86\n", + "M = 3400.000 Msun T = 0 K m_ubv_R = 21.09\n", + "M = 4100.000 Msun T = 5 K m_ubv_R = 19.35\n", + "M = 4900.000 Msun T = 3 K m_ubv_R = 18.39\n", + "M = 3000.000 Msun T = 1 K m_ubv_R = 22.15\n", + "M = 3700.000 Msun T = 6 K m_ubv_R = 19.98\n", + "M = 4500.000 Msun T = 4 K m_ubv_R = 18.83\n", + "M = 2600.000 Msun T = 2 K m_ubv_R = 23.90\n", + "M = 3300.000 Msun T = 6 K m_ubv_R = 20.87\n", + "M = 4100.000 Msun T = 4 K m_ubv_R = 19.38\n", + "M = 4900.000 Msun T = 2 K m_ubv_R = 18.40\n", + "M = 3000.000 Msun T = 0 K m_ubv_R = 24.15\n", + "M = 3700.000 Msun T = 4 K m_ubv_R = 20.06\n", + "M = 4500.000 Msun T = 2 K m_ubv_R = 18.84\n", + "M = 2900.000 Msun T = 2 K m_ubv_R = 23.91\n", + "M = 4100.000 Msun T = 6 K m_ubv_R = 19.37\n", + "M = 4900.000 Msun T = 4 K m_ubv_R = 18.30\n", + "M = 3000.000 Msun T = 2 K m_ubv_R = 24.20\n", + "M = 3700.000 Msun T = 6 K m_ubv_R = 20.30\n", + "M = 4500.000 Msun T = 4 K m_ubv_R = 18.77\n", + "M = 2600.000 Msun T = 2 K m_ubv_R = 25.38\n", + "M = 3400.000 Msun T = 0 K m_ubv_R = 22.82\n", + "M = 4100.000 Msun T = 4 K m_ubv_R = 19.64\n", + "M = 4900.000 Msun T = 2 K m_ubv_R = 18.26\n", + "M = 5000.000 Msun T = 2 K m_ubv_R = 18.38\n", + "M = 10500.000 Msun T = 3 K m_ubv_R = 15.56\n", + "M = 18000.000 Msun T = 5 K m_ubv_R = 14.61\n", + "M = 47000.000 Msun T = 4 K m_ubv_R = 12.95\n", + "M = 7750.000 Msun T = 1 K m_ubv_R = 16.35\n", + "M = 11000.000 Msun T = 2 K m_ubv_R = 15.49\n", + "M = 21000.000 Msun T = 3 K m_ubv_R = 14.32\n", + "M = 5500.000 Msun T = 2 K m_ubv_R = 17.76\n", + "M = 8000.000 Msun T = 2 K m_ubv_R = 16.26\n", + "M = 11250.000 Msun T = 4 K m_ubv_R = 15.46\n", + "M = 23000.000 Msun T = 4 K m_ubv_R = 14.22\n", + "M = 5750.000 Msun T = 3 K m_ubv_R = 17.61\n", + "M = 8250.000 Msun T = 4 K m_ubv_R = 16.19\n", + "M = 11750.000 Msun T = 4 K m_ubv_R = 15.39\n", + "M = 25000.000 Msun T = 5 K m_ubv_R = 14.10\n", + "M = 6000.000 Msun T = 4 K m_ubv_R = 17.46\n", + "M = 8750.000 Msun T = 2 K m_ubv_R = 16.02\n", + "M = 12250.000 Msun T = 4 K m_ubv_R = 15.32\n", + "M = 28000.000 Msun T = 4 K m_ubv_R = 13.88\n", + "M = 6250.000 Msun T = 4 K m_ubv_R = 17.32\n", + "M = 9000.000 Msun T = 4 K m_ubv_R = 15.95\n", + "M = 12750.000 Msun T = 4 K m_ubv_R = 15.14\n", + "M = 31000.000 Msun T = 4 K m_ubv_R = 13.57\n", + "M = 6750.000 Msun T = 0 K m_ubv_R = 16.81\n", + "M = 9500.000 Msun T = 3 K m_ubv_R = 15.76\n", + "M = 14000.000 Msun T = 3 K m_ubv_R = 14.95\n", + "M = 35000.000 Msun T = 4 K m_ubv_R = 13.30\n", + "M = 7000.000 Msun T = 2 K m_ubv_R = 16.66\n", + "M = 10000.000 Msun T = 2 K m_ubv_R = 15.60\n", + "M = 16000.000 Msun T = 3 K m_ubv_R = 14.68\n", + "M = 39000.000 Msun T = 4 K m_ubv_R = 13.13\n", + "M = 7250.000 Msun T = 2 K m_ubv_R = 16.51\n", + "M = 50000.000 Msun T = 8 K m_ubv_R = 12.86\n", + "M = 14000.000 Msun T = 8 K m_ubv_R = 14.99\n", + "M = 13750.000 Msun T = 8 K m_ubv_R = 15.02\n", + "M = 10750.000 Msun T = 7 K m_ubv_R = 15.48\n", + "M = 16500.000 Msun T = 8 K m_ubv_R = 14.71\n", + "M = 6750.000 Msun T = 9 K m_ubv_R = 16.86\n", + "M = 7000.000 Msun T = 7 K m_ubv_R = 16.72\n", + "M = 17250.000 Msun T = 10 K m_ubv_R = 14.64\n", + "M = 13250.000 Msun T = 8 K m_ubv_R = 15.09\n", + "M = 7750.000 Msun T = 8 K m_ubv_R = 16.35\n", + "M = 8500.000 Msun T = 7 K m_ubv_R = 16.03\n", + "M = 2601.716 Msun T = 7 K m_ubv_R = 22.65\n", + "M = 2601.716 Msun T = 8 K m_ubv_R = 22.65\n", + "M = 14736.126 Msun T = 10 K m_ubv_R = 14.67\n", + "M = 14736.126 Msun T = 11 K m_ubv_R = 14.67\n", + " Time taken: 154.51 seconds\n" + ] + } + ], "source": [ "# Fetch isochrone\n", "logAge = 10 # Age in log(years)\n", @@ -98,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "4c12140e-88d8-48ba-98e2-0ba55e87d9e6", "metadata": {}, "outputs": [], @@ -113,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "17d315a9-ec84-4289-a810-855443628e67", "metadata": { "scrolled": true @@ -124,65 +1171,68 @@ "output_type": "stream", "text": [ "/opt/mambaforge3/envs/astro_cosmic/lib/python3.11/site-packages/cosmic/utils.py:1324: UserWarning: At least one of your initial binaries is starting in Roche Lobe Overflow:\n", - " kstar_1 kstar_2 mass_1 mass_2 porb ecc \\\n", - "913 1.0 1.0 36.226198 21.011972 0.059753 0.276916 \n", - "2592 1.0 1.0 79.074503 4.929898 0.064817 0.957888 \n", - "5131 1.0 1.0 119.959908 28.001784 0.090811 0.649207 \n", - "5752 1.0 1.0 12.882534 5.568839 0.113793 0.443804 \n", - "7023 1.0 1.0 7.866092 2.351273 0.179980 0.925944 \n", - "7431 1.0 1.0 96.571261 88.946686 0.114503 0.870712 \n", - "8343 1.0 1.0 16.781999 15.103265 0.072249 0.456203 \n", - "8745 1.0 1.0 18.382829 9.790526 0.778778 0.724077 \n", - "10560 1.0 1.0 2.365459 0.847537 0.309513 0.608170 \n", - "12893 1.0 1.0 73.141720 9.824216 0.284091 0.976405 \n", - "13291 1.0 1.0 13.282887 0.767718 0.339531 0.842305 \n", - "15588 1.0 1.0 57.713263 24.994475 0.474699 0.783841 \n", - "16606 1.0 1.0 4.359086 1.573977 0.398929 0.557661 \n", - "17213 1.0 1.0 16.901245 15.158980 0.823797 0.152604 \n", - "18387 1.0 1.0 6.214054 4.708508 0.510991 0.503864 \n", - "21167 1.0 1.0 82.717318 38.064096 0.478343 0.185265 \n", + " kstar_1 kstar_2 mass_1 mass_2 porb ecc \\\n", + "982 1.0 1.0 34.983501 10.502985 0.737500 0.780722 \n", + "3145 1.0 1.0 26.931079 22.490881 0.386408 0.869985 \n", + "3221 1.0 1.0 112.879903 33.537283 0.674712 0.846808 \n", + "6147 1.0 0.0 2.719136 0.430439 0.258503 0.743511 \n", + "8163 1.0 1.0 51.934940 25.942593 0.104794 0.906464 \n", + "8976 1.0 1.0 1.353911 1.047359 0.270292 0.880324 \n", + "11341 1.0 0.0 2.210809 0.039488 0.243581 0.696509 \n", + "11997 1.0 1.0 56.384960 8.901185 0.376155 0.812874 \n", + "12177 1.0 1.0 867.905900 91.463500 0.054490 0.871674 \n", + "12473 1.0 1.0 89.947583 65.866705 0.194641 0.337963 \n", + "12907 1.0 1.0 5.848843 4.541931 0.195558 0.566851 \n", + "13526 1.0 1.0 282.145127 150.334490 0.037996 0.970647 \n", + "14116 1.0 1.0 23.279792 13.816783 0.373627 0.175221 \n", + "14810 1.0 1.0 4.124396 2.772040 0.423764 0.160526 \n", + "15923 1.0 1.0 94.804547 13.757087 0.039236 0.962300 \n", + "16181 1.0 1.0 33.320239 29.349840 0.081215 0.818545 \n", + "16402 1.0 1.0 1256.702679 283.551447 0.026743 0.330402 \n", "\n", - " metallicity tphysf mass0_1 mass0_2 ... tacc_1 tacc_2 \\\n", - "913 0.02 10000.0 36.226198 21.011972 ... 0.0 0.0 \n", - "2592 0.02 10000.0 79.074503 4.929898 ... 0.0 0.0 \n", - "5131 0.02 10000.0 119.959908 28.001784 ... 0.0 0.0 \n", - "5752 0.02 10000.0 12.882534 5.568839 ... 0.0 0.0 \n", - "7023 0.02 10000.0 7.866092 2.351273 ... 0.0 0.0 \n", - "7431 0.02 10000.0 96.571261 88.946686 ... 0.0 0.0 \n", - "8343 0.02 10000.0 16.781999 15.103265 ... 0.0 0.0 \n", - "8745 0.02 10000.0 18.382829 9.790526 ... 0.0 0.0 \n", - "10560 0.02 10000.0 2.365459 0.847537 ... 0.0 0.0 \n", - "12893 0.02 10000.0 73.141720 9.824216 ... 0.0 0.0 \n", - "13291 0.02 10000.0 13.282887 0.767718 ... 0.0 0.0 \n", - "15588 0.02 10000.0 57.713263 24.994475 ... 0.0 0.0 \n", - "16606 0.02 10000.0 4.359086 1.573977 ... 0.0 0.0 \n", - "17213 0.02 10000.0 16.901245 15.158980 ... 0.0 0.0 \n", - "18387 0.02 10000.0 6.214054 4.708508 ... 0.0 0.0 \n", - "21167 0.02 10000.0 82.717318 38.064096 ... 0.0 0.0 \n", + " metallicity tphysf mass0_1 mass0_2 ... tacc_1 tacc_2 \\\n", + "982 0.02 10000.0 34.983501 10.502985 ... 0.0 0.0 \n", + "3145 0.02 10000.0 26.931079 22.490881 ... 0.0 0.0 \n", + "3221 0.02 10000.0 112.879903 33.537283 ... 0.0 0.0 \n", + "6147 0.02 10000.0 2.719136 0.430439 ... 0.0 0.0 \n", + "8163 0.02 10000.0 51.934940 25.942593 ... 0.0 0.0 \n", + "8976 0.02 10000.0 1.353911 1.047359 ... 0.0 0.0 \n", + "11341 0.02 10000.0 2.210809 0.039488 ... 0.0 0.0 \n", + "11997 0.02 10000.0 56.384960 8.901185 ... 0.0 0.0 \n", + "12177 0.02 10000.0 867.905900 91.463500 ... 0.0 0.0 \n", + "12473 0.02 10000.0 89.947583 65.866705 ... 0.0 0.0 \n", + "12907 0.02 10000.0 5.848843 4.541931 ... 0.0 0.0 \n", + "13526 0.02 10000.0 282.145127 150.334490 ... 0.0 0.0 \n", + "14116 0.02 10000.0 23.279792 13.816783 ... 0.0 0.0 \n", + "14810 0.02 10000.0 4.124396 2.772040 ... 0.0 0.0 \n", + "15923 0.02 10000.0 94.804547 13.757087 ... 0.0 0.0 \n", + "16181 0.02 10000.0 33.320239 29.349840 ... 0.0 0.0 \n", + "16402 0.02 10000.0 1256.702679 283.551447 ... 0.0 0.0 \n", "\n", " epoch_1 epoch_2 tms_1 tms_2 bhspin_1 bhspin_2 tphys binfrac \n", - "913 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "2592 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "5131 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "5752 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "7023 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "7431 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "8343 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "8745 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "10560 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "12893 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "13291 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "15588 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "16606 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "17213 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "18387 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "21167 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "982 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "3145 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "3221 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "6147 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "8163 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "8976 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "11341 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "11997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "12177 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "12473 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "12907 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "13526 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "14116 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "14810 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "15923 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "16181 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "16402 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "\n", - "[16 rows x 38 columns]\n", + "[17 rows x 38 columns]\n", " warnings.warn(\n", - "/u/nsabrams/code/multiplicity/PyPopStar/spisea/evolution.py:1442: RuntimeWarning: divide by zero encountered in log10\n", + "/u/nsabrams/code/multiplicity/PyPopStar/spisea/evolution.py:1849: RuntimeWarning: divide by zero encountered in log10\n", " return np.log10(((np.array(c.G.to('Rsun^3/(Msun*s^2)').value*masses/((radii)**2))*u.Rsun/u.s**2).to('cm/s^2')).value)\n", - "/u/nsabrams/code/multiplicity/PyPopStar/spisea/evolution.py:1442: RuntimeWarning: divide by zero encountered in log10\n", + "/u/nsabrams/code/multiplicity/PyPopStar/spisea/evolution.py:1849: RuntimeWarning: divide by zero encountered in log10\n", " return np.log10(((np.array(c.G.to('Rsun^3/(Msun*s^2)').value*masses/((radii)**2))*u.Rsun/u.s**2).to('cm/s^2')).value)\n", "/opt/mambaforge3/envs/astro_cosmic/lib/python3.11/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log10\n", " result = getattr(ufunc, method)(*inputs, **kwargs)\n", @@ -206,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "467f1672-1f16-4c43-8add-47d0953fcd05", "metadata": {}, "outputs": [], @@ -233,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "45ac7b14-e09b-4840-b11d-647e2c5609e4", "metadata": {}, "outputs": [ @@ -243,13 +1293,13 @@ "Text(0, 0.5, 'm_ubv_R')" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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BvwCqDDKubQ2bTUdzMC8p3W4DDfDX2KKK9XMqhxugfgOoiYjIPjDMVGKPYeZ+Kq0eY//vCJQa6z0u6RYiR6pSC+CvL3sANU7hru3LXqXVY83hbKw6dLnKgGR7/AtU8dRMvO/P1tzVnIiILIdhphJHCDMV9maocSDzJlKVt5F+TWfVe8XVME0bqHu2FFAeak5c0UAUgR6t/QzhJ02pwT+3n0baVevWbi6uRExEZP8YZipxpDAD/LW2jJebMw5fzMOHu87bvAZBAFLm1Txbqj4qHqN5ujnhblEZPN2cMHJ5ioWrNM+THRV4bUA4Anzc+fiJiMgOMcxU4khhpqZ9kwAgLim91kG81rJ8XHeM6BJc47YIpth0NAdzk9ItWKHlRQY2weLRXQyLFtbGUp8JERHVjmGmEkcJMw/aNwkoH5h76uptLNmZafIqw8aYOagtvNxdsHjHuXpvSlkXlVaPBdvPIPlCLvTF9vvXrpmXKzzdnKHU3AMAeLoCZ98f8eeYoSysOpTF8TdERFbGMFOJo4SZ2hauqzx9GvjrEc7hizexfP8lW5ZYZayJOb0TtS3s17a5Fy7eLLBgxbbRq7UfxvZsiTE9q4aayo8MuTUDEZFxuNGkA6pYuO7+npmKdV4qVGxuGR3eDD4eroaeE1uo2JTyu7TrWLTjHEQTe2xq2vASAN5/tjNOXb2NxB3nLFi19R3N1uBotgazv01HC7kM17SFNZ5Xn8HVRERkPK4AbCeC5B5IHNUZzkL5ROKKfZMe9P/kp/QLxy/zBuHrVx/FK9Gt4Otu3WzqJACf7DuPxJ/KgwxQHr7iktKRpqz/Wjk1rThcEdym9A/HkbhBeKKDwoKV205tQQYonxo+LymdKxYTEVkYHzPZmcp7Opny+CY6cZ+VKnswAcC84ZGY0i+8Xuffv+JwwqhO1XosVFo9tpy4iku5d9A+yAcXc+/g9HUtzt/IR3GpFX4JG/F1d0ZY8yaYMagtBrcPBMBBxURE93OYMTMrVqzAihUrkJ2dDQDo2LEj/vnPf2LYsGEAAFEUsWDBAqxcuRIajQZRUVFYvnw5OnbsWO97OFqYMdeUL45i19lcye4/fUA4+kT41+tL2ZzgtjdDjS9/vYLColJc0+iRc/ueOWVLplOwDzq3lOPr35UAuDUDEVEFhwkz33//PZydndG2bVsAwLp16/Dhhx/i5MmT6NixIxYvXowPPvgAa9euRbt27bBw4UIcPHgQmZmZ8Pb2rtc9GluYeXPTSWw9eV3qMmrcUsCaX8pdF+yCVl9itfalFuTjhllDH6o2yJiIqKFymDBTk6ZNm+LDDz/EpEmTEBwcjNjYWMydOxcAUFhYCIVCgcWLF2PKlCn1aq+xhZm9GWpMXndc6jIAlPcyCH8OarbFNOZVBy/hv4cu41ZBEYptvTCPDb0S3QpDOgVCX1SCy3kFeKR1UwT4uONY9i0IgoAQPw8UFJVCX1SCVOVtBHi74/EOCvbwEJFDccgwU1pais2bNyMmJgYnT56Eu7s7wsPDceLECXTv3t1w3siRI+Hr64t169bVq93GFmYAYNSnv+BEzm2py6hGim0E0pQavLT6d2jvNdxem/oa06MF/ufhlnx0RUQOwaGmZqenpyM6Ohr37t1DkyZNsHXrVnTo0AEpKeXL3ysUVWe1KBQKXLlypdb2CgsLUVj414wSnc6+9wmyhi3T+mBvhhoJP2ZAdVsPCMBdO1ikrmJq94M2srT0I6muIX5Ii3/izwX7TuPwxTwIAuDj7grdvRLcLSpFqfQfjU1sPn4Nm49fgwBg+sBwvPVEpNQlERFZhORh5qGHHkJqaipu376NpKQkxMTEIDk52fC+IFSdwyuKYrVjlSUmJmLBggVWq9dRDG4faJgpA5T3UOzNyIXM1QlyD1ecVemw4TelWfcY/XALvBTdCnszchHgI0NBUSmW7ChfodgJ5VORK+eEmtbNqVDTVg6WfCQVJPfA/73Uq8b3VFo94r49hbRrWvRp0wzjo1vjblEx3t6ajhu6IovVYC9EAMv2X8Lnhy5jYAcFvNxc0KmFD4Z0CGSPDRE5JLt5zFTh8ccfR3h4OObOnWvSY6aaemZCQkIa1WOm+lJp9dibcQO/XvoDF3LzkX+vBLcKinCvpPa/EgKAEV0C8epjbWrcw6jyDKWD52/WOf264pratnKo+HKVauryk/9Jxrkbd2x2P6l5uALBck8095YhPKAJmnq5oVuIL/TFZRBFET1bN2XgISKbcKjHTPcTRRGFhYUICwtDYGAg9uzZYwgzRUVFSE5OxuLFi2u9XiaTQSaT2apchxYk98CER1tjwqOtqxxPU2qw4bcc5NwqQLMmMjzaphk6t5DjblFZndOoK1YoBoDne4WiX7vmdU6/rmlF4MqPpKzda/MgO9/sj70ZahzIvAlfD1fc1hejS0s5PFxd8M0xJQ5eyLNJHbaiLwYu5d3Fpby7+DWr5oUQOwR6w8/LFQ+H+gECUFRSBjcXJ4Q29cRtfTHa+HvBw82FY3OIyGYkDTNvv/02hg0bhpCQEOTn52Pjxo04cOAAdu7cCUEQEBsbi4SEBERERCAiIgIJCQnw9PTEuHHjpCy7wesa4lfnztH1VTnc1OZBWzmotHpDkAHKz3l7y2n0a9fcZl+U9z+yq/BU12B8dvASFv10DnbVvWllZ9X5AIBfLt164HkCyh9FylydMCgyAB2C5Uj6cxHE3uHN0MLPs1rg4eKBRGQKScPMjRs3MHHiRKhUKsjlcnTp0gU7d+7EkCFDAABz5syBXq/HtGnTDIvm7d69u95rzJBjqNjK4f5HUkFyD6Rcyntgr43UpvQLxzNdg3HiigbZfxSgqLgMXULk0BeV4cqtAqRczKvzS7+hEgF8e+IaAOCr+8ZnVayF5CQAk/uG4akuQdh0TGkYxyUAmDcsEp1byhHm7wUAhpBT25/t4e8DEUnD7sbMWFpjnJrtqGpaEbg+42nsnUqrx/FsDQQBaOnngasaPY5czkOm+g48XZxx6GIeylD+Ba7wkUGtq31/p8ZKQHk4qvjPimOoOC6Uh58p/cIf2LtT007mQNVAxN4hIvvgkOvMWAvDjOOrzz5Ojuz+EJem1GDfuVycU+Vj99kbhi9xAI3qcZYpHgnzw9FsTZUd3StWoU6/pq22y3zlz9VJAP6newtsPXlNkvFZRFQVw0wlDDMNgzn7ODmyyr83AGTn3YWnmxNGLk+RuLLGwQnAx+O6o0crv3r9vavcqwPU/TgsTanB79m38EjrphYbp0bUUDDMVMIwQ41Bu7d/RFED3sJBavVZaLDyrLsqj8Aq/blyb88/vklF0p9jioDywdL/HtvN8JqPu6ixY5iphGGGGqMZ64/jh9NqqctocOTuLmgf5I1wRRO4OTmhuLQMF2/ewa2CIpy/UVDvdl4fGI5P9l+qdnz79N7oGuJXbTmCyX3DMKlvGEMNNSoMM5UwzBBVFRH3IyrvbiFzAkrKgFLpSqJ6ihseiWe6BnM2FzUKDDOVMMwQGWfzsRzsPnMDQzsqMKZnKNrM+xF8gmU/Ks/sAv56fDV3WCQ6t5BDX1SCtKta+Ddx4xYV5NAYZiphmCGyLZVWj+dXpEB5+x6aerrAxdkJN/Kr73HV0tcdLgJw+14JguTu6NLCF1p9EY7naFBUUgYPN2fcKy6Dr7srIIiAIKClnwda+Hpi07GrEvxmjmn6gHC0D/bhdhTkcBhmKmGYIZKeSqvHiSvlU6Z7tK7fzKC6zFh/HLvOqlFWVr7OTKnIqet1EQAsGt0ZkYHenEVFdo9hphKGGaLGK02pwT82peJS3l0AgK+HC5ydBDSRuSAyyBtafTFu3SlCYWkZ3JydUFhcCt29Eri5OMHd1QntA31wTp2P7Ft6iX8T67l/FhWRvWCYqYRhhojMlabUYMOvObiQmw+ZixPCFU3g6uSM4rJSXLp5B/l3S9DEwwVtmzeBSnsPl27eQUlpGWQuzigVy+AkCMi5pUeJnQ4+6tOmKb76e7TUZRBVwTBTCcMMEdmLzcdysOaXLBSXiIgKb4ombi44p87HgfPS774uAMhaNELqMogMGGYqYZghIkey+VgOFv5wFtp7tp8s/1SnQCyb0MPm9yWqiTHf35Lumk1ERFWN6RmKMT2r7wf15H+Sce7GHQBVN9y0pIMXb1qhVSLrY5ghInIAO9/sX+V1mlKDv609ipsFxRa7h06C3iAiS+BjJiKiBmL8yiP45fIts9vJ5tgZsgPGfH872agmIiKysq/+Ho3sRSMw5uEWUpdCZFN8zERE1IBYqneGyJEwzBARNQCbj+Vg9rfpZrfjI2OHPTkehhkiIgek0uqRlVeAbcev4psT1yzW7qkFwyzWFpGtMMwQETmYTUdzMDfJ9F6Y7i3l+HRiDwTJPdBl/g7oCsvgI3NikCGHxTBDROQgVFo9thy/ig93nzfpepmLgAOzB1bZ6JMBhhoChhkiIju3N0ONhT9mIOvPDTON5eYsYPYTD+HVfuEWrozIPjDMEBHZsd6L9uL67XsmXSt3d8YXk6PQNcTPwlUR2ReGGSIiiVUM5g3z9wIAvPn1CRzLuW3yLttdW/jgX892YoihRoNhhohIQpuO5iBuSzrKLLAW++sDw/GPJyLNb4jIwTDMEBFJRKXVmzUrqcI7wyM5HoYaNYYZIiIJbD6Wg3lmBpnVMT0wuH2ghSoiclwMM0RENqLS6rFg+2nsPJtrchtuTsCz3VpgydhuliuMyMExzBARWVmaUoNZ36Ti0k3TplYLAMY/GoLpAyOqrBFDROUYZoiIrCRNqcHMr0/iyi29yW1wUC9R3RhmiIgsbG+GGq9/fRJ3i0ycWw1g7MN8lERUX3azPWpiYiIEQUBsbKzhmCiKiI+PR3BwMDw8PDBgwACcOXNGuiKJiGqg0uqRcikPqw5eQsd/7sTkdcdNCjKuAvDhc52RvWgEgwyREeyiZ+bo0aNYuXIlunTpUuX4kiVLsHTpUqxduxbt2rXDwoULMWTIEGRmZsLb21uiaomIyqUpNfh47wXsPXfTrHY6BXvjzSHtODOJyESSh5k7d+5g/PjxWLVqFRYuXGg4LooiPvroI7zzzjsYNWoUAGDdunVQKBTYsGEDpkyZIlXJRNTILdh+GmuOXDG7Ha4PQ2QZkj9mmj59OkaMGIHHH3+8yvGsrCyo1WoMHTrUcEwmk6F///5ISUmptb3CwkLodLoqP0REltLunZ/MDjKvRLdC9qIRDDJEFiJpz8zGjRtx4sQJHD16tNp7arUaAKBQKKocVygUuHKl9n9IEhMTsWDBAssWSkSE8h6ZolLT9h3wdHXCgpEdMaZnKICq+zFxujWReSQLM0qlEm+88QZ2794Nd3f3Ws8TBKHKa1EUqx2rLC4uDrNmzTK81ul0CAkJMb9gImr0dp29YfK1d4vLkPBjBgCgTIRhPyYnAUgc1RnP92LIITKVZGHm+PHjyM3NRY8ePQzHSktLcfDgQSxbtgyZmZkAyntogoKCDOfk5uZW662pTCaTQSaTWa9wImq0nuigMOsRk0ZfgtnfVt3CoEwE3t5yGv3aNcfB8zdrDTlEVDvJxswMHjwY6enpSE1NNfz07NkT48ePR2pqKtq0aYPAwEDs2bPHcE1RURGSk5PRu3dvqcomokZs/shOcHOuvWfYVKWiiBc/O4K5SX/tnl0RclTavxbcU2n1+OHUdXyfdq3KcaLGTrKeGW9vb3Tq1KnKMS8vLzRr1sxwPDY2FgkJCYiIiEBERAQSEhLg6emJcePGSVEyERHOfzDcYrOZKsuuYZXgUlFEdl75FghrDmdh5aGsKu8/1TkQr/Zrg64hfhathcjRSD41+0HmzJkDvV6PadOmQaPRICoqCrt37+YaM0QkqfkjO2H+yE7YfCwH/9l9HjfzC1Fs2rjgOr246tda3/shXY0f0tUY3ikQn07oUet5RA2dIIqilf4naB90Oh3kcjm0Wi18fHykLoeIGrDNx3Kw6KcM/HG3xOb3nj4wHLO5hxM1IMZ8fzPMEBFZgUqrx4LvTuPAuZu4Z+J0bmMdiRvEGVDUYDDMVMIwQ0T2YG+GGkt2nkN2XgEKS61zDxcBaOHrjgnRrbkgHzk8hplKGGaIyB7tzVDjP3vOI+N6PqyUbeDmDAT5uOOfz3Sstu8T17Mhe8cwUwnDDBHZuzSlBp/su4Cz13XQ3C2Gvtj4HbfrIqC856aJuwu6h/ph//mbEO9bz4YBh+wJw0wlDDNE5GjSlBrM+/YUMm7cscn9nAUBc558CIt3nuOCfWQ3GGYqYZghIkeWptRg4Q9ncfLKbdhyjpSzIODwvIE19tCwB4dsgWGmEoYZImpI5nyTiu9OXQcgorgUsOZEKT8PF0wb2LbKYOLPDl7Coh3nqj2iIrI0hplKGGaIqCHbm6HG8v0XkfPHXfxRUAxr/YPu4+6MNv5NkHpVW+W4syBgy7RoFBSVsqeGLIphphKGGSJqTDYfy8FXv+ZUCx3WJAAQUd5TM/fJSHRuKWewIbMxzFTCMENEjdXmYzn4LPkS/igogsbGqxLzERSZi2GmEoYZIqJym4/l4F/fn0F+oeWnftdEAJDygFWJOZCYHoRhphKGGSKimm0+loPl+y4g+9Y9q97H38sFnVr4YmJ0K8PifZuO5iBuSzqnglOtGGYqYZghIqpbxRTwzBv5KC4VrbJwX4UmMmfcuW9PBycB2DqtNwJ83NlbQwAYZqpgmCEiMp5Kq8e0L48j9arWajOkalJ5MDF7axo3hplKGGaIiMxnmAJ+6y7y7hTb5J4PWriPGj6GmUoYZoiILG/ymt+xL/Om1Xtt2vh74rUB4RjTkz00jQ3DTCUMM0RE1pWm1ODDXedw6qoW+sJSFFvpW8XD1Qmers4I8HHHpL6taw04nCXVMEgWZu7du4dly5bhrbfeslSTZmOYISKyrTSlBv89eBmHLubhtt6669v4e7mib0QzzB3WAUFyjwfOkmLIcSxWDTN5eXn47bff4OrqisGDB8PZ2RnFxcX49NNPkZiYiJKSEuTl5Zn1C1gSwwwRkbTSlBr89/BlHM3SQK0rtNp9FE1cceO+8TwV424Onr/JqeAOxmphJiUlBSNGjIBWq4UgCOjZsyfWrFmDZ599FmVlZYiNjcWkSZPg6elp9i9hKQwzRET25d+7ziHp+FWUiiJy84usPu5mwqMh2PCbEmWVbsTBxfbPamFm8ODBaN68Od599118/vnn+Oijj9C6dWvEx8dj4sSJEATB7OItjWGGiMi+VewnpS8uQeaNApvd9+tXH0V0eDOb3Y+MY7Uw4+/vj+TkZHTs2BF3796Ft7c3Nm7ciDFjxphdtLUwzBAROZY536Rix1k18u+V1n2yGZq4OSFC4Y0Zg9oaViYm+2G1MOPk5AS1Wo2AgAAAgLe3N06ePIm2bduaV7EVMcwQETmuii0X1Lp7KC4BrBVvnAB4ypzRs5UvEkd3rfL4iQOHpWHM97eLMQ0LgoD8/Hy4u7tDFEUIgoC7d+9Cp9NVOY+hgYiILGFMz9AqU7ArpoGfuabD3eISFFposlQZgDuFpThw/g9EJ+6DAMDH3Rk9WjXFgfM3DQOH5z4Zic4t5YZgw6BjH4zumak8LqYi0Nz/urTUul2DxmDPDBFRw6XS6pF0/Cp+uXgTx7I1sOKWUlU4CcD/dG+BrSevcYaUlVjtMVNycnK9zuvfv399m7Q6hhkiosZj87EcrEy+BN29EtzIL7LpvTlDyrLsZgXgRYsWYerUqfD19bXWLerEMENE1HiVTwNX4o87RSi0Qa/N+yM7IjygCR87WYDdhBkfHx+kpqaiTZs21rpFnRhmiIgIKH8kteC70zhwPg/3bPA8avrAcMx+ItLq92mo7CbMeHt7Iy0tjWGGiIjsTppSg4U/nsUF9R0UFJVYZbyN3N0FQzsqMOHRVuga4mf5GzRgDDOVMMwQEVF9pCk1WPjDWaRf0+JeieW/Gv29XOHs7IThHQMxf2Qni7ff0Bjz/e1ko5pqFB8fD0EQqvwEBv61cJEoioiPj0dwcDA8PDwwYMAAnDlzRsKKiYiooeoa4ofNr/XBuYXDkb1oBLZP742nuwSieRNXi7SfV1CMG7pCrDlyBa3n/Yih/zmAvRlqi7Td2Bm1zow1dOzYET///LPhtbOzs+HPS5YswdKlS7F27Vq0a9cOCxcuxJAhQ5CZmQlvb28pyiUiokaia4gfPhnXA0D5eJvsvLtIuXgTW05cxTWt+Rtmnr9RgMnrjsNZAFr4umNidGu82i+ca9eYQNLHTPHx8di2bRtSU1OrvSeKIoKDgxEbG4u5c+cCAAoLC6FQKLB48WJMmTKlXjXwMRMREVnDjPXHsTfzBpwAFBRb/qu0Yu2afu2aVwk3jSXsWG0FYGM99thj8PB48Ad94cIFBAcHQyaTISoqCgkJCWjTpg2ysrKgVqsxdOhQw7kymQz9+/dHSkpKrWGmsLAQhYV/Jeb7VycmIiKyhGUTehj+rNLqseXEVexIV+GcKh+WGHJTJgJzk9INr7lQX+1MGjMzcOBArF69Glqt9oHn/fTTTwgKCqr1/aioKHzxxRfYtWsXVq1aBbVajd69e+OPP/6AWl3+HFGhUFS5RqFQGN6rSWJiIuRyueEnJCTEiN+MiIjIeEFyD0wfGIEfZvbDxcQRWB3TAwPa+aOpp2XG2wDl4SbpRHmQqXgdtyUdXxzJgkqrt9h9HJFJj5lmzpyJzZs34/bt2xg+fDgmTpyI4cOHw83NzaxiCgoKEB4ejjlz5uDRRx9Fnz59cP369SqB6NVXX4VSqcTOnTtrbKOmnpmQkBA+ZiIiIkmkKTXYm5GL37Py8GvWbavcQwCwaHTD6qWx+mymjz/+GNeuXcP27dvh7e2NmJgYBAYG4u9//3u9tzyoiZeXFzp37owLFy4YZjXd3wuTm5tbrbemMplMBh8fnyo/REREUuka4odZQx/Cxil9kL1oBF6JbgWh7suMIqL8kdSqg5cs3LJjMHlqtpOTE4YOHYq1a9fixo0b+Oyzz/D7779j0KBBJhdTWFiIjIwMBAUFISwsDIGBgdizZ4/h/aKiIiQnJ6N3794m34OIiEhK80d2QtaiEfjwuc5oH9gEbhZcJOWDn86h9bwf8fi/9yNNqbFcw3bO7AHAarUaGzduxPr163Hq1Cn06tWr3te+9dZbePrppxEaGorc3FwsXLgQOp0OMTExEAQBsbGxSEhIQEREBCIiIpCQkABPT0+MGzfO3LKJiIgkNaZnKMb0LH8slKbU4L+HLiPl0h/4o6DY7LYv3ryLkctT4OoENPV0w9heIfhHA95awaQwo9PpkJSUhA0bNuDAgQNo06YNxo0bh40bN6Jt27b1bufq1at48cUXkZeXh+bNm+PRRx/Fr7/+ilatWgEA5syZA71ej2nTpkGj0SAqKgq7d+/mGjNERNSgVF7TBijf/XvN4WycVeeb1W5xGXDjThE+2X8Jn+y/hAHt/DGiSxBa+Hk2qKndJg0A9vDwgJ+fH8aOHYvx48cb1Rtja1xnhoiIHNmqg5ew+tBl3LxThFILL2cTqWiCnW/2t2yjFmL1vZl2796Nxx9/HE5Oku6GUC8MM0RE1FDszVDjyyNXcDTrFgosuDOmr7sLIhRNMLZXiOHRl9RsttFkbm4uMjMzIQgC2rVrh4CAAFObshqGGSIiaojSlBrMSzqFDPUdi7cd6vfX9gpSsXqY0el0mD59OjZu3IjS0lIA5XsqPf/881i+fDnkcrlplVsBwwwRETV0qw5eQtLxq7h88w6KLNdhAwBo6SvDG4+3s3mPjdXDzNixY5GamopPPvkE0dHREAQBKSkpeOONN9ClSxd88803JhdvaQwzRETUmKQpNfhk7wWkX9fhhs78DTErCABa+3ti3COhNumxsXqY8fLywq5du9C3b98qxw8dOoQnn3wSBQUFxjZpNQwzRETUmG0+loP1v11BmtKyexU+2SEA80d2stqMKKuvANysWbMaHyXJ5XL4+fmZ0iQRERFZwZieodg+/TFkLxqB1weGo5mnZfaY3nk2F9GJ+7DpaI5F2jOHSWHm3XffxaxZs6BSqQzH1Go1Zs+ejffee89ixREREZHl/OOJSBz/5xPI/nMFYh+Zs9ltzk1Kl3yjy3o/ZurevTsE4a/dJC5cuIDCwkKEhpYPCMrJyYFMJkNERAROnDhhnWpNwMdMRERED7b5WA4+O3gZt/ILodGXwNjxJ2FNPbB/junbGdXEmO/vevc1Pfvss+bWRURERHao8tYKQHm4+fjnC1Devlev67NuOUjPjKNizwwREZHpFmw/je1p13DrbskDz8teNMKi97X6AGAiIiJqHOaP7IQTf46zsVcmDWl2cnKqMn7mfhUL6RERERFZm0lhZuvWrVVeFxcX4+TJk1i3bh0WLFhgkcKIiIiI6sOkMDNy5Mhqx5577jl07NgRmzZtwuTJk80ujIiIiByHSqu32gJ6dbHomJmoqCj8/PPPlmySiIiIHMDxbI1k97ZYmNHr9fjkk0/QsmVLSzVJREREDuLzw5clu7dJj5n8/PyqDAAWRRH5+fnw9PTE+vXrLVYcERER2Y/uIT44WcseTyeVWhtX8xeTwsxHH31U5bWTkxOaN2+OqKgo7s1ERETUQH06oSeiE/fV+J6Ui9aZFGZiYmLqdd60adPwr3/9C/7+/qbchoiIiOyIVAN862LVRfPWr18Pnc6yW44TERERVWbVMNPAd0ogIiIiO8DtDIiIiMgiVFppNpxkmCEiIiKLyM67K8l9GWaIiIjIIhZ8d1qS+zLMEBERUb2F+3vW+t65G3dsWMlfTJqaDQD37t3DqVOnkJubi7KysirvPfPMMwCACRMmwMfHx7wKiYiIyG7sfWsgWs/7UeoyqjApzOzcuRMvvfQS8vLyqr0nCAJKS0sBACtWrDCvOiIiIqI6mPSYacaMGRgzZgxUKhXKysqq/FQEGSIiIiJbMCnM5ObmYtasWVAoFJauh4iIiOyYVNOvH8SkMPPcc8/hwIEDFi6FiIiI7N3nh7OkLqEak8bMLFu2DGPGjMGhQ4fQuXNnuLq6Vnl/5syZ9W7r2rVrmDt3Lnbs2AG9Xo927dph9erV6NGjB4DyVYQXLFiAlStXQqPRICoqCsuXL0fHjh1NKZ2IiIhMpNLqsbqOMJOm1KBriG03nTYpzGzYsAG7du2Ch4cHDhw4AEEQDO8JglDvMKPRaNCnTx8MHDgQO3bsQEBAAC5dugRfX1/DOUuWLMHSpUuxdu1atGvXDgsXLsSQIUOQmZkJb29vU8onIiIiExzLvoWyOnYqmvjfX3FqwTDbFPQnQTRhA6XAwEDMnDkT8+bNg5OT6UvVzJs3D7/88gsOHTpU4/uiKCI4OBixsbGYO3cuAKCwsBAKhQKLFy/GlClT6ryHTqeDXC6HVqvlNHEiIiITLNh+GpuOKXG3uKzukwFkLxph9j2N+f42KYkUFRXh+eefNyvIAMB3332Hnj17YsyYMQgICED37t2xatUqw/tZWVlQq9UYOnSo4ZhMJkP//v2RkpJSY5uFhYXQ6XRVfoiIiMh4aUoNWs/7EWuOXKl3kGkhl1m5qupMSiMxMTHYtGmT2Te/fPkyVqxYgYiICOzatQtTp07FzJkz8cUXXwAA1Go1AFSbNaVQKAzv3S8xMRFyudzwExISYnadREREjUmaUoNHE37GyOU1dxw8yC9xj1uhogczacxMaWkplixZgl27dqFLly7VBgAvXbq0Xu2UlZWhZ8+eSEhIAAB0794dZ86cwYoVK/DSSy8Zzqs8Jgcof/x0/7EKcXFxmDVrluG1TqdjoCEiIqqFSqtHVl4B9EUlWHM4C79euoUSE9pxFYALieY/XjKFSWEmPT0d3bt3BwCcPl11U6naQkZNgoKC0KFDhyrH2rdvj6SkJADlY3OA8h6aoKAgwzm5ubm1rnEjk8kgk9m+i4uIiMjRbDqag7lJ6Wa10byJGxaN7ozB7QMtVJXxTAoz+/fvt8jN+/Tpg8zMzCrHzp8/j1atWgEAwsLCEBgYiD179hjCU1FREZKTk7F48WKL1EBERNTQVfS+hPl7IUjuAQDYfMz0IOMiAI+2aYrZT0bafBp2jfVIefM333wTvXv3RkJCAsaOHYvff/8dK1euxMqVKwGU9/LExsYiISEBERERiIiIQEJCAjw9PTFu3DgpSyciIrJ7Kq0enx/OwurDWSgTAUEAIhVNcE59B0ZPZf7Ty71bIf6ZThat01yShplevXph69atiIuLw7/+9S+EhYXho48+wvjx4w3nzJkzB3q9HtOmTTMsmrd7926uMUNERPQAm47mYF5SepXQIopAhvqOSe39T7dgzBkWaejZsScmrTPjSLjODBERNTZpSg2eXZ5icu9LBQHA28Mj8Wq/cEuUZRRjvr8l7ZkhIiIiy7LEoF4BwIyB4fjHE5GWKcrKGGaIiIgagDSlBgt/OIujV26bdL0AoGtLH4x/tBXG9Ay1aG3WxjBDRETkwDYfy8F7287gXkn9VuityTsSPUqyFIYZIiIiB6PS6pF04iqW7joPUyKMAKCZlytefCTUYR4lPQjDDBERkQOZ/U0qNp+4ZvL1MY+2woJn7WtqtbkYZoiIiOzM/YvcpSk1+DnjBj47cBlFZabNUereUo5PJ/awy6nV5mKYISIisiObjuYgbks6KjJLEzcn3CkybTyMswAsGNkRg9srGmSIqcAwQ0REZCdUWn21adWmBBkfd2e8PijCoQf1GoNhhoiISGIqrR4/n72BhJ8yzGpH4S3Dthl9GnQvTE0YZoiIiCSi0uoRv/00dp3NNbkNFwHoHuqLd5/qYBebPkqBYYaIiMjGVFo95m8/jd1mhBgA2D69d6MNMJUxzBAREdnA5mM52HlaDXcXZ/x4Wm1yOz7uzhjdvSXmj2xY06vNwTBDRERkJSqtHknHr2LZvotmrdDbxM0Zi5/rgodb+TW68TD1wTBDRERkQSqtHsv3XcAPp1S4rS8xuZ2Wvu4I9vXAmJ4tHW6vJFtjmCEiIqqn+xezu/+9T/ZdwIbflGbdw9PNGXv/0Z89MEZgmCEiIqqHyovZOQlA4qjOcBKA5fsu4GZ+EQqKTX+M5O4CtPD1xAuPhDaatWEsiWGGiIioDiqtvsqqvGUiqi1uZ4qHQ+SYPqgtBrcPNLutxoxhhoiIqA5ZeQUwcUukaroE+yBuRAe09vfkoyQLYZghIiJ6gL0Zaizecc7sdnq28sV7jXhhO2timCEiokbpQYN592ao8cGPZ3E5T2/WPdycgEl9wxDTJ4y9MFbEMENERI1OTYN5IwO9serQZew4pUapGW33j/CHv7cbhncO4lgYG2GYISKiRsVag3lDfN3xzWu92QMjAYYZIiJqVCw5mNdJAMb0aIlxUaEcCyMhhhkiImo00pQaTPzvb2a34+7qhPdHduTKvHaCYYaIiBq8f+86h/8ezoa+2PTRMAKAJzsp8FyPlhwLY2cYZoiIqMFRafX4OeMGTilvY8uJayg147GSzBn4e79w/OOJSMsVSBbFMENERA2GSqvHa18eQ+pVnVntOAEY2kGB+SM7ckCvA2CYISIih5em1ODjvRew99xNk9twBfB092CM6MIp1Y6GYYaIiBzS3gw1vjhyBSdyNMi/Z/pYGJkL8NbQSG7w6MAYZoiIyKEs2H4aX/x6xaxxMO7OAnqG+WH2E5GcUt0AOEl589atW0MQhGo/06dPBwCIooj4+HgEBwfDw8MDAwYMwJkzZ6QsmYiIJLL5WA5az/sRa46YHmTaNvfC9um9ce6D4Vj/t2gGmQZC0p6Zo0ePorT0r67B06dPY8iQIRgzZgwAYMmSJVi6dCnWrl2Ldu3aYeHChRgyZAgyMzPh7e0tVdlERGQjezPU2HzsKvafy0WhiQmmmZcr3hzSDoPbKziYt4ESRFG00DqI5ouNjcUPP/yACxcuAACCg4MRGxuLuXPnAgAKCwuhUCiwePFiTJkypV5t6nQ6yOVyaLVa+Pj4WK12IiIyTk0bPaq0ehy/osGpqxps+E2JO4WmjYXxkTkjtJnnnyGGg3kdkTHf33YzZqaoqAjr16/HrFmzIAgCLl++DLVajaFDhxrOkclk6N+/P1JSUmoNM4WFhSgsLDS81unMm55HRESWV9NGj4D5eyT5erhgR2w/9sA0MnYTZrZt24bbt2/j5ZdfBgCo1WoAgEKhqHKeQqHAlStXam0nMTERCxYssFqdRERkHktv9OjsBHQK9sGER1txe4FGym7CzOrVqzFs2DAEBwdXOS4IQpXXoihWO1ZZXFwcZs2aZXit0+kQEhJi2WKJiMhkltro0dfDBe+MaM8AQ/YRZq5cuYKff/4ZW7ZsMRwLDCx/xqlWqxEUFGQ4npubW623pjKZTAaZTGa9YomIqE41jYdJU2qw6tBl7DuXa1bbCh83bJvel4+SyMAuwsyaNWsQEBCAESNGGI6FhYUhMDAQe/bsQffu3QGUj6tJTk7G4sWLpSqViIjqcP94mNcGhGP7yWu4evueyW02kTnhsYjm3OSRaiR5mCkrK8OaNWsQExMDF5e/yhEEAbGxsUhISEBERAQiIiKQkJAAT09PjBs3TsKKiYioNjWNh1m+/5LJ7fnInPGfF7oxwNADSR5mfv75Z+Tk5GDSpEnV3pszZw70ej2mTZsGjUaDqKgo7N69m2vMEBHZqaTjV80eDxMklyG6TTPukUT1ZlfrzFgD15khIrKuzcdy8M2xq8hU50N3r8SkNnw9XNDG3wvTB7VlgCEADrrODBEROZY0pQbPf/Yr7pWUmdxGiJ8HvpkazcG8ZBaGGSIiqqam2UhAeYD56rccJGfm4kZ+kcntt/H3xDsj2rMXhiyCYYaIiAxUWj0+P5yF1YezqqzOe+66Dl/+noMSM7aqbunrjqe6BiGmdxh7YsiiGGaIiAhA+ZTqeUnpqBxXzF2dFwDaB3nj85d7McCQ1TDMEBGRYUq1pWaEtPJ1R1S4P8Y/GoquIX4WapWoZgwzRERkkS0GXARg/jMd8XgHBXthyKYYZoiIGrE0pQZLdp7DL5dumdxGoLcMkx8Lw6v9wi1YGVH9McwQETUyKq0ea365jPW/5uBukenTqp/ooED8yI7shSHJMcwQETUSKq0e87edwe6MGya34e4i4OU+rTkjiewKwwwRUQNR29owezPUWLrnAs5c15nctoergI1/j+ZgXrJLDDNERA3A/TtVJ47qDP8mbpiblI68O6YtbtfE3RkdAn0wpmdLjOkZauGKiSyHYYaIyMHVtFO1OWvDhDb1wPynO3B1XnIYDDNERA7u+BWN2dOqAaB7iByfTujBsTDkcBhmiIgc2OxvUrH5xDWTr/f1cMFTXYMwfWAEQww5LIYZIiIHlKbU4MWVv+JusfFTq4Pl7hj/aChGPdySAYYaBIYZIiIHodLqsWRnBnaevgG9kSHGW+aM4Z2DMC6K2wtQw8MwQ0Rk5zYfy8H//nweV28XmnT9y9GtED+yk4WrIrIfDDNERHas5/t7kFdg2tTqZ7sFY+6wSD5KogaPYYaISAK1LXCXptTg9+xb8PVwRcKPGdDoS4xq96EALzzdrQVG9+B4GGo8GGaIiGyspgXu+rVrjtiNJ/FblsakNgO8Zdg+ow8DDDVKDDNERDZk6QXuOgX74M0hEVzgjho1hhkiIhvKyivgAndEFsYwQ0RkZRXjY/RFJUi7qjW5HS9XJ7w2sC3HwxDdh2GGiMiKNh3NwbykdJjTGdPG3xPvjGjPR0lEtWCYISKysIqemD2n1Vhz5IpJbYzoHIiHQ/3Qs7UfF7kjqgPDDBGRBW06mmPWgF4AiBseiSn9wi1UEVHDxzBDRGQBaUoNvvotB98cu2rS9a5OwOTHwhDTO4zjYYiMxDBDRGSGNKUG0786gau375l0fVNPV3w4pgvHwxCZgWGGiMgEezPUWPD9WeTc0pt0/aNhTfFqvzCGGCILYJghIqqnNKUG7207jVPXdCZd39LXHU91CUJMHz5KIrIkJylvXlJSgnfffRdhYWHw8PBAmzZt8K9//QtlZX9tbS+KIuLj4xEcHAwPDw8MGDAAZ86ckbBqImpsVFo9nv8sBSOXp5gUZB5t7YcjcYNweN5gzBvegUGGyMIk7ZlZvHgx/u///g/r1q1Dx44dcezYMbzyyiuQy+V44403AABLlizB0qVLsXbtWrRr1w4LFy7EkCFDkJmZCW9vbynLJ6IGLk2pwZKd5/DLpVtGXxssd8e4qFAucEdkA4IoihZYWNs0Tz31FBQKBVavXm04Nnr0aHh6euLLL7+EKIoIDg5GbGws5s6dCwAoLCyEQqHA4sWLMWXKlDrvodPpIJfLodVq4ePjY7XfhYgajjSlBv/4Jg0XbxaYdP2MgeF464lIC1dF1LgY8/0t6WOmvn37Yu/evTh//jwAIC0tDYcPH8bw4cMBAFlZWVCr1Rg6dKjhGplMhv79+yMlJUWSmomo4dp8LAc93t+NkctTjA4yIX7umNIvDEfiBjHIENmYpI+Z5s6dC61Wi8jISDg7O6O0tBQffPABXnzxRQCAWq0GACgUiirXKRQKXLlS86qahYWFKCwsNLzW6UwbqEdEjcPeDDW+PHIFKZf+QFGp8R3VQXJ3bJnWm4+SiCQkaZjZtGkT1q9fjw0bNqBjx45ITU1FbGwsgoODERMTYzhPEIQq14miWO1YhcTERCxYsMCqdROR49ubocasTWnQ3isx+lpXJ6ClnwfefaoDp1YT2QFJw8zs2bMxb948vPDCCwCAzp0748qVK0hMTERMTAwCA8v/kVCr1QgKCjJcl5ubW623pkJcXBxmzZpleK3T6RASEmLF34KIHEmaUoMpX56AWmfaInfcaoDI/kgaZu7evQsnp6rDdpydnQ1Ts8PCwhAYGIg9e/age/fuAICioiIkJydj8eLFNbYpk8kgk8msWzgROZS9GWqsOngZZ1X50JnQE9OqqQf+9lgbPN5BwcdJRHZI0jDz9NNP44MPPkBoaCg6duyIkydPYunSpZg0aRKA8sdLsbGxSEhIQEREBCIiIpCQkABPT0+MGzdOytKJyAGkKTX427pjuHmnyKTrw/29sPT5rty1msjOSRpmPvnkE7z33nuYNm0acnNzERwcjClTpuCf//yn4Zw5c+ZAr9dj2rRp0Gg0iIqKwu7du7nGDBHVKk2pwaxNabiUZ9rU6pa+7lg+/mGGGCIHIek6M7bAdWaIGo80pQbzkk4hQ33HpOu93Jyx4dUohhgiO2DM9zf3ZiIih6bS6pF04iq+TLmCG/mFdV9wn9ZNPRDgI8OYniEY0zPUChUSkbUxzBCRw5q/7TTW/VrzmlN1ebZbEOYOa88BvUQNAMMMETmUNKUG21KvY3vqNdwqKDb6+gBvN2yf0ZchhqgBYZghIoew+VgOPtx5Drl3jA8wgT5u6B7ih+d6tuQid0QNEMMMEdkFlVaPrLwChPl7Vek1SVNqMPazIygsMX6uQp/wppjzZCQH9BI1cAwzRCS5TUdzELclHWUi4CQAiaM64/leoZi2/jh+Oq02ur1uIXKsmNCDj5KIGglOzSYiSam0evRZtA9l9/1LFNDEDblGLnbXuqkn/vfFbuyJIWoAODWbiBxGVl5BtSADoN5BJtjXHY9F+GPcI6EMMUSNFMMMEUlKX2T8XkkA0KuVL959qgMDDBExzBCRNDYfy8EHP2bgtt74MMOdq4moMoYZIrIplVaPpz4+hD9MWCOme4gcn3JgLxHdh2GGiGwmfvtprD1i3Iq9/l5uiH+mI3q09mOIIaIaMcwQkVUt2H4a29Ku425RiVFrxXi5CYh/phP3SyKiOjHMEJFVqLR6PLZ4H0rKjLvOy03Axy8+zJV6iajeGGaIyGJUWj2Sjl/FjtMqnLmeb9S1cg8XLB3blSGGiIzGMENEZktTavDx3gvYe+6m0deG+3vh7RGRDDFEZDKGGSIy2eZjOVi045xJM5PCmnliw98f5aBeIjIbwwwRmaTfkn3IuaU3+jp/Lzcsfq4ze2KIyGIYZoio3lRaPeK+PYXfsv6A3oiZSR6uAkKbemLOk3ycRESWxzBDRHXam6HG0t3ncUZl3KBegKv1EpH1McwQUa32Zqgx/avjuGfkjgMhfu544ZFQjHq4JcfEEJHVMcwQUTV7M9SY/e0p3DJhYG/nYB98P/MxK1RFRFQzhhkiMtibocZbm9OguVv/rphmXq5o09wL/l4yPNezJcfEEJHNMcwQEVRaPUZ9+gtU2kKjrnMWgOPvDbVSVURE9cMwQ9SIqbR6LNpxDttTrxt1nYszMKprCywZ2806hRERGYFhhqgRSlNq8P6PZ3Es+7bR104fEI7ZT0ZavigiIhMxzBA1ImlKDd7YeBLZfxi32F0TmTOGtA/AnGHtOTuJiOwOwwxRI7Dq4CV8uCsTRaX1X+gOAJ7ooED8yI4MMERk1xhmiBq4LvG7oDNyoZhguQxJ0/owxBCRQ2CYIWqgNh/LQfz2MygoLqv3NcFyd7z/bEdOryYih+IkdQH5+fmIjY1Fq1at4OHhgd69e+Po0aOG90VRRHx8PIKDg+Hh4YEBAwbgzJkzElZMZF9UWj1SLuVBpdVj1cFLGLr0ANq98xNmf5tuVJCZPiAcKXGDGWSIyOFI3jPzt7/9DadPn8aXX36J4OBgrF+/Ho8//jjOnj2LFi1aYMmSJVi6dCnWrl2Ldu3aYeHChRgyZAgyMzPh7e0tdflEktp0NAdxW9JRZtxQmCqe6BCA+JGd+EiJiByWIIqiGf8Mmkev18Pb2xvbt2/HiBEjDMe7deuGp556Cu+//z6Cg4MRGxuLuXPnAgAKCwuhUCiwePFiTJkypc576HQ6yOVyaLVa+Pj4WO13IbI1lVaPPov2mRxk3hraDqN7cO8kIrJPxnx/S/qYqaSkBKWlpXB3d69y3MPDA4cPH0ZWVhbUajWGDv1rhVGZTIb+/fsjJSXF1uUS2ZWsvAKTgky4vyeyF43AjEERDDJE1CBI+pjJ29sb0dHReP/999G+fXsoFAp8/fXX+O233xAREQG1Wg0AUCgUVa5TKBS4cuVKjW0WFhaisPCvJdl1Op31fgEiK1Np9cjKK0CYv5cheCzYfhqbjuXgbrFxSaZPeFPMeTISXUP8rFEqEZFkJB8z8+WXX2LSpElo0aIFnJ2d8fDDD2PcuHE4ceKE4RxBEKpcI4pitWMVEhMTsWDBAqvWTGQLlcfDOAlA4qjOeHfbaRQbuVZMuwAvrJscxV4YImqwJJ/NFB4ejuTkZNy5cwdKpRK///47iouLERYWhsDA8lkVFT00FXJzc6v11lSIi4uDVqs1/CiVSqv/DkSWptLqqwzsLROBuUnpRgWZPuHNsH16b+yeNYBBhogaNMl7Zip4eXnBy8sLGo0Gu3btwpIlSwyBZs+ePejevTsAoKioCMnJyVi8eHGN7chkMshkMluWTmRxpo6HAYBuIb5YMeFhBhgiajQkDzO7du2CKIp46KGHcPHiRcyePRsPPfQQXnnlFQiCgNjYWCQkJCAiIgIRERFISEiAp6cnxo0bJ3XpRFbz7tZ0o68RAGyb3ptjYoio0ZE8zGi1WsTFxeHq1ato2rQpRo8ejQ8++ACurq4AgDlz5kCv12PatGnQaDSIiorC7t27ucYMNThpSg2++i0Hm49dhbGdMq9Et8L8kZ2sUhcRkb2TdJ0ZW+A6M2TvVFo9YjeexG9ZGqOuc3UCJkQxxBBRw2TM97fkPTNEjdXeDDU+2XcRqUqtUddFBHjhC85OIiIyYJghksBTHx/C6evGrYGk8JEh4X86ce8kIqL7MMwQ2YhKq8ees2p8/VsOMtR3jLp2xsBwvPVEpJUqIyJybAwzRFam0urxyd4L2PC7cWseuTkLGNE5EHOGtecjJSKiB2CYIbKSNKUG/7v3Avadu2nUdZ2CffDmkAg+TiIiqieGGSILU2n1eG39caMH9j7RQYH4kR3ZC0NEZCSGGSIL2nQ0B3OTjF/wbninQHw6oYcVKiIiavgYZogsRKXVGx1kBkcGYObgtly1l4jIDAwzRBay5fjVep/braUcKyb24CMlIiILYJghspCLufl1njM4sjlmDo5gTwwRkQUxzBBZyFNdg7E1VVXteIifO+Y+2R49WvuxJ4aIyAoYZogsZHD7QDwc6osTObcNxx5SNMGuN/tLVxQRUSPAMENkQVum9cHeDDUOZN7EgIeac60YIiIbYJghsrDB7QMZYoiIbMhJ6gKIiIiIzMEwQ0RERA6NYYaIiIgcGsMMEREROTSGGSIiInJoDDNERETk0BhmiIiIyKExzBAREZFDY5ghIiIih8YwQ0RERA6NYYaIiIgcWoPfm0kURQCATqeTuBIiIiKqr4rv7Yrv8Qdp8GEmPz8fABASEiJxJURERGSs/Px8yOXyB54jiPWJPA6srKwM169fh7e3NwRBsPr9dDodQkJCoFQq4ePjY/X72SN+BuX4OZTj58DPoAI/h3L8HOr3GYiiiPz8fAQHB8PJ6cGjYhp8z4yTkxNatmxp8/v6+Pg02r+kFfgZlOPnUI6fAz+DCvwcyvFzqPszqKtHpgIHABMREZFDY5ghIiIih8YwY2EymQzz58+HTCaTuhTJ8DMox8+hHD8HfgYV+DmU4+dg+c+gwQ8AJiIiooaNPTNERETk0BhmiIiIyKExzBAREZFDY5ixoE8//RRhYWFwd3dHjx49cOjQIalLsqmDBw/i6aefRnBwMARBwLZt26QuSRKJiYno1asXvL29ERAQgGeffRaZmZlSl2VTK1asQJcuXQxrSERHR2PHjh1SlyW5xMRECIKA2NhYqUuxqfj4eAiCUOUnMDBQ6rJs7tq1a5gwYQKaNWsGT09PdOvWDcePH5e6LJtq3bp1tb8LgiBg+vTpZrXLMGMhmzZtQmxsLN555x2cPHkSjz32GIYNG4acnBypS7OZgoICdO3aFcuWLZO6FEklJydj+vTp+PXXX7Fnzx6UlJRg6NChKCgokLo0m2nZsiUWLVqEY8eO4dixYxg0aBBGjhyJM2fOSF2aZI4ePYqVK1eiS5cuUpciiY4dO0KlUhl+0tPTpS7JpjQaDfr06QNXV1fs2LEDZ8+exb///W/4+vpKXZpNHT16tMrfgz179gAAxowZY17DIlnEI488Ik6dOrXKscjISHHevHkSVSQtAOLWrVulLsMu5ObmigDE5ORkqUuRlJ+fn/jf//5X6jIkkZ+fL0ZERIh79uwR+/fvL77xxhtSl2RT8+fPF7t27Sp1GZKaO3eu2LdvX6nLsDtvvPGGGB4eLpaVlZnVDntmLKCoqAjHjx/H0KFDqxwfOnQoUlJSJKqK7IVWqwUANG3aVOJKpFFaWoqNGzeioKAA0dHRUpcjienTp2PEiBF4/PHHpS5FMhcuXEBwcDDCwsLwwgsv4PLly1KXZFPfffcdevbsiTFjxiAgIADdu3fHqlWrpC5LUkVFRVi/fj0mTZpk9t6JDDMWkJeXh9LSUigUiirHFQoF1Gq1RFWRPRBFEbNmzULfvn3RqVMnqcuxqfT0dDRp0gQymQxTp07F1q1b0aFDB6nLsrmNGzfixIkTSExMlLoUyURFReGLL77Arl27sGrVKqjVavTu3Rt//PGH1KXZzOXLl7FixQpERERg165dmDp1KmbOnIkvvvhC6tIks23bNty+fRsvv/yy2W01+I0mben+ZCmKok126ib7NWPGDJw6dQqHDx+WuhSbe+ihh5Camorbt28jKSkJMTExSE5OblSBRqlU4o033sDu3bvh7u4udTmSGTZsmOHPnTt3RnR0NMLDw7Fu3TrMmjVLwspsp6ysDD179kRCQgIAoHv37jhz5gxWrFiBl156SeLqpLF69WoMGzYMwcHBZrfFnhkL8Pf3h7Ozc7VemNzc3Gq9NdR4vP766/juu++wf/9+SXZul5qbmxvatm2Lnj17IjExEV27dsX//u//Sl2WTR0/fhy5ubno0aMHXFxc4OLiguTkZHz88cdwcXFBaWmp1CVKwsvLC507d8aFCxekLsVmgoKCqgX59u3bN6pJIpVduXIFP//8M/72t79ZpD2GGQtwc3NDjx49DKOyK+zZswe9e/eWqCqSiiiKmDFjBrZs2YJ9+/YhLCxM6pLsgiiKKCwslLoMmxo8eDDS09ORmppq+OnZsyfGjx+P1NRUODs7S12iJAoLC5GRkYGgoCCpS7GZPn36VFui4fz582jVqpVEFUlrzZo1CAgIwIgRIyzSHh8zWcisWbMwceJE9OzZE9HR0Vi5ciVycnIwdepUqUuzmTt37uDixYuG11lZWUhNTUXTpk0RGhoqYWW2NX36dGzYsAHbt2+Ht7e3ocdOLpfDw8ND4ups4+2338awYcMQEhKC/Px8bNy4EQcOHMDOnTulLs2mvL29q42V8vLyQrNmzRrVGKq33noLTz/9NEJDQ5Gbm4uFCxdCp9MhJiZG6tJs5s0330Tv3r2RkJCAsWPH4vfff8fKlSuxcuVKqUuzubKyMqxZswYxMTFwcbFQDLHAzCr60/Lly8VWrVqJbm5u4sMPP9zopuLu379fBFDtJyYmRurSbKqmzwCAuGbNGqlLs5lJkyYZ/rfQvHlzcfDgweLu3bulLssuNMap2c8//7wYFBQkurq6isHBweKoUaPEM2fOSF2WzX3//fdip06dRJlMJkZGRoorV66UuiRJ7Nq1SwQgZmZmWqxN7ppNREREDo1jZoiIiMihMcwQERGRQ2OYISIiIofGMENEREQOjWGGiIiIHBrDDBERETk0hhkiIiJyaAwzRERE5NAYZojogeLj49GtWzepy3AogiBg27ZtUpdB1GgwzBCRZJKSkuDs7FzrzsGRkZGYOXOmjauyfwMGDIAgCBAEAW5ubggPD0dcXFyj28iTqALDDBFJ5plnnkGzZs2wbt26au/98ssvyMzMxOTJkyWozP69+uqrUKlUuHjxIpYsWYLly5cjPj5e6rKIJMEwQ+RABgwYgNdffx2xsbHw8/ODQqHAypUrUVBQgFdeeQXe3t4IDw/Hjh076tXe2rVr4evrW+XYtm3bIAhCtXM/++wzhISEwNPTE2PGjMHt27cBALt27YK7u7vhdYWZM2eif//+D7y/q6srJk6ciLVr1+L+beI+//xz9OjRA127dq3X71KTAwcOQBAE7Nq1C927d4eHhwcGDRqE3Nxc7NixA+3bt4ePjw9efPFF3L17t15ttm7dGh999FGVY926dasWJFQqFYYNGwYPDw+EhYVh8+bNhveio6Mxb968KuffvHkTrq6u2L9/f73q8PT0RGBgIEJDQzF69GgMGTIEu3fvrte1RA0NwwyRg1m3bh38/f3x+++/4/XXX8drr72GMWPGoHfv3jhx4gSeeOIJTJw4sd5fzvVx8eJFfPPNN/j++++xc+dOpKamYvr06QCAxx9/HL6+vkhKSjKcX1paim+++Qbjx4+vs+3Jkyfj8uXLSE5ONhwrKCjAN998Y7Femfj4eCxbtgwpKSlQKpUYO3YsPvroI2zYsAE//vgj9uzZg08++cQi96rw3nvvYfTo0UhLS8OECRPw4osvIiMjAwAwfvx4fP3111UC3KZNm6BQKOoMgDVJS0vDL7/8AldXV4vVT+RQLLb/NhFZXf/+/cW+ffsaXpeUlIheXl7ixIkTDcdUKpUIQDxy5Eid7a1Zs0aUy+VVjm3dulWs/E/D/PnzRWdnZ1GpVBqO7dixQ3RychJVKpUoiqI4c+ZMcdCgQYb3d+3aJbq5uYm3bt2q1+8VFRUlvvTSS4bXn3/+uejh4SFqNJp6XV+b/fv3iwDEn3/+2XAsMTFRBCBeunTJcGzKlCniE088Ua82W7VqJf7nP/+pcqxr167i/PnzDa8BiFOnTq1yTlRUlPjaa6+JoiiKubm5oouLi3jw4EHD+9HR0eLs2bPrVUP//v1FV1dX0cvLS3RzcxMBiE5OTuK3335br+uJGhr2zBA5mC5duhj+7OzsjGbNmqFz586GYwqFAgCQm5trsXuGhoaiZcuWhtfR0dEoKytDZmYmgPKehgMHDuD69esAgK+++grDhw+Hn59fvdqfPHkyvv32W+Tn5wMof8Q0atSoao/AKuTk5KBJkyaGn4SEhAe2X/kzUygU8PT0RJs2baocs+TnBZR/Rve/ruiZad68OYYMGYKvvvoKAJCVlYUjR47Uqyerwvjx45GamoojR45g7NixmDRpEkaPHm25X4DIgTDMEDmY+x8lCIJQ5VjFeJeysrI623Jycqo2VqW4uLjO6yruUfGfjzzyCMLDw7Fx40bo9Xps3boVEyZMqLOdCi+88AIEQcCmTZtw8eJFHD58+IGPmIKDg5Gammr4mTp16gPbv//zqekzrM/nBZj+mVXcp8L48ePx7bffori4GBs2bEDHjh2NGh8kl8vRtm1bPPzww1i/fj2Sk5OxevXqel9P1JAwzBA1Ys2bN0d+fj4KCgoMx1JTU6udl5OTY+h1AYAjR47AyckJ7dq1MxwbN24cvvrqK3z//fdwcnLCiBEj6l2Ht7c3xowZgzVr1uDzzz9HmzZtMGDAgFrPd3FxQdu2bQ0/TZs2rfe9zNW8eXOoVCrDa51Oh6ysrGrn/frrr9VeR0ZGGl4/++yzuHfvHnbu3IkNGzYYFf7u5+rqirfffhvvvvuuRcdKETkKhhmiRiwqKgqenp54++23cfHiRWzYsAFr166tdp67uztiYmKQlpaGQ4cOYebMmRg7diwCAwMN54wfPx4nTpzABx98gOeeew7u7u5G1TJ58mSkpKRgxYoVmDRpUo0zquzBoEGD8OWXX+LQoUM4ffo0YmJi4OzsXO28zZs34/PPP8f58+cxf/58/P7775gxY4bhfS8vL4wcORLvvfceMjIyMG7cOLPqGjduHARBwKeffmpWO0SOiGGGqBFr2rQp1q9fj59++gmdO3fG119/XeNaJW3btsWoUaMwfPhwDB06FJ06dar2pRkREYFevXrh1KlTRo39qNC3b1889NBD0Ol0iImJMfVXsrq4uDj069cPTz31FIYPH45nn30W4eHh1c5bsGABNm7ciC5dumDdunX46quv0KFDhyrnjB8/HmlpaXjssccQGhpqVl1ubm6YMWMGlixZgjt37pjVFpGjEcT7H/4SERERORD2zBAREZFDY5ghasCmTp1aZQpz5Z+6ZgBZyrBhw2qtoa4p1bZ2/5Tv+39q20PKkg4dOvTAGoioOj5mImrAcnNzodPpanzPx8cHAQEBVq/h2rVr0Ov1Nb7XtGlTm85EqktJSQmys7Nrfb9169ZwcXGxag16vR7Xrl2r9f22bdta9f5EjohhhoiIiBwaHzMRERGRQ2OYISIiIofGMENEREQOjWGGiIiIHBrDDBERETk0hhkiIiJyaAwzRERE5NAYZoiIiMih/X/4Jlwm5A3KZQAAAABJRU5ErkJggg==", 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" ] @@ -267,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "77389803-b5ab-408d-a825-68c2ac30c131", "metadata": {}, "outputs": [ @@ -277,13 +1327,13 @@ "Text(0.5, 1.0, 'Singles Only')" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -310,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "594beb18-cdee-4cc4-8d4b-64848ce4700e", "metadata": {}, "outputs": [ @@ -320,13 +1370,13 @@ "Text(0.5, 0, 'log(a) [AU]')" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -350,7 +1400,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "cf7ede5c-417f-4342-8e6a-3b9efae1e43e", "metadata": {}, "outputs": [ @@ -360,13 +1410,13 @@ "Text(0, 0.5, 'N objects')" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -392,7 +1442,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "ce24f553-6020-4000-be02-13feb6c7ba39", "metadata": {}, "outputs": [], @@ -402,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "a9b3f917-bf60-4c99-bb57-4270541733f9", "metadata": {}, "outputs": [], @@ -413,7 +1463,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "dade2fb6-ae07-4597-9db7-5bfee462599d", "metadata": {}, "outputs": [], @@ -430,13 +1480,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "13d68eb1-b495-4051-a150-4c63ddeefc2a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/docs/Quick_Start_Make_Cluster_w_BDs.ipynb b/docs/Quick_Start_Make_Cluster_w_BDs.ipynb new file mode 100644 index 00000000..dea97223 --- /dev/null +++ b/docs/Quick_Start_Make_Cluster_w_BDs.ipynb @@ -0,0 +1,997 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SPISEA Quick Start: Making A Cluster with Brown Dwarfs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a quick start guide to making a synthetic cluster using the SPISEA package, with the recent addition of brown dwarf capabilities. The cluster is constructed using a user-specified isochrone and initial mass function (IMF). Detailed documentation is provided in the ReadtheDocs page (https://spisea.readthedocs.io/en/latest/index.html).\n", + "\n", + "Before starting this tutorial, it is assumed that SPISEA has been installed and the user's python path has been altered to include the SPISEA top-level directory" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import vstack\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Make a SPISEA isochrone object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cluster is made from a theoretical isochrone at a given age, extinction, and distance from Earth. These parameters MUST be specified by the user. Other inputs (e.g. stellar evolution/atmosphere models, extinction law, and photometric filters used) are optional keywords. See documentation for all keywords and their default values.\n", + "\n", + "Important Note: The IsochronePhot class saves its output as a FITS table, which it will read on subsequent calls for the same isochrone rather than regenerating it from scratch. We highly recommend reading the \"Tips and Tricks: The IsochronePhot Object\" section of the Isochrone object documentation for details on how this process works.\n", + "\n", + "Here, we create a 5 Myr cluster isochrone at an extinction of 0.8 mags and distance of 4000 pc from Earth. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define isochrone parameters\n", + "logAge = np.log10(5*10**6.) # Age in log(years)\n", + "AKs = 0.8 # extinction in mags\n", + "dist = 4000 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define brown dwarf supported evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Also specify filters for synthetic photometry (optional). Here we use \n", + "# the HST WFC3-IR F127M, F139M, and F153M filters\n", + "filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " iso_dir=iso_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once calculated, the isochrone will be written as a fits file to a location set by the \"iso_dir\" keyword (note: if iso_dir is not defined, the isochrone is saved in the current working directory by default). In the future, the IsochronePhot function will read this file directly rather than recalculating the isochrone again. \n", + "\n", + "The output file is named as: \"iso_logAge_AKs_distance_metallicity.fits, using the specified values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " L Teff R mass logg isWR mass_current phase m_hst_f127m m_hst_f139m m_hst_f153m \n", + " W K m solMass solMass \n", + "---------------------- ------------------ ------------------ ------- ------ ----- ------------ ----- ------------------ ------------------ ------------------\n", + "2.5969998772673413e+20 432.41425250986447 102099864.14758782 0.0005 2.8027 False 0.0005 1 33.96148101996776 41.349910373359705 34.0979568030492\n", + " 8.992323640770438e+20 594.8397740692212 100398023.76104526 0.001 3.1183 False 0.001 1 31.543816736848385 37.485693950053815 31.74912432063749\n", + "3.7824389676866323e+21 848.3987109859956 101222058.29958254 0.002 3.412 False 0.002 1 29.766073130622274 33.63286970536957 29.89254633083153\n", + " 8.832261707714201e+21 1038.2453626533147 103282124.85442652 0.003 3.5708 False 0.003 1 29.02543484155902 31.81705248352881 28.90711516172416\n", + " 1.637087638152397e+22 1197.8432778258546 105639214.73169859 0.004 3.6762 False 0.004 1 28.50411100931251 30.631805143722218 28.20569350209261\n", + "2.7044062385807246e+22 1344.3117666713051 107801589.44807644 0.005 3.7555 False 0.005 1 28.743636291005714 29.26628300083259 27.46562164782375\n", + " 4.309946994929829e+22 1495.2024500170564 110008226.74654458 0.006 3.817 False 0.006 1 28.967396030125585 28.47721532076903 27.304549861471813\n", + " 6.123108133536956e+22 1614.3585568264868 112479962.84627353 0.007 3.8649 False 0.007 1 28.313354773527784 27.875356378312524 26.7119534651289\n", + " 8.504953218239384e+22 1734.2032668438671 114874904.73113203 0.008 3.9041 False 0.008 1 27.643694437524978 27.46206350340845 26.27499584127883\n", + " 1.139126431658312e+23 1844.1659537910111 117564219.30211641 0.009 3.9355 False 0.009 1 26.899838148339686 27.054189808173756 25.813918901177253\n", + "1.4567060673103695e+23 1939.0990786148488 120247253.09559214 0.01 3.9614 False 0.01 1 26.434795746099354 26.70402266370232 25.505200218109437\n", + "1.8204213869346178e+23 2025.816014501357 123161555.19497135 0.011 3.9823 False 0.011 1 26.08431494331709 26.515390202963896 25.460851589219537\n", + "2.2211195212106118e+23 2102.80983703774 126262726.80063727 0.012 3.9985 False 0.012 1 25.830417769249294 26.261911250846772 25.24675323138816\n", + "2.7737794679666444e+23 2191.7953438441314 129874881.47435205 0.013 4.0085 False 0.013 1 25.61017010667633 25.920886898678226 24.97583514579402\n", + " 3.348666743779798e+23 2257.3557110214474 134531865.43699867 0.014 4.0101 False 0.014 1 25.390425928750485 25.63927034298637 24.73498924387469\n", + "4.0688523367793376e+23 2321.132860663976 140257199.84967285 0.015 4.0039 False 0.015 1 25.211513303103406 25.378874895430037 24.518086721694612\n", + " ... ... ... ... ... ... ... ... ... ... ...\n", + "2.0154495387748317e+32 60158.91570534644 4647026662.010959 56.096 6.1667 True 0.014 1 11.766159725472992 11.413999520380655 11.06582056408738\n", + "2.0182359095265675e+32 60353.158050326085 4620353072.632374 56.116 6.1668 True 0.014 1 11.778659725472991 11.426499520380656 11.07832056408738\n", + " 2.021491544497126e+32 60534.08747539136 4596477771.565344 56.136 6.1756 True 0.014 1 11.78990972547299 11.437749520380654 11.089570564087376\n", + "2.0256850788490884e+32 60757.514610574115 4567464328.358439 56.157 6.1844 True 0.014 1 11.803659725472986 11.45149952038065 11.103320564087374\n", + "2.0298873125846324e+32 60967.726445724344 4540724620.555787 56.177 6.1845 True 0.014 1 11.816409725472989 11.464249520380653 11.116070564087378\n", + " 2.035972600517817e+32 61235.039172477336 4507909246.527953 56.197 6.1934 True 0.014 1 11.83215972547299 11.479999520380654 11.131820564087375\n", + "2.0430167585847428e+32 61503.52393069763 4476361627.706634 56.217 6.2024 True 0.014 1 11.84740972547299 11.495249520380654 11.147070564087379\n", + "2.0524468954776198e+32 61844.34582271275 4437365109.317572 56.237 6.2114 True 0.014 1 11.866409725472986 11.514249520380648 11.166070564087374\n", + "2.0666737776929845e+32 62287.37089440294 4389602193.657228 56.258 6.2297 True 0.014 1 11.889909725472995 11.537749520380656 11.189570564087381\n", + "2.1036424651269878e+32 63037.64788294872 4323895249.627832 56.278 6.2672 True 0.014 1 11.922659725472986 11.57049952038065 11.222320564087374\n", + "2.1760653272980036e+32 64239.1816824387 4234724526.2221713 56.298 6.3165 True 0.014 1 11.967909725472992 11.615749520380657 11.267570564087382\n", + " 2.250981517613893e+32 65463.61740672747 4147392750.6757283 56.318 6.3686 True 0.014 1 12.013159725472986 11.660999520380651 11.312820564087374\n", + "2.3279407848949313e+32 66696.03248243559 4063265158.5149813 56.339 6.4241 True 0.014 1 12.057659725472986 11.70549952038065 11.357320564087372\n", + "2.4080856466771184e+32 67967.29719504561 3979469351.1107726 56.359 6.4713 True 0.014 1 12.10290972547299 11.750749520380653 11.402570564087378\n", + "2.4909896846856737e+32 69262.79294373626 3897401645.8767776 56.379 6.5339 True 0.014 1 12.148159725472985 11.795999520380649 11.447820564087374\n", + "2.6240489308607902e+32 71367.42051224748 3767690015.125812 56.4 6.6299 True 0.014 1 12.22165972547299 11.869499520380655 11.521320564087379\n", + "Length = 1678 rows\n" + ] + } + ], + "source": [ + "# The stars in the isochrone and associated properties \n", + "# are stored in an astropy table called \"points\" \n", + "# within the IsochronePhot object. \n", + "print(my_iso.points)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OrderedDict([('REDLAW', 'H18b'), ('ATMFUNC', 'get_merged_atmosphere'), ('EVOMODEL', 'MergedPhillipsBaraffePisaEkstromParsec'), ('LOGAGE', 6.698970004336019), ('AKS', 0.8), ('DISTANCE', 4000), ('METAL_IN', 0), ('METAL_ACT', 0.0), ('WAVEMIN', 3000), ('WAVEMAX', 52000)])\n" + ] + } + ], + "source": [ + "# The isochrone table has meta keywords describing its input properties:\n", + "# REDLAW: which redlaw object was used\n", + "# ATMFUNC: atmosphere grid was used\n", + "# EVOMODEL: evolution model grid used\n", + "# LOGAGE: log(Age) of isochrone\n", + "# AKS: total extinction used\n", + "# DISTANCE: distance used\n", + "# METAL_IN: metallicity requested by user, in [M/H]\n", + "# METAL_ACT: actual metallicity of model, in [M/H] \n", + "# (only relevant if user chooses metallicity other than defined grid-points)\n", + "# WAVEMIN, WAVEMAX: the minimum and maximum wavelengths of the stellar spectra (angstroms)\n", + "print(my_iso.points.meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The columns in the isochrone table are: ['L', 'Teff', 'R', 'mass', 'logg', 'isWR', 'mass_current', 'phase', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m']\n" + ] + } + ], + "source": [ + "# See Isochrone Object documentation for column definitions\n", + "print('The columns in the isochrone table are: {0}'.format(my_iso.points.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 M_sun: F127M = 19.051 mag, F139M = 18.453 mag, F153M = 17.785 mag\n" + ] + } + ], + "source": [ + "# Example case:\n", + "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", + "idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n", + "f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n", + "f139m = np.round(my_iso.points[idx[0]]['m_hst_f139m'], decimals=3)\n", + "f153m = np.round(my_iso.points[idx[0]]['m_hst_f153m'], decimals=3)\n", + "print('1 M_sun: F127M = {0} mag, F139M = {1} mag, F153M = {2} mag'.format(f127m, f139m, f153m))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a color-magnitude diagram from the isochrone\n", + "py.figure(1, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n", + " my_iso.points['m_hst_f153m'], 'r-', label='_nolegend_')\n", + "py.plot(my_iso.points['m_hst_f127m'][idx] - my_iso.points['m_hst_f153m'][idx], \n", + " my_iso.points['m_hst_f153m'][idx], 'b*', ms=15, label='1 $M_\\odot$')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Make an Initial Mass Function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SPISEA offers a range of initial mass functions (IMFs) a user can use from to make the cluster. In addition to the parameters defining the IMF, the user can input a SPISEA multiplicity object, which defines the multiplicity properties of the population. The default multiplicity is None (e.g. all stars are single).\n", + "\n", + "Here we define a Kirkpatrick and Salpeter IMF using the Multiplicity properties defined in Lu+13 and Begbie+26. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n", + "# based on Lu+13. This means that star systems will be unresolved, i.e., \n", + "# that all components of a star system are combined into a single \"star\" in the cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "\n", + "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n", + "\n", + "# NOTE: when defining the power law slope for each segment of the IMF, we define\n", + "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", + "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", + "\n", + "massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement\n", + "powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment\n", + "my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Make the Cluster \n", + "#### Option 1: Resolved Cluster without compact objects\n", + "\n", + "Here we make a resolved cluster using the ResolvedCluster object. \n", + "\n", + "To create the cluster, the user passes in an isochrone object, and imf object, and specifies the total cluster mass. Here we will make a 10^5 M_sun cluster using the isochrone and imf we have defined. \n", + "\n", + "###### Some Notes\n", + "1. Unless an IFMR object is defined, no compact objects are included. Stars that have evolved into compact objects will be dropped from the cluster. \n", + "2. Stars generated by the IMF object that have masses below the lowest mass in the evolution model are dropped from the cluster.\n", + "2. If you wish to create a cluster with differential extinction included in the output photometry, then you can use the ResolvedClusterDiffRedden object. It is the same as ResolvedCluster, but with an additional parameter to define dAKs, which characterizes the spread of extinction within the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 30 stars out of mass range\n" + ] + } + ], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The individual stars (or star systems) in the cluster are stored in an astropy table called \"star_systems\" within the cluster object. If a multiplicity object is used, then an additional \"companions\" table is created that contains the properties of the companions to the primary star within each star system. See cluster object documentation for a description of the columns in these tables. \n", + "\n", + "If a multiplicity object is used, then the photometry in the star_systems table is the COMBINED photometry of the system; it includes the contributions from all companions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass Teff L ... metallicity m_hst_f127m m_hst_f139m m_hst_f153m N_companions\n", + "-------------------- ---------- -------------------- ------------------ ---------------------- ... ----------- ------------------ ------------------ ------------------ ------------\n", + " 0.8945070753124857 False 0.8945070753124857 4225.696580422644 2.0017761931081406e+26 ... 0.0 19.243056683615986 18.637173365678077 17.95084609292932 0\n", + " 0.9214480579938956 False 0.9214480579938956 4261.931211189814 2.116376266341599e+26 ... 0.0 19.19230771149491 18.588166309349045 17.906424455244924 0\n", + " 0.10024865234106253 False 0.10024865234106253 3027.3930442939845 8.920168492066747e+24 ... 0.0 22.126796830827757 21.771742516715804 21.121481298455976 0\n", + " 0.10331038960864716 False 0.10331038960864716 3033.2987146970418 9.299161930812652e+24 ... 0.0 22.0895560136715 21.731299701031396 21.081202065802547 0\n", + " 1.076851234267687 True 2.0546110053672764 4475.247283026874 2.915277577875829e+26 ... 0.0 18.238384604284892 17.64362975490658 16.98251770953915 1\n", + " 0.04550634271811754 False 0.04550634271811754 2907.073026736088 4.2987876632153534e+24 ... 0.0 22.86985622025449 22.578455896796655 21.922735680260743 0\n", + " 0.02057279229829472 False 0.02057279229829472 2592.6489001923687 1.0686433670147005e+24 ... 0.0 24.269977725835812 24.15885180995034 23.43422668084687 0\n", + " 0.1164879299290811 False 0.1164879299290811 3058.7163789360357 1.0930327796391296e+25 ... 0.0 21.929273688065674 21.55723615874269 20.90784257542883 0\n", + " 0.3011656908091307 False 0.3011656908091307 3381.005777927854 3.6465258290587674e+25 ... 0.0 20.77351977574311 20.262461248743314 19.595109800059674 0\n", + " 0.17103385046662112 False 0.17103385046662112 3163.96714800829 1.7678874696828524e+25 ... 0.0 21.455525612758095 21.036515054199743 20.384316470660114 0\n", + " 0.08647234805282597 False 0.08647234805282597 2980.1468856715474 8.453356916162971e+24 ... 0.0 22.159572961352055 21.832592634028575 21.17882282533773 0\n", + " 0.5438845821920302 True 0.6984912843595693 3773.589684576896 9.078463730803296e+25 ... 0.0 19.743652920498597 19.183873404619415 18.49368284927326 1\n", + " 0.06570331399723098 False 0.06570331399723098 3023.1716403002797 5.657113527948109e+24 ... 0.0 22.618813470993725 22.265343298536244 21.615534582925605 0\n", + " 0.6656753065815819 False 0.6656753065815819 3937.3176263869022 1.2365543288808776e+26 ... 0.0 19.687068904336645 19.083097570925787 18.379919030232507 0\n", + " 0.5089672130684594 False 0.5089672130684594 3729.542689305321 8.25392928841976e+25 ... 0.0 20.04904065871442 19.46279957797426 18.7680612269903 0\n", + " 0.2057786669731296 True 0.35015551288124436 3228.841994104164 2.213027864160729e+25 ... 0.0 20.676981288410097 20.249552004654582 19.595841215176918 1\n", + " ... ... ... ... ... ... ... ... ... ... ...\n", + " 0.20825958860927216 False 0.20825958860927216 3232.815912724316 2.2501626185589996e+25 ... 0.0 21.224069212493763 20.775357718194957 20.118298918902617 0\n", + " 0.14087954414741297 False 0.14087954414741297 3105.5526305019907 1.3946863955552297e+25 ... 0.0 21.688268213061907 21.292796845536593 20.644176998812117 0\n", + " 0.07469065773536471 False 0.07469065773536471 2951.534998904973 7.086107228344845e+24 ... 0.0 22.341435926066605 22.02694876760282 21.37263650179408 0\n", + " 0.6657824647823649 False 0.6657824647823649 3937.46312032627 1.2368621911866713e+26 ... 0.0 19.686839820664634 19.082861990648198 18.37968422643919 0\n", + " 1.1119472097955938 True 2.1819957339746914 4524.801113161184 3.1325420707808415e+26 ... 0.0 18.239939341376857 17.65784149495578 16.99412258354396 3\n", + " 0.20714722227773794 False 0.20714722227773794 3231.0341340446043 2.233512576122893e+25 ... 0.0 21.231135068941178 20.783288811238673 20.126380476131036 0\n", + " 0.11944545321920931 False 0.11944545321920931 3064.4210348991 1.129642123418031e+25 ... 0.0 21.893300456381454 21.518169916654795 20.868934348409407 0\n", + " 0.24800411202852105 False 0.24800411202852105 3295.9889087357333 2.8455882476473613e+25 ... 0.0 21.000088980176024 20.52252909657682 19.860623823459612 0\n", + " 0.08959718269185585 False 0.08959718269185585 2990.880928723694 8.787193864898941e+24 ... 0.0 22.120992869255 21.789321321865096 21.135885857980387 0\n", + "0.021559700900544373 False 0.021559700900544373 2620.3285372568107 1.203778611185518e+24 ... 0.0 24.15137366130518 24.017512390221583 23.303451105142077 0\n", + " 0.3654503647802998 False 0.3654503647802998 3488.358949105219 4.850834207222937e+25 ... 0.0 20.52038048863164 19.974444670523486 19.301358822464927 0\n", + "0.025273405877510015 False 0.025273405877510015 2702.510134767554 1.781147306062996e+24 ... 0.0 23.754965643144065 23.564033377191404 22.87585796983192 0\n", + " 0.24659794294040255 False 0.24659794294040255 3293.745676576533 2.824519426688407e+25 ... 0.0 21.006909892684053 20.53029949412168 19.86854728251577 0\n", + " 0.3370293445772329 True 0.6355153702785177 3440.836171686819 4.318269544740905e+25 ... 0.0 19.947508542307638 19.426118967061818 18.75711998661427 1\n", + " 0.8172248515214722 False 0.8172248515214722 4129.6093471768845 1.7156083342455475e+26 ... 0.0 19.383974807973964 18.775945032430535 18.080475905838 0\n", + " 0.01654650185255516 False 0.01654650185255516 2417.767065117449 5.58918923392165e+23 ... 0.0 24.90422715448032 24.96822186613111 24.162155489913683 0\n", + " 17.630115661052674 True 20.769088438035276 31468.54394656097 1.5193931809647468e+31 ... 0.0 12.32702290744279 11.969042567673034 11.617455775398914 1\n", + "Length = 134627 rows\n" + ] + } + ], + "source": [ + "# Look at star systems table\n", + "print(cluster.star_systems)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The cluster table contains these columns: ['mass', 'isMultiple', 'systemMass', 'Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase', 'metallicity', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m', 'N_companions']\n" + ] + } + ], + "source": [ + "print('The cluster table contains these columns: {0}'.format(cluster.star_systems.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass Teff L logg isWR ... phase metallicity m_hst_f127m m_hst_f139m m_hst_f153m \n", + "---------- -------------------- ------------------ ---------------------- ------------------ ---- ... ----- ----------- ------------------ ------------------ ------------------\n", + " 4 0.9777597710995893 4338.028423554631 2.376440000333853e+26 4.144613121985373 0.0 ... 1.0 0.0 19.086957934184962 18.486987528104535 17.81521684302705\n", + " 11 0.15460670216753916 3132.2066605872137 1.5646612433306536e+25 3.965782010650262 0.0 ... 1.0 0.0 21.575780039801344 21.16920821774943 20.518870336074574\n", + " 15 0.14437684590811475 3112.343343205245 1.437991304956611e+25 3.9627130537724344 0.0 ... 1.0 0.0 21.65960932490563 21.261309867319465 20.61225231261144\n", + " 17 0.4650204014154078 3654.3377635407696 7.089917157017561e+25 4.045880610389237 0.0 ... 1.0 0.0 20.18069477498541 19.60485943995035 18.91589665728705\n", + " 18 0.21611829149024636 3245.4039143983646 2.367792694867138e+25 3.983874304532171 0.0 ... 1.0 0.0 21.174149986384823 20.719325724071265 20.06120391766671\n", + " 24 0.019251769432724716 2545.0517403588783 8.916886525425982e+23 3.9323450965869524 0.0 ... 90.0 0.0 24.447240624904143 24.38001047476367 23.634375576570488\n", + " 27 0.061399105243014986 2998.6063816271535 5.300514940142659e+24 3.9461766893991554 0.0 ... 90.0 0.0 22.674800431423606 22.335929893121737 21.68493380391131\n", + " 28 0.07586618186354736 2946.3336034173467 7.240598634041845e+24 3.8718905600099807 0.0 ... 90.0 0.0 22.31659287751744 22.004930985326748 21.35028028555419\n", + " 29 0.011279987308020363 2047.3733076075164 1.9326117788793354e+23 3.98683579438993 0.0 ... 90.0 0.0 26.01322695703587 26.4444193135208 25.400906766358748\n", + " 33 0.030747220674507657 2780.4846621353186 2.6698057497113615e+24 3.812589174560011 0.0 ... 90.0 0.0 23.340125524685607 23.100765619185168 22.430858171279134\n", + " 41 0.1899395949467709 3200.3102116434984 2.0018990400453213e+25 3.9762321805092973 0.0 ... 1.0 0.0 21.335481404290615 20.901601265503523 20.247057833568675\n", + " 43 0.17370286184077677 3169.1166434990073 1.8008924120018483e+25 3.9714423442430293 0.0 ... 1.0 0.0 21.43712755304723 21.01616311553559 20.36367771974419\n", + " 43 0.08988731510379509 2991.8775554768927 8.818189719182378e+24 3.886842241145313 0.0 ... 1.0 0.0 22.117410812453294 21.785303698017433 21.13189927714019\n", + " 44 0.11493495166390834 3055.7208972941844 1.0738094263081045e+25 3.9537298350158117 0.0 ... 1.0 0.0 21.94816302263832 21.57774961507522 20.92827305896864\n", + " 45 0.10921744100612697 3044.6926050144025 1.0030359132375865e+25 3.9519574067118994 0.0 ... 1.0 0.0 22.01770679807904 21.653272826081725 21.003490793821804\n", + " 56 0.17921144789136326 3179.744713241384 1.8690114790913798e+25 3.9730673771279523 0.0 ... 1.0 0.0 21.399155704720734 20.974158646572544 20.32108129752664\n", + " ... ... ... ... ... ... ... ... ... ... ... ...\n", + " 134568 0.019225820797705587 2544.0290143144557 8.880303425842793e+23 3.9327836285187754 0.0 ... 90.0 0.0 24.451109451721695 24.384862166462295 23.638750638409665\n", + " 134579 0.9105591799205729 4247.109006199656 2.069295157626062e+26 4.136927642811795 0.0 ... 1.0 0.0 19.212729002183476 18.607888422031078 17.924298222857573\n", + " 134584 2.5983636084266126 11615.076492887843 1.9347372795972835e+28 4.369187004568884 0.0 ... 1.0 0.0 16.67444748487556 16.281989752966407 15.901843222779457\n", + " 134586 0.13520743874798025 3094.654090962411 1.324538155931306e+25 3.9599382688181337 0.0 ... 1.0 0.0 21.741263743980408 21.35106844597512 20.70239185675216\n", + " 134591 0.05924781160596321 2986.9173416223575 5.096124050865475e+24 3.940889766856008 0.0 ... 90.0 0.0 22.71310841542965 22.379300532008862 21.72780517170673\n", + " 134593 0.2756279064559549 3340.298843201504 3.2593715060641265e+25 4.000875813807668 0.0 ... 1.0 0.0 20.874753464501353 20.37908637910061 19.714261946333085\n", + " 134593 0.033931438676622946 2815.1226643949935 3.178566974129362e+24 3.8008714033084425 0.0 ... 90.0 0.0 23.16235232828712 22.907014765554077 22.242486764196556\n", + " 134598 0.01365547347589503 2234.768425598801 3.150602828891717e+23 4.009548757561432 0.0 ... 90.0 0.0 25.46613362656358 25.7362947160493 24.81796704530788\n", + " 134602 0.06175905869223863 3000.595002880443 5.329553967912544e+24 3.9475085171612827 0.0 ... 90.0 0.0 22.66976905273162 22.329907579832305 21.679012057011885\n", + " 134606 0.10518387013167073 3036.912401146452 9.53106844786149e+24 3.950706999740818 0.0 ... 1.0 0.0 22.066768315215985 21.706552696027686 21.056555157441903\n", + " 134607 0.06595297956077562 3024.561478938733 5.678834305674499e+24 3.9623448325505595 0.0 ... 90.0 0.0 22.615497518054603 22.261201029492877 21.611459944695717\n", + " 134608 0.05357984927260094 2954.364382789203 4.724557235607795e+24 3.9110891709993054 0.0 ... 90.0 0.0 22.783575035804013 22.46537907115991 21.812070559399526\n", + " 134614 0.05350104453859063 2953.935579562626 4.719419229341913e+24 3.9106557449622485 0.0 ... 90.0 0.0 22.784605903103657 22.466628802592354 21.813297076407856\n", + " 134614 0.8143279945366201 4126.131564052948 1.7055764580864243e+26 4.122227458611192 0.0 ... 1.0 0.0 19.389364903246538 18.78128947374045 18.08551995697676\n", + " 134614 0.20221948510388665 3223.1409276550503 2.1597535717979043e+25 3.9798436506801274 0.0 ... 1.0 0.0 21.262436522872264 20.818423227763923 20.16218144281632\n", + " 134623 0.29848602570128485 3376.6883990671927 3.602000596222681e+25 4.007183657067854 0.0 ... 1.0 0.0 20.784583849392742 20.275102250818385 19.608008027386852\n", + " 134626 3.138972776982603 13189.7781963926 3.9271090984804426e+28 4.3648125078236415 0.0 ... 1.0 0.0 16.286117145293822 15.899794361248118 15.524214462670491\n", + "Length = 36781 rows\n" + ] + } + ], + "source": [ + "# The companions table is accessed in a similar way\n", + "print(cluster.companions)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "# specify mask to only extend isochrone to brown dwarf masses\n", + "bds = iso['mass'] >= 0.01\n", + "\n", + "py.figure(2, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(clust['m_hst_f127m'] - clust['m_hst_f153m'], clust['m_hst_f153m'],\n", + " 'k.', ms=5, alpha=0.1, label='__nolegend__')\n", + "py.plot(iso['m_hst_f127m'][bds] - iso['m_hst_f153m'][bds], iso['m_hst_f153m'][bds],\n", + " 'r-', label='Isochrone')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Option 2: Resolved cluster with compact objects (white dwarfs, neutron stars, and black holes)\n", + "This is quite similar to the above, but includes compact objects. The additional piece of information required is to choose an initial-final mass relation (IFMR.)\n", + "\n", + "The output is the same as if we were making a cluster without using an IFMR. However, you can tell that compact objects are made by looking at the 'phase' keyword. Black holes have 'phase' = 103, neutron stars have 'phase' = 102, and white dwarfs have 'phase' = 101. Though not classified as a compact object, brown dwarfs have 'phase' = 90. For these compact objects (black holes, neutron stars, white dwarfs), the luminosity and temperature will return values of zero, and photometry will return nan, since we are assuming they are totally dark.\n", + "\n", + "Here, we make 4 different clusters, each of mass $10^6 M_\\odot$, by taking different combinations of age (either 100 Myr or 10 Gyr) and IMF (top-heavy or Kroupa). We then look at the different distributions of BH and WD masses. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso_young = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(),\n", + " filters=filt_list)\n", + "\n", + "my_iso_old = synthetic.IsochronePhot(10, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "massLimits = np.array([0.01, 0.05, 0.1, 0.5, 120])\n", + "powers_kroupa = np.array([-0.6, -0.25, -1.3, -2.3])\n", + "powers_theavy = np.array([-0.6, -0.25, -1.3, -1.3]) # top heavy\n", + "trunc_kroupa = imf.IMF_broken_powerlaw(massLimits, powers_kroupa)\n", + "trunc_theavy = imf.IMF_broken_powerlaw(massLimits, powers_theavy)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/caitlinbegbie/.local/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:501: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + } + ], + "source": [ + "# Make clusters \n", + "cluster_mass = 10**6\n", + "cluster_young_theavy = synthetic.ResolvedCluster(my_iso_young, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n", + "cluster_old_theavy = synthetic.ResolvedCluster(my_iso_old, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n", + "cluster_young_kroupa = synthetic.ResolvedCluster(my_iso_young, trunc_kroupa, cluster_mass, ifmr=my_ifmr)\n", + "cluster_old_kroupa = synthetic.ResolvedCluster(my_iso_old, trunc_kroupa, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get the outputs\n", + "young_theavy = cluster_young_theavy.star_systems\n", + "old_theavy = cluster_old_theavy.star_systems\n", + "young_kroupa = cluster_young_kroupa.star_systems\n", + "old_kroupa = cluster_old_kroupa.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "young_theavy_bh_idx = np.where(young_theavy['phase'] == 103)[0]\n", + "old_theavy_bh_idx = np.where(old_theavy['phase'] == 103)[0]\n", + "young_kroupa_bh_idx = np.where(young_kroupa['phase'] == 103)[0]\n", + "old_kroupa_bh_idx = np.where(old_kroupa['phase'] == 103)[0]\n", + "\n", + "bh_bins = np.linspace(5, 16, 16)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(young_theavy[young_theavy_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '100 Myr, TH IMF', color = 'red', linestyle = ':', lw = 2)\n", + "plt.hist(old_theavy[old_theavy_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '10 Gyr, TH IMF', color = 'gray', linestyle = ':', lw = 2)\n", + "plt.hist(young_kroupa[young_kroupa_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '100 Myr, Kr IMF', color = 'red', lw = 2)\n", + "plt.hist(old_kroupa[old_kroupa_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '10 Gyr, Kr IMF', color = 'gray', lw = 2)\n", + "plt.title('BH Mass Function')\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "young_theavy_wd_idx = np.where(young_theavy['phase'] == 101)[0]\n", + "old_theavy_wd_idx = np.where(old_theavy['phase'] == 101)[0]\n", + "young_kroupa_wd_idx = np.where(young_kroupa['phase'] == 101)[0]\n", + "old_kroupa_wd_idx = np.where(old_kroupa['phase'] == 101)[0]\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(young_theavy[young_theavy_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '100 Myr, TH IMF', color = 'red', linestyle = ':', lw = 2)\n", + "plt.hist(old_theavy[old_theavy_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '10 Gyr, TH IMF', color = 'gray', linestyle = ':', lw = 2)\n", + "plt.hist(young_kroupa[young_kroupa_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '100 Myr, Kr IMF', color = 'red', lw = 2)\n", + "plt.hist(old_kroupa[old_kroupa_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '10 Gyr, Kr IMF', color = 'gray', lw = 2)\n", + "plt.yscale('log')\n", + "plt.title('WD Mass Function')\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotted above are the distributions of BH and WD masses for clusters of different ages (100 Myr or 10 Gyr), with either a top-heavy or Kroupa IMF. For BHs, since those are formed relatively early on, the age of the cluster does not significantly change the mass distribution as most BHs have already formed by 100 Myr. However, the top heavy IMF allows the creation of many more massive compact objects. For WDs, both the age and IMF make significant differences in the distribution (note y-axis is logscaled)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explicitly Showing Brown Dwarfs in Simulated Clusters\n", + "\n", + "Here we are creating a cluster of log(age) = 8.4 with brown dwarfs. We show where they fall on the generated isochrone and how they incorporate into clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # Only 1 filter for plotting purposes\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8.4, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Brown Dwarfs in Simulated Isochrone\n", + "We have expanded the capabilites of isochrones to generate down to 0.005 solar masses, but masses below 0.01 solar masses are not yet supported by physical constraints. This will be expanded in later versions of the codebase." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf (mass < 0.08 M_sun): F153M = 15.849 mag\n" + ] + } + ], + "source": [ + "# specifying brown dwarfs by mass\n", + "bd_idx = np.where((my_iso.points['mass'] > 0.01) & (my_iso.points['mass'] < 0.08) )[0]\n", + "if len(bd_idx) > 0:\n", + " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", + " mass = my_iso.points[bd_idx[0]]['mass']\n", + " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", + "else:\n", + " print('No brown dwarf with mass < 0.08 M_sun found.')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a mass-magnitude diagram from the isochrone and plot brown dwarfs\n", + "py.figure(1, figsize=(6,6))\n", + "py.clf()\n", + "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", + "py.plot(my_iso.points['mass'][bd_idx], my_iso.points['m_hst_f153m'][bd_idx], 'b*', ms=15, label='BD mass')\n", + "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", + "py.xlabel('Mass')\n", + "py.ylabel('Magnitude in F153M filter')\n", + "py.gca().invert_yaxis()\n", + "py.legend()\n", + "py.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Brown Dwarfs in Simulated Clusters\n", + "\n", + "We will now use our generated isochrone to pinpoint brown dwarfs in clusters. An easy way to do this is through the 'phase' column, where brown dwarfs are assigned a value of 90. We will see how their numbers and evolution compares to compact objects." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12259 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "\n", + "# Define bins for histograms\n", + "bh_bins = np.linspace(14, 100, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(0, 30, 20)\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", + "plt.title(\"Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", + "plt.title(\"White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", + "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", + "plt.title(\"Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotted above are the number of generated compact objects (black holes, white dwarfs, and neutron stars) against the generated number of brown dwarfs." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", + "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", + "plt.title(\"Black Holes by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", + "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", + "plt.title(\"White Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", + "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", + "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", + "plt.title(\"Neutron Stars by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we have plotted the initial masses of compact objects (black holes, neutron stars, and white dwarfs) against their current masses. We have done the same for brown dwarfs. As expected, brown dwarf masses do not change over time." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/add_filters.rst b/docs/add_filters.rst index 1bbfef12..60dfde03 100644 --- a/docs/add_filters.rst +++ b/docs/add_filters.rst @@ -11,9 +11,11 @@ If the user wants to add new photometric filters to SPISEA, there are 4 main ste 3) Edit the ``get_filter_info()`` function in ``synthetic.py`` to call the new function in ``filters.py`` when the new filter string is called. - 4) Edit the ``get_obs_str()`` function in ``synthetic.py`` to - convert between column name and filter string (e.g input string - for get_filter_info) for the new filters. + 4) If needed (if converting between the ``'_'`` and ``','`` separators is not sufficient), + edit the ``get_obs_str()`` and ``get_filter_col_name()`` functions in ``synthetic.py`` to + convert between column name and filter string for the new filters. + 5) Add the filter to the ``filt_list`` in the ``test_filters()`` function in ``tests/test_models.py`` to + ensure the filter can be loaded properly. Additional documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker `_ if you'd like to diff --git a/docs/atmo_models.rst b/docs/atmo_models.rst index 4fc8cb2d..b54056bc 100644 --- a/docs/atmo_models.rst +++ b/docs/atmo_models.rst @@ -4,7 +4,7 @@ Atmosphere Model Object ======================================== Stellar atmosphere models are defined as functions in -``popstar/atmospheres.py``. These can be called by:: +``spisea/atmospheres.py``. These can be called by:: from popstar import atmospheres atmo = atmospheres. @@ -31,10 +31,107 @@ resolution of the atmosphere model grid. These are available in the uses has degraded the resolution of all atmosphere grids to R = 250 (the `spisea_cdbs.tar.gz` file). -.. figure:: images/atm_models.png - :width: 900 - :height: 196 - :align: center +.. list-table:: Atmosphere Models + :header-rows: 2 + :widths: 25 15 12 15 12 18 20 + + * - Model Name + - T\ :sub:`eff` Range (K) + - log *g* Range (cgs) + - Metallicity Range [Fe/H] + - λ Range (μm) + - Resolution\ :sup:`a` λ/Δλ + - Ref + * - ``get_merged_atmosphere`` + - 250 – 50000 + - \ :sup:`b` + - \ :sup:`b` + - \ :sup:`b` + - \ :sup:`b` + - Appendix B; Hosek et al. (2020) + * - ``get_castelli_atmosphere`` + - 3500 – 50000 + - 0 – 5.0 + - -2.5 – 0.2 + - 0.1 – 10 + - ~250 + - Castelli & Kurucz (2004) + * - ``get_phoenixv16_atmosphere`` + - 2300 – 12000 + - 0.0 – 6.0 + - -4.0 – +1.0 + - 0.05 – 5.5 + - 100,000 – 500,000 + - Husser et al. (2013) + * - ``get_BTSettl_2015_atmosphere`` + - 1200 – 7000 + - 2.5 – 5.5 + - 0 + - 0.01 – 30 + - 2000 – 700,000 + - Baraffe et al. (2015) + * - ``get_BTSettl_atmosphere``\ :sup:`d` + - 2600 – 7000 + - 4.5 – 5.5 + - -2.5 – 0.5 + - 0.1 – 6.9 + - 20,000 – 250,000 + - Allard et al. (2012b,a) + * - ``get_kurucz_atmosphere`` + - 3000 – 50000 + - 0 – 5.0 + - -5.0 – 1.0 + - 0.1 – 10 + - ~250 + - \ :sup:`c` + * - ``get_phoenix_atmosphere`` + - 2100 – 69000 + - + - -4.0 – 0.5 + - 0.001 – 995 + - ~280 + - Allard et al. (2003, 2007) + * - ``get_Phillips2020_atmosphere`` + - 200 - 3000 + - 2.5 - 5.5 + - 0 + - 0.2 - ~1980.2 + - 0.5 - 5000 + - Phillips et al. (2020) + * - ``get_Meisner2023_atmosphere`` + - 250 - 1200 + - 2.5 - 5.5 + - -1.0 - 0.3 + - 0.2 - 30 + - ~3000 + - Meisner et al. (2023) + * - ``get_wd_atmosphere``\ :sup:`e` + - – + - – + - – + - 0.1 – 3.0 + - 200 – 500,000 + - Koester (2010) + * - ``get_bb_atmosphere`` + - – + - – + - – + - – + - – + - Blackbody Spectrum + +.. rubric:: Footnotes + +:sup:`a` Resolution column reports the original resolution of the atmosphere model grid. +The default SPISEA grid degrades all atmosphere grids to R = 250 (``spisea_cdbs.tar.gz``). + +:sup:`b` See Appendix B; values depend on the underlying model selected by ``get_merged_atmosphere``. + +:sup:`c` Kurucz (1993); see CDBS documentation. + +:sup:`d` Solar metallicity only for BTSettl 2015. + +:sup:`e` White dwarf atmospheres only. Model Atmosphere Classes ------------------------- @@ -58,3 +155,7 @@ Model Atmosphere Classes .. autofunction:: atmospheres.get_phoenix_atmosphere +.. autofunction:: atmospheres.get_Phillips2020_atmosphere + +.. autofunction:: atmospheres.get_Meisner2023_atmosphere + diff --git a/docs/conf.py b/docs/conf.py index c72f260d..590e51da 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,9 @@ author = 'Matthew Hosek Jr, Jessica R. Lu, Casey Y. Lam' # The short X.Y version -version = '2.2' +version = '2.5' # The full version, including alpha/beta/rc tags -release = '2.2' +release = '2.5' diff --git a/docs/contributors.rst b/docs/contributors.rst index 714e4f94..af28f0f4 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -19,7 +19,7 @@ Dongwon Kim -- code testing/debugging Siyao Jia -- helped with early code and documentation development Natasha Abrams -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) +(ResolvedMultiplicityDK class) and added COSMIC support. Michael Medford -- developed resolved multiplicity capabilities (ResolvedMultiplicityDK class) @@ -37,8 +37,14 @@ what operating system is used Sage Hironaka Remulla -- added Rubin Observatory filters -Lingfeng Wei -- bugfix to improve creation of iso_dir in IsochronePhot +Lingfeng Wei -- bugfix to improve creation of iso_dir in +IsochronePhot, implemented faster cluster generation and test +functions for primary and companion star mass generation (v2.3), +updated random state generators (v2.4) + +Macy Huston -- bug fixes, SynthPop compatibility updates, magnitude system flexibility, +new filter sets, data set maintenance + +Anna Pusack -- Added IRTF L-band filter support -Macy Huston -- New metallicity bound + isochrone filter checks, -imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility -updates (v2.2) +Caitlin Begbie -- added brown dwarf physics and capabilities diff --git a/docs/evo_models.rst b/docs/evo_models.rst index 98d3541c..d066ae2e 100644 --- a/docs/evo_models.rst +++ b/docs/evo_models.rst @@ -14,10 +14,76 @@ The evolution object is an input for the :ref:`isochrone_objects`. Below is a table of the evolution model grids currently supported by SPISEA. -.. figure:: images/evo_models_f2.png - :width: 900 - :height: 210 - :align: center +.. list-table:: Evolution Models + :header-rows: 2 + :widths: 30 15 18 30 25 + + * - Model Name + - Mass Range + - log(Age) Range + - Metallicity Values + - Ref + * - + - M\ :sub:`⊙` + - Years + - [Fe/H] + - + * - ``MISTv1`` + - 0.10 – 300 + - 5.01 – 10.30 + - -4.0, -3.5, -3.0, -2.5, -2.0, -1.75, -1.5, -1.25, -1.0, -0.75, -0.5, -0.25, 0, 0.25, 0.5 + - v1.2; Choi et al. (2016) + * - ``MergedBaraffePisaEkstromParsec`` + - 0.08 – 120 + - 6.00 – 10.09 + - 0 + - Appendix B; Hosek et al. (2020) + * - ``MergedPhillipsBaraffePisaEkstromParsec`` + - 0.01 – 120 + - 6.00 – 10.00 + - 0 + - Appendix; Begbie et al. (2026) + * - ``Parsec`` + - 0.10 – 65 + - 6.60 – 10.12 + - 0 + - Bressan et al. (2012) + * - ``Baraffe15`` + - 0.07 – 1.4 + - 5.70 – 10.0 + - 0 + - Baraffe et al. (2015) + * - ``Ekstrom12`` + - 0.80 – 300 + - 6.00 – 8.0 + - 0 + - Ekström et al. (2012) + * - ``Pisa`` + - 0.20 – 7 + - 6.00 – 8.0 + - 0 + - Tognelli et al. (2011) + * - ``Phillips2020`` + - 0.0005–0.075 + - 6.00 - 10.00 + - 0 + - Phillips et al. (2020) + * - ``Marley2021`` + - 0.0005–0.083\ :sup:`a` + - 6.00 - 10.00 + - -0.5, 0, 0.5 + - Marley et al. (2021) + * - ``COSMIC`` :sup:`b` + - 0.08 - 150 + - - + - -2.3 - 0.18 + - Breivik et al. (2020) + +.. rubric:: Footnotes + +:sup:`a` Actual maximum value given by the Sonora models (Marley et al., 2021) relies on the age of the cluster. For example, for log(Age)=6.0, the mass range is limited to 0.0005 - 0.011 M\ :sub:`⊙`. + +:sup:`b` COSMIC evolves the stars externally and does not use SPISEA's standard isochrone-grid architecture. Instead, it uses a custom atmosphere grid that is created on the fly. See Breivik et al. (2020) for more details on COSMIC. When COSMIC is used, the IFMR is ignored. Please note the stellar mass range, age range, and metallicity values of the evolution model grid you choose: @@ -78,4 +144,12 @@ Specific Evolution Model Classes .. autoclass:: evolution.Pisa :show-inheritance: + +.. autoclass:: evolution.MergedPhillipsBaraffePisaEkstromParsec + :show-inheritance: +.. autoclass:: evolution.Phillips2020 + :show-inheritance: + +.. autoclass:: evolution.COSMIC + :show-inheritance: \ No newline at end of file diff --git a/docs/extinction.rst b/docs/extinction.rst index 6b93be6b..0dc9d172 100755 --- a/docs/extinction.rst +++ b/docs/extinction.rst @@ -36,6 +36,7 @@ Available extinction laws: * RedLawHosek18b * RedLawNoguerasLara18 * RedLawNoguerasLara20 +* RedLawSODC Extinction Law Classes @@ -90,4 +91,7 @@ Extinction Law Classes .. autoclass:: reddening.RedLawNoguerasLara20 :members: NoguerasLara20 +.. autoclass:: reddening.RedLawSODC + :members: SODC + diff --git a/docs/filters.rst b/docs/filters.rst index ab2e5418..330e990c 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -7,16 +7,16 @@ Photometric Filters The user can specify what filters are used for synthetic photometry when defining the :ref:`isochrone_objects`. Each filter is identified by a unique string, and an array of such strings -are passed into the Isochrone call. +are passed into the Isochrone call. For example:: - + # Use the HST WFC3-IR F127M and F153M filters, along with NIRC2 Kp filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f153m', 'nirc2,Kp'] my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0, evo_model=evo_model, atm_func=atm_func, red_law=red_law, filters=filt_list) - + These strings follow the format ``,``. Note that there is no space after the comma, and case matters. @@ -29,30 +29,39 @@ directories. Available filters: * 2MASS +* Bessell * CTIO_OSIRIS * DeCam * Euclid * GAIA * HAWK-I +* Hipparcos * Hubble Space Telescope +* Kepler +* IRTF * Johnson-Cousins * Johnson-Glass * JWST * Keck NIRC * Keck NIRC2 -* NACO +* NACO +* OGLE * PanStarrs 1 * Roman Space Telescope +* Subaru +* TESS +* Tycho * UKIRT * Vera C. Rubin Observatory * VISTA +* Washington * ZTF - + Filter Sets ------------ - + **2MASS** `Two-Micron Sky Survey `_ @@ -61,6 +70,13 @@ Filters: J, H, Ks Example: ``'2mass,H'`` +**Bessell** + +`Bessel (1990) `_ Johnson-Cousins UBVRI filters + +Filters: U, B, V, R, I + +Example: ``'bessell,U'`` **CTIO_OSIRIS** @@ -75,7 +91,7 @@ Example: ``'ctio_osiris,H'`` **DeCam** -`Dark Energy Camera `_ +`Dark Energy Camera `_ Filters: u, g, r, i, z, Y @@ -83,26 +99,29 @@ Example: ``'decam,r'`` **Euclid** -`Euclid (NISP) space telescope filters `_ +Euclid space telescope `NISP filters `_ +and `VIS single filter `_ -Filters: Y, J, H +Filters: VIS, Y, J, H Example: ``'euclid,Y'`` **GAIA** The `GAIA Space Telescope filters `_. -Note that three sets are available: the pre-launch passbands used in DR1 +Note that four sets are available: the pre-launch passbands used in DR1 (`Jordi+10 `_), -the passbands used for the DR2 published photometry, and -the *revised* DR2 passbands based on the DR2 data (October 2017). -ONLY THE REVISED DR2 PASSBANDS ARE SUPPORTED BY SPISEA. +the passbands used for the DR2 published photometry, +the *revised* DR2 passbands based on the DR2 data (October 2017), +and the `(E)DR3 passbands `_. Filters: G, Gbp, Grp -Example (gaia G filter from revised DR2 passbands): -``'gaia,dr2_rev,G'`` +Versions: dr1, dr2, dr2_rev, edr3 + +Example (gaia G filter from (E)DR3 passbands): +``'gaia,edr3,G'`` **HAWK-I** @@ -114,6 +133,14 @@ Filters: J, H, Ks Example: ``'hawki,J'`` +**Hipparcos** + +`Hipparcos Hp filter `_ + +Filters: Hp + +Example: ``'hipparcos,Hp'`` + **Hubble Space Telescope** HST filters are defined by their `pysynphot OBSMODE strings @@ -128,6 +155,12 @@ Example: ``'wfc3,ir,f125w'`` Johnson-Cousin filters (downloaded from http://www.aip.de/en/research/facilities/stella/instruments/data/johnson-ubvri-filter-curves). +Note: As of July 2026, the link for the source is broken. We note that the I filter here is that of +the Johnson standard filter, which has a long red tail. In the transmission curve here, it is cut off +at 1.1 microns. We recommend careful selection for a standard I filter. Note that the Bessell (1990) +filters are available as listed above. We also have specific filter profiles for many existing +instruments. + Filters: U, B, V, R, I Example: ``'ubv,B'`` @@ -145,10 +178,10 @@ Example: ``'jg,K'`` JWST NIRCam filters, downloaded from `NIRCam website `_. The filter functions in the nircam_throughputs/modAB_mean/nrc_plus_ote folder is used. -Filters: F070W, F090W, F115W, F140M, F150W, F150W2, F162M, F164N, F182M, F187N, F200W, F210M, F212N, F250M, F277W, F300M, F322W2, F323N, F335M, F356W, F360M, F405N, F410M, F430M, F444W, F460M, F466N, F470N, F480M +Filters: F070W, F090W, F115W, F140M, F150W, F150W2, F162M, F164N, F182M, F187N, F200W, F210M, F212N, F250M, F277W, F300M, F322W2, F323N, F335M, F356W, F360M, F405N, F410M, F430M, F444W, F460M, F466N, F470N, F480M Example: ``'jwst,F356W'`` - + **Keck NIRC** @@ -169,6 +202,14 @@ Filters: J, H, Hcont, K, Kp, Ks, Kcont, Lp, Ms, Brgamma, FeII Example: ``'nirc2,Ks'`` +**Kepler** + +`Kepler Kp filter `_ + +Filters: Kp + +Example: ``'kepler,Kp'`` + **NACO** @@ -179,12 +220,20 @@ IB_2.30, IB_2.33, IB_2.36 Example: ``'naco,H'`` +**OGLE** + +OGLE R-wide filter, provided by Andrzej Udalski + +Filters: Rw + +Example: ``'ogle,Rw'`` + **PanStarrs1** PanStarrs 1 filters from `Tonry et al. 2012 `_ -Filters: g, r, i, z, y +Filters: g, r, i, z, y, w Example: ``'ps1, g'`` @@ -204,11 +253,37 @@ Filters: F062, F087, F106, F129, F158, W146, F184, F213 Example: ``'roman,wfi,f062'`` +**Subaru** + +Subaru filters from the `SVO Filter Profile Service `_. + +Filters: g, r, i, z, Y, nb387, nb468, nb515, nb527, nb656, nb718, nb816, nb921, nb926, nb973 + +Instruments: hsc + +Example: ``'subaru,hsc,i'`` + +**TESS** + +TESS filter: a single wide, red-optical `bandpass `_ + +Filters: tess + +Example: ``'tess,tess'`` + +**Tycho** + +`Tycho filters `_ + +Filters: B, V + +Example: ``'tycho,B'`` + **UKIRT** `UKIRT Telescope filters `_ -Filters: J, H, K +Filters: Z, Y, J, H, K Example: ``'ukirt,K'`` @@ -229,6 +304,14 @@ Filters: Z, Y, J, H, K Example: ``'vista,Y'`` +**Washington** + +Washington filter system from `Bessell et al. 2001 `_ + +Filters: C, M, T1, T2 + +Example: ``'washington,C'`` + **ZTF** `ZTF Telescope `_ @@ -236,3 +319,11 @@ Example: ``'vista,Y'`` Filters: g, r, i Example: ``'ztf,g'`` + +**IRTF** + +`IRTF NSFCam `_ + +Filters: L + +Example: ``'nsfcam,L'`` diff --git a/docs/getting_started.rst b/docs/getting_started.rst index dd2eeb96..db20785a 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,5 +1,7 @@ .. _getting_started: +Installation +############ ========================== Install From Git @@ -98,15 +100,15 @@ STScI CDBS conventions and should be placed in the ``cdbs/grid`` directory. You will need to download 2 files: * `spisea_models.tar.gz - `_ (3.4 GB; 18 GB unzipped) + `_ (4.1 GB; 23 GB unzipped) -* `spisea_cdbs.tar.gz `_ (142 MB; 248 MB unzipped) +* `spisea_cdbs.tar.gz `_ (155 MB; 354 MB unzipped) You may **optionally** download a third file, which contains higher-resolution stellar atmospheres. Note that this file is quite large, and is not necessary for most SPISEA use cases: -* `spisea_cdbs_highres.tar.gz `_ (50 GB; 74 GB unzipped) +* `spisea_cdbs_highres.tar.gz `_ (54 GB; 80 GB unzipped) SPISEA uses the low-resolution atmospheres (R = 250) in ``spisea_cdbs.tar.gz`` by default, as diff --git a/docs/imf.rst b/docs/imf.rst index a99b8fe1..ed601775 100644 --- a/docs/imf.rst +++ b/docs/imf.rst @@ -43,3 +43,6 @@ Broken Power-Law IMFs .. autoclass:: imf.imf.Weidner_Kroupa_2004 :show-inheritance: + +.. autoclass:: imf.imf.Salpeter_Kirkpatrick_2024 + :show-inheritance: \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 11e1e44e..223f102b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -82,20 +82,62 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- +2.5 (2026-07-17) + +* *Major Changes* + * Addition of brown dwarf models (see `Begbie et al. (2026) `_) + * New evolution grids: `Phillips2020`, Marley2021, and mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec + * New atmosphere grids: Phillips2020, Meisner2023 + * get_merged_atmosphere now uses Meisner2023 for T < 1000 K and interpolated BTSettl/Meisner2023 for 1000 < T <= 1200 + * Updated evolution and atmosphere grid data sets w/ grid_version=3.0 + * Added option to evolve binary systems using `COSMIC `_ via new COSMIC evolution model. + * When COSMIC is used when creating synthetic clusters, SPISEA creates binary systems normally (using the Multiplicity obj) which are then evolved using COSMIC. + * New COSMIC evolution class, which is used with new IsochronePhotExternalEvolution obj ('external evolution' referring to fact that stellar evolution is done outside of standard SPISEA isochrone-grid framework) + * New merged atmosphere model class get_merged_atmosphere_w_bb_supplement, which reverts to blackbody atmospheres for stars with parameters outside of existing grid support. + * New tutorial for creating SPISEA clusters using COSMIC: docs/Cluster_w_COSMIC.ipynb + +* *Minor Changes* + * The MISTv1.2-synthpop model extension was modified to include denser sampling in the gap between the base MISTv1.2 grids and 0.1Msun. + * Modified default for MISTv1.2 isochrones: synthpop_extension will be True by default to keep a consistent lower mass limit of 0.1Msun across all ages and metallicities. + * Added option to return synthetic photometry in terms of AB or ST mag units in IsochronePhot. Vega mag units remains the default. New meta keyword `MAGSYS` added to output tables to specify magnitude unit system. + * Added SODC extinction law + * Filter handling improvements + * Fix bug where DECam "Y" filter was mislabeled "y", and updated DECam filters to latest version + * Add new filters for Hipparcos, Kepler, OGLE, TESS, Tycho, Washington, Subaru HSC + * Enable use of all Gaia filters with warning recommending latest (EDR3) + * All pysynphot filters can now be used + * Minor bugs and case handling + * Debug edge case for no companion stars + * Restore functionality where final mass = initial for companion stars with lower mass than the isochrone's range + * MIST evolution allows input metallicities within 0.1 dex of the allowed range, since the nearest grid value is adopted, and this eliminates floating value precision issues. + +2.4 (2026-03-20) + * Added backward compatibility for isochrone file names created + before v2.3 + * When reading isochrone from existing file, only keep user + requested filters in resulting table. + * Changed the global random seed to a random generator within each class, + but still retaining the reproducibility. Test cluster files in + test_data also updated accordingly for testing purposes + * Added filter support for IRTF L-band + * Added conversion function between ST mags and Vega mags + +2.3 (2026-02-10) + * Achieves faster cluster generation (factor of about 2x) by using + replacing ragged arrays with masked arrays when calculating + multiplicity properties + * Added new test functions (and associated test data files) ensuring + that the primary mass and companion + mass distibutions remain the same as generated with SPISEA <= v2.2 + * Added support to Euclid VIS filter + + 2.2 (2026-01-16) - * Compatibility updates for SPISEA to work with `SynthPop - `_. Updates include: - * Low mass objects below the isochrone grid can optionally be kept - in clusters (off by default) and will have - ``current_mass=initial_mass`` and ``phase=98``, with no other - evolutionary information or photometry. - * Evolution model versions are now logged in IsochronePhot files - and checked if present. - * The option ``synthpop_extension`` is now available for MISTv1 - version=1.2 evolution. This fills in the missing parameter space - down to initial mass 0.1Msun in isochrones where needed. Use of - this option will require downloading updated isochrone files. - * Added support for Euclid filters. + * Compatibility updates for SPISEA to work with `SynthPop `_. Updates include: + * Low mass objects below the isochrone grid can optionally be kept in clusters (off by default) and will have ``current_mass=initial_mass`` and ``phase=98``, with no other evolutionary information or photometry. + * Evolution model versions are now logged in IsochronePhot files and checked if present. + * The option ``synthpop_extension`` is now available for MISTv1 version=1.2 evolution. This fills in the missing parameter space down to initial mass 0.1Msun in isochrones where needed. Use of this option will require downloading updated isochrone files. + * Added support for Euclid filters. 2.1.15 (2025-10-25) * Updated Roman filter name from outdated w146 to current f146. From diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst index c4f3270e..5a7b3fb3 100644 --- a/docs/make_isochrone.rst +++ b/docs/make_isochrone.rst @@ -9,7 +9,11 @@ total extinction, and metallicity, along with the :ref:`atmo_models`, :ref:`evo_models`, and :ref:`ext_law`. If the IsochronePhot sub-class is used then synthetic photometry -will be produced. The :ref:`filters` are defined as additional inputs. +will be produced. The :ref:`filters` are defined as additional +inputs. The output photometry is in Vega mags by default (and is always +saved to the iso file in Vega mag), but the user +can opt to return the IsochronePhot object in AB or ST mags. Either way, +the magnitude system is indicated in the MAGSYS isochrone table metadata. An example of making an IsochronePhot object:: @@ -76,10 +80,9 @@ Tips and Tricks: The IsochronePhot Object * **WARNING**: When IsochronePhot checks to see if the desired isochrone table already exists, it checks all isochrone properties - except for the photometric filters (evolution models, atmosphere - models, and reddening law are encoded in the table meta-data). + (evolution models, atmosphere models, and reddening law are encoded in the table meta-data). If any of these parameters do not match, then the isochrone will - be re-calculated. + be re-calculated. However, to keep the isochrone filenames reasonable, only the age, extinction, distance, and metallicity are encoded in the @@ -87,17 +90,17 @@ Tips and Tricks: The IsochronePhot Object reddening law have changed, the original file will be overwritten by the new isochrone. - *To avoid files from being unintentially overwritten, we recommend + *To avoid files from being unintentionally overwritten, we recommend that users specify different iso_dir paths when making isochrones with different evolution models, atmosphere models, or reddening laws.* - - * **WARNING**: IsochronePhot does not check existing - isochrone tables to see if the photometric filters match - those specified by the user. *So, if the user wishes to generate an - isochrone with different filters, we recommend either using a - different iso_dir path or setting the keyword recomp=True (see - docs below).* + +* For external evolution models (i.e. COSMIC), you should use + IsochronePhotExternalEvolution + instead of IsochronePhot. This is because those evolution models do not have isochrones but + instead evolve the stars externally. The first time you run a new AKs, metallicity, or distance, + this will take ~10-20 mins because it is creating a new atmosphere grid. This table is saved in the + specified iso_dir, under the filename atm___.fits. Base Isochrone Class ---------------------------- @@ -112,3 +115,17 @@ Isochrone Sub-classes .. autoclass:: synthetic.IsochronePhot :show-inheritance: :members: make_photometry, plot_CMD, plot_mass_magnitude + +.. autoclass:: synthetic.IsochronePhotExternalEvolution + :show-inheritance: + :members: make_photometry, plot_CMD, plot_mass_magnitude + + +Photometry Conversion Functions +----------------------------- +.. _phot_conversions: + +.. autofunction:: synthetic.calc_ab_vega_filter_conversion + +.. autofunction:: synthetic.calc_st_vega_filter_conversion + diff --git a/docs/multiplicity.rst b/docs/multiplicity.rst index 69011c8c..2eb597a1 100644 --- a/docs/multiplicity.rst +++ b/docs/multiplicity.rst @@ -27,6 +27,12 @@ returned in the ``star_systems`` table off the cluster object is the same for both unresolved and resolved multiplicity classes: it represents the combined photometry of all stars within a given system. +For most selected evolution models, the multiples are evolved as single stars. +To evolve binaries (does not support higher order multiples), you should use one of the ``MultiplicityResolved`` classes +and the ``COSMIC`` evolution model. +See the example jupyter notebook `Cluster_w_COSMIC.ipynb `_ for an example. +Note that currently COSMIC due to being external evolution is significantly slower than the other evolution options. + Unresolved Multiplicity Classes ------------------------------------------ diff --git a/docs/paper_examples/Begbie+26/Figure 10.ipynb b/docs/paper_examples/Begbie+26/Figure 10.ipynb new file mode 100644 index 00000000..10180077 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 10.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 10.\n", + "\n", + "Topic: Comparing mass fraction as a function of primary mass. This shows the mass-dependent override imposed for brown dwarfs." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# multiplicity fraction vs. mass plot\n", + "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", + "\n", + "bins = np.logspace(np.log10(0.01), np.log10(10), 25)\n", + "digitized = np.digitize(masses, bins)\n", + "\n", + "mf_mean = [mf[digitized == i].mean() for i in range(1, len(bins))]\n", + "\n", + "plt.figure()\n", + "plt.plot(bins[:-1], mf_mean, marker='o', markersize=8)\n", + "plt.xscale('log')\n", + "plt.xlabel('Primary Mass [M$_\\odot$]', fontsize=16)\n", + "plt.xticks(fontsize=12)\n", + "plt.ylabel('Multiplicity Fraction', fontsize=16)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('Multiplicity Fraction vs Mass', fontsize=20)\n", + "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", + "plt.annotate('Brown Dwarf\\nRegime', xy=(0.01, 0.8), xytext=(0.025, 0.8), fontsize=14, fontweight='bold', ha='center', va='center')\n", + "plt.annotate('Stellar\\nRegime', xy=(0.01, 0.8), xytext=(0.5, 0.8), fontsize=14, fontweight='bold', ha='center', va='center')\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "#plt.savefig('multfrac.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 11.ipynb b/docs/paper_examples/Begbie+26/Figure 11.ipynb new file mode 100644 index 00000000..0f9432ee --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 11.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 10.\n", + "\n", + "Topic: Showing the brown dwarf companion count distribution to prove an enforced limit of one." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity\n", + "from matplotlib import ticker" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [], + "source": [ + "# binarity (BD primaries have ≤ 1 companion)\n", + "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", + "csf = np.array([mult.companion_star_fraction(m) for m in masses])\n", + "\n", + "rand = np.random.rand(len(masses))\n", + "is_mult = rand < mf\n", + "\n", + "n_comp = np.zeros(len(masses), dtype=int)\n", + "\n", + "# same process as in MultiplicityUnresolved\n", + "for i, m in enumerate(masses):\n", + " if not is_mult[i]:\n", + " n_comp[i] = 0\n", + " elif m <= 0.08:\n", + " n_comp[i] = 1 # hard BD limit\n", + " else:\n", + " n_comp[i] = mult.random_companion_count(1, csf[i], mf[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1bde3a03-d9c5-4762-a8be-7611f8668d1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.hist(n_comp[masses <= 0.08], bins=np.linspace(-0.5, 2.5, 4))\n", + "plt.xlabel('Number of Companions', fontsize=20)\n", + "plt.ylabel('Count', fontsize=20)\n", + "\n", + "ax = plt.gca() # get current axes\n", + "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "\n", + "plt.title('Companion Count Distribution (Brown Dwarfs)', fontsize=20)\n", + "plt.tight_layout()\n", + "#plt.savefig('bdbinarity.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 12.ipynb b/docs/paper_examples/Begbie+26/Figure 12.ipynb new file mode 100644 index 00000000..929b6a92 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 12.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 12.\n", + "\n", + "Topic: Showing the semimajor axis distribution as a function of initial mass (reflects changes induced for brown dwarfs)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity\n", + "from matplotlib import ticker" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# semimajor axis distribution\n", + "log_a = np.array([mult.log_semimajoraxis(m) for m in masses])\n", + "\n", + "plt.figure()\n", + "plt.scatter(masses, 10**log_a, s=1, alpha=0.3)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.xlabel('Primary Mass (M$_\\odot$)', fontsize=16)\n", + "plt.ylabel('Semimajor Axis (AU)', fontsize=16)\n", + "#plt.plot(bd_mass, bd_sep, 'r--', label='Fontanive+18 (approx)')\n", + "plt.annotate('Brown Dwarf\\nRegime', xy=(0.01, 100), xytext=(0.025, 100), fontsize=14, fontweight='bold', ha='center', va='center')\n", + "plt.annotate('Stellar\\nRegime', xy=(0.01, 100), xytext=(0.2, 100), fontsize=14, fontweight='bold', ha='center', va='center')\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('Semimajor Axis vs Mass', fontsize=20)\n", + "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", + "plt.legend(fontsize=14)\n", + "plt.tight_layout()\n", + "#plt.savefig('semimajoraxis.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 2.ipynb b/docs/paper_examples/Begbie+26/Figure 2.ipynb new file mode 100644 index 00000000..5589c42f --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 2.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 2.\n", + "\n", + "Topic: Introducing and comparing initial mass functions extending to the brown dwarf mass regime. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea.imf import imf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define IMFs with substellar capabilities\n", + "simf = imf.Salpeter_Kirkpatrick_2024()\n", + "oimf = imf.Weidner_Kroupa_2004()\n", + "\n", + "# Normalize the IMFs\n", + "simf.normalize(Mcl=1e4) # 10^4 Msun cluster\n", + "oimf.normalize(Mcl=1e4)\n", + "\n", + "# Define a mass grid from 0.01 to 120 M_sun\n", + "m_min = 0.01\n", + "m_max = 120\n", + "\n", + "m_grid = np.logspace(np.log10(m_min), np.log10(m_max), 2000)\n", + "\n", + "# find the dN/dm values\n", + "s_xi_vals = simf.xi(m_grid) # dN/dm\n", + "o_xi_vals = oimf.xi(m_grid)\n", + "\n", + "s_xi_logm = m_grid * s_xi_vals\n", + "o_xi_logm = m_grid * o_xi_vals" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the figure\n", + "plt.figure(figsize=(8,6))\n", + "\n", + "plt.loglog(m_grid, o_xi_vals, lw=2, ls='-.', color='black', label='Normalized Weidner_Kroupa_2004 IMF')\n", + "plt.loglog(m_grid, s_xi_vals, lw=3, color='green', label='Normalized Salpeter_Kirkpatrick_2024 IMF')\n", + "plt.axvspan(0.01, 0.08, alpha=0.3, color='tab:blue', label='Brown Dwarfs (M < 0.08 $M_\\\\odot$)')\n", + "\n", + "plt.xlabel(r'Mass $(M_\\odot)$', fontsize=16)\n", + "plt.ylabel(r'dN/dm', fontsize=16)\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('SPISEA Normalized Brown Dwarf IMFs', fontsize=20)\n", + "plt.legend(fontsize=14)\n", + "\n", + "plt.grid(alpha=0.3, which='both')\n", + "plt.tight_layout()\n", + "#plt.savefig('imf.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 3.ipynb b/docs/paper_examples/Begbie+26/Figure 3.ipynb new file mode 100644 index 00000000..049968a1 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 3.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 3.\n", + "\n", + "Topic: Showing the atmospheric interpolation between BT-Settl (pre-existing) and Meisner (new) model grids to classify brown dwarf atmospheres." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define path to atmospheric model files\n", + "cdbs_path = '/System/Volumes/Data/mnt/g/lu/models/cdbs'\n", + "logg = 4.5\n", + "\n", + "# 1000 K, plot 1\n", + "merged_file_1000 = f'{cdbs_path}/grid/merged_BTSettl_meisner/merged_T1000_g{logg}_Z0.0.fits'\n", + "bt_file_1000 = f'{cdbs_path}/grid/BTSettl_rebin/btp00/lte010-{logg}-0.0a+0.0.BT-Settl.spec.fits'\n", + "meisner_file_1000 = f'{cdbs_path}/grid/Meisner2023_rebin/mp00/spec_jwst_t1000_g{logg}_p0_kg_g1.25.fits'\n", + "\n", + "# 1100 K, plot 2\n", + "merged_file_1100 = f'{cdbs_path}/grid/merged_BTSettl_meisner/merged_T1100_g{logg}_Z0.0.fits'\n", + "bt_file_1100 = f'{cdbs_path}/grid/BTSettl_rebin/btp00/lte011-{logg}-0.0a+0.0.BT-Settl.spec.fits'\n", + "meisner_file_1100 = f'{cdbs_path}/grid/Meisner2023_rebin/mp00/spec_jwst_t1100_g{logg}_p0_kg_g1.25.fits'\n", + "\n", + "# 1200 K, plot 3\n", + "merged_file_1200 = f'{cdbs_path}/grid/merged_BTSettl_meisner/merged_T1200_g{logg}_Z0.0.fits'\n", + "bt_file_1200 = f'{cdbs_path}/grid/BTSettl_rebin/btp00/lte012-{logg}-0.0a+0.0.BT-Settl.spec.fits'\n", + "meisner_file_1200 = f'{cdbs_path}/grid/Meisner2023_rebin/mp00/spec_jwst_t1200_g{logg}_p0_kg_g1.25.fits'\n", + "\n", + "with fits.open(merged_file_1000) as h:\n", + " wave = h[1].data['Wavelength']\n", + " merged_flux_1000 = h[1].data['Flux']\n", + "\n", + "with fits.open(bt_file_1000) as h:\n", + " bt_flux_1000 = h[1].data['Flux']\n", + "\n", + "with fits.open(meisner_file_1000) as h:\n", + " meis_flux_1000 = h[1].data['Flux']\n", + "\n", + "with fits.open(merged_file_1100) as h:\n", + " merged_flux_1100 = h[1].data['Flux']\n", + "\n", + "with fits.open(bt_file_1100) as h:\n", + " bt_flux_1100 = h[1].data['Flux']\n", + "\n", + "with fits.open(meisner_file_1100) as h:\n", + " meis_flux_1100 = h[1].data['Flux']\n", + "\n", + "with fits.open(merged_file_1200) as h:\n", + " merged_flux_1200 = h[1].data['Flux']\n", + "\n", + "with fits.open(bt_file_1200) as h:\n", + " bt_flux_1200 = h[1].data['Flux']\n", + "\n", + "with fits.open(meisner_file_1200) as h:\n", + " meis_flux_1200 = h[1].data['Flux']\n", + "\n", + "wave_scaled = wave/1e4" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the figure\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,6))\n", + "\n", + "axs[0].semilogy(wave_scaled, wave * merged_flux_1000, label='Merged', lw=3, color='black')\n", + "axs[0].semilogy(wave_scaled, wave * bt_flux_1000, label='BT-Settl', lw=2, color='mediumseagreen', linestyle='--')\n", + "axs[0].semilogy(wave_scaled, wave * meis_flux_1000, label='Meisner', lw=2, color='m', linestyle=':')\n", + "\n", + "axs[0].set_xlabel('Wavelength ($10^4$ Ã…)', fontsize=20)\n", + "axs[0].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", + "axs[0].set_xlim(1, 4)\n", + "axs[0].set_ylim(1e6, 2e8)\n", + "axs[0].tick_params(axis='both', labelsize=16)\n", + "axs[0].legend(loc='upper right', fontsize=18)\n", + "axs[0].set_title(r'T$_{eff}$=1000 K', fontsize=24)\n", + "axs[0].grid()\n", + "\n", + "\n", + "axs[1].semilogy(wave_scaled, wave * merged_flux_1100, label='Merged', lw=3, color='black')\n", + "axs[1].semilogy(wave_scaled, wave * bt_flux_1100, label='BT-Settl', lw=2, color='mediumseagreen', linestyle='--')\n", + "axs[1].semilogy(wave_scaled, wave * meis_flux_1100, label='Meisner', lw=2, color='m', linestyle=':')\n", + "\n", + "axs[1].set_xlabel('Wavelength ($10^4$ Ã…)', fontsize=20)\n", + "axs[1].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", + "axs[1].set_xlim(1, 4)\n", + "axs[1].set_ylim(3e6, 3e8)\n", + "axs[1].tick_params(axis='both', labelsize=16)\n", + "axs[1].legend(loc='upper right', fontsize=18)\n", + "axs[1].set_title(r'T$_{eff}$=1100 K', fontsize=24)\n", + "axs[1].grid()\n", + "\n", + "\n", + "axs[2].semilogy(wave_scaled, wave * merged_flux_1200, label='Merged', lw=3, color='black')\n", + "axs[2].semilogy(wave_scaled, wave * bt_flux_1200, label='BT-Settl', lw=2, color='mediumseagreen', linestyle='--')\n", + "axs[2].semilogy(wave_scaled, wave * meis_flux_1200, label='Meisner', lw=2, color='m', linestyle=':')\n", + "\n", + "axs[2].set_xlabel('Wavelength ($10^4$ Ã…)', fontsize=20)\n", + "axs[2].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", + "axs[2].set_xlim(1, 4)\n", + "axs[2].set_ylim(5e6, 3e8)\n", + "axs[2].tick_params(axis='both', labelsize=16)\n", + "axs[2].legend(loc='upper right', fontsize=18)\n", + "axs[2].set_title(r'T$_{eff}$=1200 K', fontsize=24)\n", + "axs[2].grid()\n", + "\n", + "plt.suptitle('Merging BT-Settl and Meisner Atmospheric Grids from 1000 - 1200 K ($\\log g$=4.5)',\n", + " fontsize=24, fontweight='bold')\n", + "plt.tight_layout()\n", + "#plt.savefig('atmo_merge.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 4.ipynb b/docs/paper_examples/Begbie+26/Figure 4.ipynb new file mode 100644 index 00000000..de06325e --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 4.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 4.\n", + "\n", + "Topic: Showing the Gaussian Process-based interpolation between the Phillips and Pisa/Parsec Evolutionary Models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import Table" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Unpacking the merged Phillips and Pisa/PARSEC files for logage 6 and 9\n", + "evo_folder = '/System/Volumes/Data/mnt/g/lu/models/evolution/merged'\n", + "\n", + "philpisafile = evo_folder + '/phillips_pisa/z015/iso_6.00.fits'\n", + "philparsfile = evo_folder + '/phillips_parsec/z015/iso_9.00.fits'\n", + "\n", + "philpisa = Table.read(philpisafile, format='fits')\n", + "philpars = Table.read(philparsfile, format='fits')\n", + "\n", + "philpisa_m = philpisa['Mass']\n", + "philpisa_l = philpisa['L']\n", + "philpisa_t = philpisa['Teff']\n", + "philpisa_g = philpisa['logg']\n", + "philpisa_interp = np.where(philpisa['interpolated'] == True)\n", + "phil1 = philpisa['Mass'] < 0.075\n", + "pisa = philpisa['Mass'] > 0.2\n", + "\n", + "philpars_m = philpars['Mass']\n", + "philpars_l = philpars['L']\n", + "philpars_t = philpars['Teff']\n", + "philpars_g = philpars['logg']\n", + "philpars_interp = np.where(philpars['interpolated'] == True)\n", + "phil2 = philpars['Mass'] <= 0.075\n", + "parsec = philpars['Mass'] >= 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Cx5oq1BFIP336tGAenp6eMterrLpTEgXSSz7jkSNHFAqeASU/fI4ePSq4bxgrqQOHDh2q0rkp+dqxY4fcbSmisn7AHjp0iNnb26uUb926ddn+/fvV8nmFVDSQXlBQIPezyLphqGogvTKOJXXUfYaGhmzp0qVKBSIUpclAelWoR0vbMZcvX2Z16tThTafOQLo6rgW9evVir169UluZVFGdAumjR4/mXX7q1Klyeb1580Zm29Dc3JytXLlSqc+dmZnJrKyseNOHhIQo9PmmT5/Om8fw4cPLpddkG1gZhYWFgucWAPbjjz9WeDt8qlsg/ezZs3L3l7xXw4YNlSqPMi+hdsiFCxdY/fr1VcrXx8dH6jeKEHnly8jIYN27d+dNQ4H02oHGSCdqERsbi86dO+PixYsKr7NixQqsW7dOc4XiIRaL8cknn2DIkCFITk5WKY9r166hadOmOH/+fIXKkpGRgcDAQISHh1coH0lz587FggULFJ5xPDIyEp07d0ZUVJTctHFxcWjTpg1WrVqF4uJilcp38eJFNGvWDOfOnVNp/cqSmJiIAQMGCA7lUJ3k5+dj0KBBWL16tcKTEIaEhCg0VEVxcTEGDhyIv//+W+HynDp1CkFBQcjNzVV4nermzJkz+Oijj5CXl6fUehcuXEDTpk1VHkaguLgYa9euhb+/P169eqVSHqVWrlyJr776SqHrSVxcHHr16oW0tDTcvXsXPXv2RHx8vELbuXTpklqGRdGk1NRUweVWVlZK51md6s7q5tixYwgODsbbt28VSv/u3Tt8+OGHcse6HzduHPbu3auOIlYbs2fPxoABA/D69WuV1n/z5g0GDRqEmTNnqrlk6iXvHAcAS0tLtW2vuhxL+fn5mD9/PmbMmKHtoiisKtWj9+/fR58+fRQ6vqqKkydPokuXLir/TqqN+K5vK1euLPfe+vXrZbYNx40bB3Nzc6W2a2ZmhokTJ/Iu/+uvv+TmUVxcLPh7Ryh/Pqq2gZV169YtwXNLX18fEyZM0GgZqosHDx5Uu2tRqSVLlqB79+7lJg1X1OPHj9G2bVvs2rWrQuUoKirCwIEDq3wcg2geBdKJWixatAiRkZFKr/fll18iKytLAyXi9/nnn2Pt2rUVzicjIwMDBgxAWFiYynnMnDkTDx8+rHBZSt24cQM//vij0uslJCTgo48+EgyWZWRkoE+fPrh//35FiggASEtLw8CBA9V6A0ETGGMYNWoUXr58qe2iVNi+fftUmgxxz549uHz5smCa77//HsePH1c67xs3buDPP/9Uer3qICUlBcOHD1f4hlapBw8eYMCAAQoH/4Q8fPgQ77//vtwJ3IR89tlnSqV//fo1Jk2ahAEDBih9k0SRY02bzpw5I7jcwcFB6TyrU91Z3QwaNAiFhYVKrVNYWCh4zb9x4wZ2796tjuJVG4sWLcLvv/+ulrxWrlyJBQsWqCUvTZB3jltYWKhtHpXqeCytXr26Wky4WpXq0fz8fAwdOhSZmZkVLktle/HiBUaNGqXtYlQb/fv3h7u7e7n3//vvP6nOSkVFRTKD2zo6Ovj0009V2vaMGTN45/H6999/5d4EPXv2LO9NEw8PDwQEBChVHlXbwKq4efOm4PJmzZpV+nwPVdUXX3yBgoICbRdDaStWrMC3334rdx4NefLz8zF69GicPHlS5TyWLl1KQXQCgCYbJWoieWGzsrJChw4dYGpqitDQUME7hxkZGdi9e7dKd7pVsXv3brkzpDs5OcHLywt169ZFQkIC7t+/j3fv3slMm5mZieHDh+P+/fsqTYKkTC9EReTn53N/6+npoV27dnBxcUFaWhquXr0q2JC/c+cOfvvtN3z11Vcyl0+dOhUPHjwQ3L6LiwtatWoFAwMDREZG4t69e7xpMzMzMWDAAERERMDQ0FD4g1WQoaEhGjZsCFdXV5iamsLExAT5+flITk7G48ePBXvc5OfnY9myZdW+B6jkOWpgYIAOHTrA3t4eUVFRuH37tuC6a9euRadOnWQue/z4MZYsWSK4vkgkQqtWrdCwYUNkZmbi6tWrSE9PB4Aa2yP90aNHSq9TUFCAAQMGyP3B/t5778HLywuFhYUICwsTvNHz8OFDTJkyReUeGKVPLxgaGqJz586wtbVFeHg4Hj9+zLvO/v37pf53cnJC27ZtwRjDxYsXkZaWxruu0LGmTdeuXcOOHTsE0yj7QxOoPnVndSRZHzZp0gReXl4oKChAaGio4JMSGRkZmDJlCk6cOFFuWdljuyxvb280btwY5ubmyM3Nxbt37/DixQvExMSo/BSXNp05cwaLFy8WTGNkZIT27dvD3t6em9w8JyeHN/3333+Pjh07IjAwUN3FrZA3b97IDfKrco7z0daxpKurC1dXV9SvXx8WFhYwMzNDUVER0tPT8ezZMzx//lxw/cWLF2PIkCFqm6xe3apaPZqUlISkpCSl11MnGxsbeHh4wNbWFmZmZtDT00NWVhbi4uLw4MEDqWtlWadOncL169fRrl27Sixx9aSrq4vp06djzpw5Uu8zxvDnn39yvz/3798vsw56//330bBhQ5U6FLi4uGDw4MEyj9HCwkJs2LAB33zzDe/6Qse2Kr25VWkDq0peZ4Q2bdpUUkmqtvT0dMEndMzNzdGqVSs4ODhAR0cHGRkZSE5OxtOnT7nfbNpw9epVzJ49WzCNra0tfHx84OjoiOTkZDx8+BBv3ryRmbaoqAgjR45EVFSUSk+YqTt2Q6oxrQ4sQyqVJsdIL33NmTNHaiKm4uJitmDBAsF1PvroI6nt5ObmsujoaO4VHBzMu25wcLBU2rKvwsJCLt/8/HzBcbWcnJzY4cOHy40BmZGRwb799lvBcaXXrFmj0j6XfNnY2LAhQ4awL774gs2aNYsNGTKEOTs7KzVGeumre/fuLDY2Vmq9rKwsNmnSJMH1HBwcWH5+frnPce/ePcHPb2RkxLZt21Zuvdu3b7OGDRsKbnPFihUq7Tuh8d/GjBnDWrRowX755Rd2584dueN6Xrp0ifn6+gqOdVZUVCS1TmJiInecXb58WbCsly9f5j1G37x5U648mhgjvfQVGBhYbozX48ePM0NDQ8Hjgg/fZEmlr8aNG7Pw8HCpdfLy8ticOXPkllWdY6RnZmZK7fc2bdrwbnfmzJkyv6uykxEqci6Wvvz8/NikSZPY3Llz2eTJk1lgYCAzNDSUGh/yjz/+EMyjfv367MaNG+U+265du5ixsTHveiKRiHdcQEXGz/X392dxcXHcOmKxmE2bNk2hzz1//nypcyclJYV5eXmpdKxVhCr1X15eHnv8+DFbuHCh4P4FSiYqfv78ucxtV1bdKYnGSP/fNeT69etS64nFYrZ27Vq54/7fv3+/3DY/+OADmWnNzc0Fx6HNz89nV69eZcuWLWOdOnViOjo6ahsjvbCwUOo6NXPmTN7P1KZNG8E2k+SkoGKxmLVo0UJwH3388cflJqBLT09no0aNElyvRYsWTCwWq+XzS1J2jPTCwkL26tUrtmHDBlavXj25x9PWrVtlbleVMdIr61jasmULq1evHvv666/ZuXPn5E78GhUVxQYOHCi4H0JDQwXzUIY62lKZmZlcflW1Hi19NWzYkI0dO5bNnTuXTZ06lfXt25eZm5urdYx0Q0NDNnToUPb333/LHNNfUnZ2Nlu5ciXvhOAA2LRp09RWNmVUtzHSGSuZxNHU1FTmeZ2RkcEYY6xt27Yy8zh79myFPvedO3d416lXr1653zGlcnNzmYWFhcz19PX12evXr2Wup+42sKr4rqWlrx9++KHC2xBSXcZIDwsL400/dOhQwfkgYmNj2c6dO9mECROYnZ1duTHSNdUOYYyx9u3b8+ZlYWHBtm3bVi52kZeXx/744w/B37ZfffUV7+dV9Lg2Nzdn/fv3Z59//jmbM2cOGzZsGGvUqBGNkV5LUCC9FtF0IH3y5Mm867Zs2ZJ3vcaNGwtuV9VJnMr6+++/BS+Ez549E1z/iy++4F2/bdu2MtdRJJAuEonY4sWLZf64EYvF7MmTJ1LvyWu4eHt7C/5QktfgkDUhmLwA7c6dO3m39/z5c2ZiYsK7Ll+wtCKB9Ldv3/Iu4xMTEyNzRvnS1+3bt3nXrUhZZdFUIL1p06a8x8bs2bMF1y0bRGaspPEtNImQubm54GRV8oLw6gykl1WRBmwpRX5EuLi4sPPnz8tcPyMjQ+qGl9D3bmxsLHiN2rNnj2A5+K6V8n6wWVhYyPwRFR8fL/ez8wV6ha7FfMdaRSlzU1OV15QpU3i3rY26kwLpJRMkRkRE8K7/+++/C64/ffr0cusEBQXJTNuvXz/e7ciSkJCgsYn8VN2fZcnbv0FBQbzBcLFYzHr37q3ysaQqRW5aqfry8/OT6pwhSZV2amUdS+np6UpPElpUVMQaN27M+5l+/fVXpfJTlDraUlWxHgXArKys2D///CNz/dzcXN4bsapQpQ38/fffCx772lAdA+mMMTZ16lSZaf744w928+ZNmct8fX3V8rmFynjo0CGZ6+zbt493neDgYN5tqbsNrCp5EwrzdXZTl+oSSL9+/Tpv+jt37ij8eYuKigR/EzOmvnbIlStXePPR0dEp11GirNWrV/Ou7+joyNuGkXdcA2BTp05l6enpMtd/9OiRwp+RVF9V87k8Uu0YGxsLjs3duXNn3mWVNZHNf//9x7ts+PDhaNSokeD6H3/8Me+yW7duISUlRaVy/f7771iwYAGMjIzKLROJRPDy8lIqv4ULF8rMq9TSpUsF15f1yNKpU6d40zdt2lRw3zRo0ACTJk3iXf7y5UtEREQIlklZderUUXodNzc3ODo68i5/8uRJRYpUJfz000+8x4bQOQrIPk9v3bolOInQtGnTUK9ePd7lS5cuVWlIpOrC0tISly9fRteuXWUuNzc3h4uLCwAgIiJC8NHySZMmCV6jhg4dimbNmvEuFzqHhUycOBF2dnbl3ndycpL5vqT58+fLfF/e0C3VbXIzHx8fLFu2TKV1q0PdWV2NHTsWjRs35l3+6aefCo5rL6su5Et/8+ZNPHv2TOGyOTo6Cl4bqwJ5Y4j++OOPEIlEMpeJRCL88MMPFcq/KjE3N8eWLVvUWl9V1rFkaWmp9DAsurq6aNWqFe/yqtoeqqr1qL6+Pk6dOoVBgwbJXG5kZIQGDRqolLcsqrSB27Zty7tM3W30mm7GjBkyr41//vknli9fLnMddU3ELDQEBt+ko0LDulRk+Dhl2sCaxFdP1TZC7Z1du3YpPP64rq4uWrZsqa5iCRKK3QQGBgpetwDh2E1iYqLgELRCZs6cidWrV/MODePj46NSvqR6oUA6UYvAwEBYWVnxLndycuJdVpGJ8JQhNKbV2rVrIRKJBF/NmzfnXV8sFqs0YZyfn5/aGk9ASeXWr18/wTS+vr6CDfYbN25I/f/06VPB8R0HDhwot1zy0ly6dEluHqoQi8UIDQ3FkiVLMGTIELRo0QKOjo6wtLSEnp5eue9YaNzc6jjDuaQ6deqgZ8+evMuFzlFA9nkaGhoquM6AAQMEl9vb26N9+/aCaaqzb775RubEU7KEhIQILq/oeZaUlKTSNapPnz68y4Qa5fXq1YOfn5/MZfb29oLbrKw6QR2aN2+O8+fPw9raWqX1q0PdWV3JO2f09PQEj+9Hjx6Vm1eEb4zs5ORk+Pj4oHPnzvj000+xfPlyHDt2DJGRkZUy2ZomCF2T3N3dBQOOQMlN9vr16/Mu11S9r25169bFiRMnBAPLqtDGsfT8+XOsWrUKY8aMQYcOHVCvXj1YW1vD0NCwXHto586dvPlU1fZQVa1HJ02aBH9/f6XXq6jMzEwcOnQIM2fORO/eveHp6Ym6devC1NQUOjo6Ut+3UPuwqKiI6hslNG7cGEFBQeXej4qKkjlZb506dTBixAi1bLtv377w9PSUuezMmTNSk54CwLt372TOBwKUdDASOi7kUaYNXBHyJoAWmpenNnF3d4erq6vMZb/99htcXV3x0Ucf4dtvv8X27dtx/fp1tUzYXBFCsZuTJ0/Kjd3Ia5urclPY1tZWsAMMqT1qbldAUqnk3Zk0NzfnXVYZE3CJxWKNT/Yjb0Z0WUaOHKnWO+UNGzaU26AASgL4L168kLksMTFR6v+EhATBvJo0aSJ3e++9957gcnnbUBZjDNu2bcOSJUvkTpylKL4JZ6uLZs2aCfZKEzpHAcj8AS/vmPf19ZVbLj8/v2oTUFHWqFGjFE5bWecZ3w8sPkK9KkxNTVVaT97kwtUh8FinTh18+eWXmD17doV6qVb1urM6k3c+AOC92QOU1COvX7+W+g6GDh2KBQsWIC4urlz6oqIiXL58udxEcXp6enjvvffQoUMH9OnTB4GBgdDV1VXik2iH0DVJkesRUPIdRPNMmqvuel/dDAwMMHLkSPz000+wsbFRe/6VeSxdvnwZ3377rdxAs6KqanuoqtajyrQF1OHNmzdYsmQJ1q9fL/jUoDLevXsHCwsLteRVG8ycOVPhp24mTZoEY2NjtWxXJBJh1qxZ+OSTT8otY4xhzZo1+O2337j39u/fzzvZ7Lhx4yo0qXBlHffyOgJV9bqmMs2ePZu3A198fDz27t1b7v26deuiXbt2CAgIwODBgyv1aTqhDm7qoErsZvDgwYJP/pPag3qkE7WoW7eu4HJ9ff1KKolsb9++VfiRJVWp0ltD3TOJK/pjT+ixz7I9jfhmvVZmm1ZWVoKNMXnbUEZBQQGGDRuGsWPHqi2IDlT/oJUmzlGhWdx1dXVhZmYmNw+h3rjVmbu7u9yhTyQJnQM6OjoK7Sd5j3Orcp4J5WlgYMC7TNUe2lWVoaEhmjdvjvHjx2P//v1ITEzEV199VeGhHuSdlzV56CNNU6RuknfOlK0PTUxMcODAAaWuW0VFRQgLC8OqVavQp08f1KtXD5s3b1Z4fW0Rul6oo62hznpfHXR0dODh4YEhQ4Zg9erVSExMxMaNGzUSRAcq71havnw5unTporYgOlB120NVsR7V19cXfKJV3R48eICWLVti5cqVaguiA1X3O6+qgoKCFBqaU09PD1OnTlXrtkePHs173dq6dStyc3O5//mGddHV1cW4ceNULoOybeCK8PDwEFx+8+bNSilHdTB9+nSMHDlSqXXevHmDI0eOYPbs2XB3d8egQYPw6tUrDZWw/LY1qSrEbkj1RYF0ohby7sxV5I62OjDGquQ2KquRUZt8++23Mu+o13aaOEeFehYXFxcrdPOqoKBA6e1WBzXl3BYKlguR1+u8qmnTpg2io6OlXjExMUhMTER2djby8vJw9+5dbNy4EcHBwSrvl7LknZfVoedybePv74/Hjx9j3LhxCj0BVlZiYiLGjx+Pr7/+WgOlI0LKnuPR0dGIj49HRkYGioqKEBkZib1792Lq1KkqjTOtLE0fSydOnMDnn3+u8Y4khJ+1tXWldSbKzs7GwIEDERsbWynbI/xEIhE+/fRTuek+/PBDtffwNTY2ltkjHSi5OVw6vExiYiLv0Bm9evWq0PjlldkGlhfYvHfvntaHKFGWvGu2qjfJdHR0sH37duzatUuhp/ZklevAgQNo27Yt79Nm6qTp+A3FbkhFUCCd1Ao2NjaCgcL169eDMVah15gxY5Qul7oe5SulaENBaHzLsj8e5fWYVGSb6enpgo0CedtQVHJystQji2XZ29tjxYoVePz4MbKzs8t9h25ubmopR20hr6eeIsMpyXqsvSZQ9twWOgfEYrFg7/9S8satVdd5VhMZGRnB3d1d6uXm5gYHBweVAlxE+xSpm+SdM3zBVEdHR2zatAlJSUn4999/8dlnn6Fr165wdnZWeLi2n3/+Gbdv31YorTYIXS/U0dbQxvWo7Dnu7u4OJycnmJuba21COk0eS1999RXvOvr6+vjiiy9w8+ZNpKeno7i4WKo9NHr0aJU/k7ZUxXpU3e18IevWrRN8ErNr1644fPgw4uPjUVBQIPV9X7hwodLKWVuMHj1a7lMQ6pwnS9L06dN5OzSUTjq6Z88e3t9mFZlkFKjc497f31/wKciCgoJq8RSYpMLCQsHlFe2pPWzYMNy/fx/h4eH4/fffMWTIEDRp0kTh9m5iYiJmzJhRoTIoQuh6O2/evArHbhYtWqR0mSrz2CZVGwXSSa2gq6sreAfx+vXrlVgazXn+/DlycnLkpnv48CHvMkdHR6n/5Y09d//+fbnbk5em7DZVdfjwYd7HTw0MDHDlyhXMnDkT3t7e5RoLjLFq12NB2xo1aiS4/NatW4LLGWO4du2aOotUbVWn84yQ6uDBgwdy0wjVhSKRSO7EuObm5hg4cCCWL1+O8+fPIy4uDjk5OYiIiMDRo0cxb9482NraylyXMYbt27fLLaO2CF2TFLkeyUtH1yNp6j6Wnj17JngObNq0CT///DP8/f1haWlZrrNJSkpKxT9UJavt9eiBAwd4l3Xv3h3nzp1D//794eTkVK6XfHX8vqs6U1NTjB8/nnd5q1at0L59e41s297eHh9//LHMZbdv38bt27exe/dumcsdHR3x/vvva6RcmqCvr48hQ4YIpvntt9+q3DEu9MRh2YnOy7p7965aytCkSRPMmjULe/fuRXh4OLKzs5GUlIQbN25g/fr1MifNLXX8+HGN71Oha3pNid2Q6osC6aTKE6po+CZIkSUgIIB32b///iu3Fwqf58+fa3wyDEUVFxfj6NGjgmkePnzIO9EoALRt21bqf09PTzg4OPCm//fff+WW6+DBg4LLO3fuLDcPRTx79ox3WbNmzQQDv9evX0dWVpZK25U3/IIyx2l1Iu97k9cD5Pjx45U2zl5VJ29fKnKeHTp0iHeZvb290hOkEVKdyat3ioqKcPz4cd7lvr6+cidhlsXIyAiNGzdG3759sXTpUsFthIaGKp2/POpqMwldk2JiYhAWFia4/r179xATE6NS/qRERY4lofYQAAwaNIh3WU5ODq5evapcYauA2l6PCn3nwcHBgk84nD59WhNFqvWmT5/Oe03WVG/0Up9//jnvsi+++IK3/hkzZky1m5/ls88+E3zy/PXr1xg/frzKY/1fuXJFrXMOABCcvFfe8EyaHMLU3t4ebdq0wcSJE3Hy5En07t1bZjrGGO7cuSNzWWXEbkJCQuTWc3ySkpIQGRmp0rqElKJAOqnyhH7IPn78WOF8+vTpw7vs3bt3mDJlilLjSEZGRmL8+PHw8vJS+UKuCYsXLxas7L/55hvB9bt06SL1v0gkQmBgIG/6+/fv805WA5SMSbphwwbe5a6urvD29hYsk6KEHtt9+fIl71ho+fn5gg1OeeQFW5Q5TqsTV1dXNGnShHf50aNHsWPHDpnLYmNjMW3aNE0Vrdrx9vaGq6sr7/INGzYgKiqKd/nevXtx79493uVBQUFaG7qAEG3YsmULnj59yrv8zz//xOvXr3mXl60LgZJe7o8ePVKqHEKBN1Vv4AsRqo+ioqLkPjJeqlevXoLL586dy1unMsYwb968CuVf02n6WJI3jInQ+Lbz589XaBiUqqa216NC35nQ9x0aGoqtW7eqv0AE7u7u6N+/f7n3HRwc5Pairig/Pz/e3298Y6OLRCLBXvRVlZeXFyZMmCCY5siRIxg0aJBCT26XKp2HonPnzmoPpAsNW3L16lXe7e3du1fl3tiHDx+WmmxWEaq0YdTVDhGK3YjFYowbN06p7yU+Ph6zZ89GgwYN6IloUmEUSCdVnlBv6Pv37+Pzzz/HzZs38eLFC8TExHCvsg3KIUOGCE6c8s8//yAoKEiwER0ZGYm1a9eiQ4cOaNy4MTZv3oyioiJlP5JGPXnyBH379i3XSz47OxtTpkzB4cOHedd1cHBAv379yr3/2WefCf54GD9+vMyA6Z07d9CzZ0/BRstnn33Gu0xZfI89AyW9EWbNmlXuTnhUVBQCAwMrNKu7hYWF4JhpX375JQ4cOICIiAipY1Sot151Ie/7GzNmDMaOHYszZ84gMjISd+7cwS+//IIWLVpQb/QyZs2axbssNzeX9zjds2cPxo4dy7uuSCTSeM8nQqqa/Px8BAUFlTtnGGNYt24dvvzyS8H1ZY0Re/XqVfj5+aF169ZYvHix3F5qmZmZguOIyhs/VxVCbaaUlBSMGzcOly9fxvPnz6XqorKPaHfp0gXNmzfnzevUqVMYOXIk0tLSpN7PyMjA2LFjceLECd51mzVrJvNGRW2i6WNJqD0EAFOmTEFycrLUe+np6Zg2bRpWrFght/xVVW2uR4W+89WrV+PcuXNS7zHGuN8+iga21C0vL69cu5jvmlQW33pVrW39119/4fLly1Kvixcvqm3SciHKdhLq1q0bGjZsqKHSaNZPP/2E+vXrC6Y5dOgQPD09sXHjRt4bT7m5uTh37hxGjhwJd3d3bN68WSOTXgrVr2/fvsXkyZNRUFAg9f7OnTsFr1PyTJ48GQ4ODhg1ahR27Nght+d7SEgItm3bxrucrw2jrnZIp06d0KpVK968rly5gg4dOuDy5cu8aV6+fInt27cjKCgIrq6u+P3335W+mUCILNXruR1SKwldQAFg+fLlWL58ebn3Fy5cKDWJhJGREZYsWSI4KejZs2fRvHlzNGjQAL6+vrCwsEBmZibevn2LiIiIajOG9rlz51C/fn20a9cOzs7OSEtLw9WrV+WOuTZz5kyZDbvmzZtj6NCh3EzvZeXl5WHUqFGYP38+WrVqBQMDA0RGRsp99NvV1RVTp05V/IPJIW/m9j/++AO7d+9Gy5YtYWVlhRcvXuD27dsqP+pXSkdHBy1atOB9FDoqKor3MWpNz0iuacOHD8eKFSt4xxUVi8XYunUr9XZSwCeffIIVK1bg5cuXMpdHR0ejbdu2aNq0KRo3boyioiLcuXOHN32pwYMHo0WLFpooMiFV2suXL7lzxsvLC/n5+QgNDZU7HFtQUJDg0zalY8wuWrQIOjo6cHNzQ/369WFpaQlTU1MUFRUhPj4ed+7cEbyR/N5776n82fjIazP9/fff+Pvvv8u9P3r0aKnrtEgkwg8//CDYc3znzp34999/0aFDB9jZ2SElJQVXrlyR2+Nv2bJlVbpnb2XS1LHUqlUr6Orq8rZvLl++DHd3d7Rt2xYODg548+YNrl27plRvzaqoNtejbdq04R2aJicnBz169ECTJk3g6ekJsViM27dva71Dw40bN9C1a1eV1hUKmlaltrWDg4NgYFGTgoKC4OfnJzgfiKSKTjKqTVZWVjh06BACAgIEn86Ij4/HxIkT8cknn6Bp06bchNNpaWl4/fo1Hj58WC6ArQndu3cXXL59+3acPn0aHTp0AFBSV8i7TikiIyMDO3bs4DrAmZubw9PTE/b29jA3N4eBgQHevXuHR48eCU5eLBKJ4OvrK3OZOtshv/zyi+A14u7du+jcuTOcnJzQrFkzWFtbIycnBykpKYiKikJiYqJgWQhRFQXSSZXXqVMn1K1bt8IzVAMlF+jr169j3bp1gulevHghOI54VWVoaMj1uC4sLMSlS5cUXrdly5aYPXs27/K1a9fKfRQ5NjZW7t3tUqampjh48CDvrPKqCAwMhJOTExISEnjTJCcny+wpZ29vj8LCQpUftQ8ODq6WY4pWlIGBAXbt2oXWrVsrfYdfR0cHbdu2pcfr/p+hoSEOHjyIzp07C47XHx4ejvDwcIXy9Pb2lnu9I6QmkqwPlTlnLCwslDpnxGIxoqOjBYdO4CN0Y19VjRo1QtOmTRX+vEKCgoIwf/58LF26lDdNbm4uzp49q3Cec+fO5R1ztbZT57FUp04d9O/fX3CugNzcXFy4cKHc+zo6OvD09ERERITS5dC22lyPjh07VnCMd6DkSV5ZHR98fHxq7DCEtd2sWbMUGq7FxsYGAwYM0HyBNKhJkyY4c+YM+vbtKzh0GwDuJhrfON+a5unpia5du8q8BpdKSkqSOYmwSCRS282izMxMlfZB9+7deYfSUmc7pEuXLvjhhx8wd+5cwXQJCQmCv/8JUTca2oVUeYaGhliwYIHa8vvrr7/kjqNWXbVt21alcaednJywZ88e6Ovr86axtLTEf//9x3v3WRlWVlb4999/1d67x9jYGH/88YfSPd0MDQ2xb98+lSaWKzV+/Hi5jxTWVL6+vvjvv/9gZmam8Do6OjrYuHEjevbsyZumNvZYbN68OQ4ePAhra+sK5+Xt7Y3//vtPI8NHEFLVbdu2TXDyMVn09fWxbds2uLm5aahU/zNt2jR07NhRI3l///33astryZIlgkOKKGPq1KmCQXmiGr5j6ZdfflHp+v/DDz/IfcKvKqut9Wj//v1lDs8oT8uWLfHLL79ooESkKhg+fDjs7e3lphs1apRaOzdpS6tWrXDr1q1qMaH18uXLlR7ix9jYGKtXr9ZQiRRjYWGBNWvWCKZRZzvk66+/xsKFC5Vu0xGiSXQ0kmph+vTp+Pbbb9VyAdXR0cGGDRuwf/9+hRoWQoyMjBAcHIxGjRpVuFzq8ueff+Lrr79WOAjp4eGBS5cuKfQZXF1dERoaiilTpgjOyC2kU6dOCAsLE5zAtCIGDRqEVatWKTzjvLm5OQ4fPlzhBpeFhQWOHz+utolTq5suXbrgypUr8Pf3l5vWzc0Np0+fxtixY5GRkcGbztTUVJ1FrDZ69OiBe/fuqTyGsI6ODiZMmIDbt2/D3d1drWUjpLoYOnQo9u3bp/ANPgsLC/z7778a75Gnq6uLefPmYeXKlRrbRr9+/bB27VoYGRmpJb8//vgD+/fvF5wcTYiNjQ327NmD1atX18obpJoi71hq2LAhjh07Jne89FIikQhLliyRO39AdVBb69Hdu3ejR48eCqdv3749Tp06BRMTEw2WimiToaGhQp2salInM1dXV1y8eBFbtmyp8Pmrq6uLfv36Cc6FpaqmTZtix44dCgfT69Wrh1OnTmn1qa7GjRvj2rVrcuMG6m6HLFq0COfPn6/wGP56enro1asXWrZsqZZykdqLAumk2vjuu+8QERGB+fPno0uXLnB0dKxQpRYcHIxXr15hx44d6NKli8J5NWzYEOPHj8euXbvw+vVr7N+/X3AS08pWOq7phQsX0KNHD96At729PRYtWoT79+8rVSkZGxtjzZo1ePbsGT777DPex7okWVpaIjg4GCEhIbh06ZLGf5RMnToVV69eFQzWm5mZYfTo0Xjw4AGCgoLUsl1vb2/cu3cPe/bswccffwxvb29YWVmpfNOhumnatClu3LiBw4cPY/jw4fDw8ICZmRnMzMzg4eGB4OBg7Nu3DxEREdzYgEKTQtnZ2VVSyaseV1dXXLhwAVevXsXQoUMV6lnn4uKC6dOnIzIyEhs2bKAfxqTWCw4Oxv379zFy5EjegLqJiQlGjRqFiIgI9O3bVzC/8ePH49KlS1i4cCG6d++OOnXqKFwWV1dXTJs2DREREVi6dKnGe1ZNnjwZUVFRWLp0KQIDA+Hi4lKhm5Olbab169ejTZs2cm9W6+rqolWrVvjrr78QGxuLoUOHqrztmqiyjqUOHTrg/v37GD9+PG9vU11dXQQGBiIkJATz589X+rNUVbWxHjU1NcWpU6ewatUqwSdrPDw88OuvvyIkJAQ2NjaVWEKiDZ988ong79wOHTrAx8enEkukeSKRCGPGjEFUVBSOHj2KESNGKHwz2MjICIGBgfjtt9/w8uVLHDlyRGO99YcMGYJr164J3gCzs7PDl19+icePH6NTp04qb+vOnTvYunUrRo8eDR8fH4V/nxoYGKB79+7YunUrHj58qPDT6epuhwQEBCAyMhKHDh1Cnz59FH6S3NnZGcOHD8emTZuQkJCAEydOaGSOGlK7iFhVmo2DEC0qLCxEeHg4oqKikJaWhvT0dIhEIpibm8PKygqNGjWCl5cXLC0ttVrORYsWYfHixTKXBQQE4OLFi1LvvX79GtevX0d8fDwyMjJgZ2eHxo0bo3379mr7IR8XF4c7d+7gzZs3SEtLQ1FREaysrFCnTh14e3vDz89Pa49jJSYm4urVq4iNjUVeXh7s7Ozg4uKCTp06VbsfSDVNXl4enJ2decelHzVqlOBs8bUJYwyPHz/Go0ePkJqairS0NOjq6sLa2hq2trZo0aJFpQxHQUhVcfHiRcEJqMo2b3Nzc3Hp0iW8evUKKSkpsLKygqurK7p06VKhH3ZxcXF48eIFYmNjkZKSgpycHBQXF8PExARmZmZwd3dH48aNa9z5mZOTg1u3biE2NhZpaWnIzMyEmZkZrK2t4eLiAn9/f6WG+yKaP5YyMzNx7do1PHv2DBkZGbC2toaTkxPatGmjtckQK1Ntq0fFYjHCw8Nx9+5dpKSkQE9PD05OTvD09KTemLXQqFGjuAkmyyoNrtYGMTExePLkCWJjY5Geno78/HwYGRnB3Nwc9vb28Pb2RqNGjRR+slmdEhMTcfnyZSQkJCA3NxcODg5o1KgR2rdvr5FOWbm5uXj27BlevnyJhIQEZGZmIjc3FwYGBjAxMUHdunXh4eEBLy+vKvmUsFgsxqNHj/DkyRMudlNcXAxzc3NYWlqiQYMG8PLyUvipLEKUQYF0QqoZZQPphFSGwsJCwTH2Zfn555/x1Vdf8S7fsWMHRowYUdGiEUJqIGUD6YQQQkht1bRpU5kTzVpYWCAxMZE6FxFCiBJoaBdCCCEV1q1bN3zzzTeIjY1VKP2mTZswb9483uX6+vpaHQOQEEIIIYSQ6u7AgQMyg+gAMHLkSAqiE0KIkir/mRVCCCE1TlpaGpYuXYply5ahffv26N69O1q2bAkXFxdYWloiNzcXSUlJuHPnDvbs2YN79+4J5jdjxgwat5MQQgghhBAFlc49VFhYiNevX+PMmTP45ZdfZKYViUSYMWNGJZaOEEJqBgqkE0IIURvGGK5evYqrV6+qnIerqysWLFigxlIRQgghhBBSs9WvX1/htB999BE8PT01WBpCCKmZaGgXQgghVYajoyPOnTsHCwsLbReFEEIIIYSQGsfCwgI//vijtotBCCHVEgXSCSGEVAndunXD1atX0ahRI20XhRBCCCGEkBpHR0cHmzdvhqurq7aLQggh1RIF0gkhhFRYhw4dYGxsrNK6bdu2xd9//41z584p9UgqIYQQQgghRDHW1tbYt28fgoODtV0UQgiptmiMdEIIIRW2bt06LF++HCEhIbh+/Tru3buH6OhoJCYmIisrC0VFRTA3N4eFhQXq1q2LJk2aoEWLFujZsycaN26s7eITQgghhBBSo+jp6cHa2hq+vr7o3bs3xo0bB1tbW20XixBCqjURY4xpuxCEEEIIIYQQQgghhBBCSFVFQ7sQQgghhBBCCCGEEEIIIQIokE4IIYQQQgghhBBCCCGECKBAOiGEEEIIIYQQQgghhBAigALphBBCCCGEEEIIIYQQQogACqQTQgghhBBCCCGEEEIIIQIokE4IIYQQQgghhBBCCCGECKBAOiGEEEIIIYQQQgghhBAigALphBBCCCGEEEIIIYQQQogACqQTQgghhBBCCCGEEEIIIQIokE4IIYQQQgghhBBCCCGECKBAOiGEEEIIIYQQQgghhBAigALphBBCCCGEEEIIIYQQQogACqQTQgghhBBCCCGEEEIIIQIokE4IIYQQQgghhBBCCCGECKBAOiGEEEIIIYQQQgghhBAigALphBBCCCGEEEIIIYQQQogACqQTQgghhBBCCCGEEEIIIQIokE4IIYQQQgghhBBCCCGECKBAOiGEEEIIIYQQQgghhBAigALphBCiBmPGjIFIJIJIJMLWrVu1XRyt27p1K7c/xowZo+3iEEJqidTUVHz33Xdo06YNrK2toaurK3htfvr0KaZNmwZfX1+Ym5tzaUUiEWJiYiq9/JVh0aJF3GdctGiRtotDCCGEkEqgbBuJEHWpaW1PCqTXIF26dJH6ASgSiXDkyBGl8pgzZ065PGrCgU7Uw93dnSpbQmo4qkuIpknWJcq+hI6jmJgYNGvWDAsXLsStW7eQnp4OsVjMm/7YsWNo1qwZ/vrrLzx+/BhZWVka+LREGyRvbqvj5e7uru2PREitRe0SUlnEYjGOHz+O8ePHo0mTJrCxsYG+vj5MTEzg6OiINm3aYNSoUfjzzz9x//59bRdXKcq2kYhioqOj8ddffyE4OBi+vr6wt7eHgYEBLCws4Orqii5dumDWrFn4999/kZubq+3iEjXR03YBiGZt27YN/fv3VyhtcXExdu3apeESEUJIiZiYGNSvXx8A4ObmVmN7f9YEVJeQ6mDy5MmIjY0FABgbG6NHjx5wdnaGrq4uAMDb25tLm52djdGjRyMvLw8A4OjoiI4dO6Ju3boQiUQAAAsLi0r+BMpbtGgRFi9eDABYuHAhBYYIUbPS6wEAMMa0WBIiidolRN1u3ryJcePG4fHjx+WWFRUVITc3F0lJSbh16xZ27NgBAAgICMDFixcruaSqUaaNROSLjIzEd999h927d8u8IVFYWIjMzEzExsYiJCQEK1asgKmpKYYNG4b58+fTDXoZLl68iK5duwKo+ucWBdJruGPHjiEtLQ3W1tZy0545cwaJiYmVUCpCCCHVCdUlRFO6d+8OLy8vhdP7+/vLfD8pKQmnT58GABgaGiI8PBweHh68+Rw9ehSpqakAAF9fX4SGhsLY2FiJkpOqrEePHjAzMxNMs3r1au7vAQMGwNnZmTetjY2N2spGCKk4apcQdTp79iz69evH3VwHABcXF7Ro0QJ2dnZgjOHNmzd48OABoqOjuTTp6elaKK3ylG0jEWH//PMPxowZg5ycHO49kUgEX19fNGrUCDY2NigsLERycjIiIyPx4sULACWdODZu3Iht27YhNjYW9vb22voIpIIokF5D+fj44PHjxygoKMCePXvwySefyF1n+/bt5dYnhChm69atNNyNhDFjxtDY6DUA1SVE00aMGKGWa8Xdu3e5vzt16iT3B6Jk+mHDhtWqIPqiRYtqfM/1ESNGYMSIEYJpJAPpM2fORJcuXTRcKkJIRVG7hKhbWloaPv74Yy6I7uHhgb/++gs9evSQmT4hIQGHDh3C1q1bUVBQUJlFVZmybSTCb+3atZg6dSr3hFKdOnUwZ84cTJgwAXXr1pW5TnR0NA4cOIA//vgDcXFxKCwsRGFhYWUWW+tqWtuTxkivoYYNGwZ9fX0A0o0HPhkZGTh06BAAoFmzZnjvvfc0WTxCCCHVANUlpLpIS0vj/nZ0dFR7ekIIIdpH7RKibps2bcKbN28AAHXr1sXly5d5g+gA4OTkhKlTp+LWrVs4cOBAZRWzQqjNox7Xrl3DjBkzuCB6ixYt8OjRI8ydO5c3iA4A9evXx5w5c/DixQv88ccfMDExqawiEw2hQHoNZWtri969ewMAbty4gWfPngmm/+eff7jJD0aPHq3x8hFCCKn6qC4h1YVkzx4dHfnNW2XTE0II0T5qlxB1Kx3yBADGjh2r1HAbDRs21ESR1I7aPBUnFosxZswYbl+6ubnh/PnzcHBwUDgPfX19zJgxA+Hh4bC0tNRUUUkloLOoBhs1ahT3t7w79qXL9fT08PHHHyu1nTt37uCHH35A37590aBBA5iZmcHAwAD29vZo37495s+fj1evXimcX0pKCn799Vf06NEDTk5OMDIygomJCdzc3NCyZUsMGzYMW7ZsQXx8PG8ejDEcOnQIH3/8MRo3bgwLCwuuTL6+vujZsyeWLFmC0NBQlWerPnDgADfruzLju758+RI6OjoQiUQwMDDA27dvy6XJysrC2rVr8f7778PV1RUmJiYwMjKCi4sLmjVrhoEDB+Kvv/5CVFSUSmXXpq1bt3L7TZHH+WNiYrj0fJNy8KW5cOEChg0bhgYNGsDIyAjW1tYIDAzE0aNHy+VRXFyMgwcPonfv3nBzc+P294gRI/DgwQO55RwzZgxXBr4hXhYtWsSlKX20qaioCNu3b+cmfDE0NISjoyMGDBiAY8eOyd2upMLCQmzZsgUDBgyAm5sbjI2NYWFhgcaNG2P8+PE4c+aMwnlV9DwU+p5Ll5VONAqUnBel6cu+AOD169cwMDCASCSCnp6e4PkvSSwWw9XVlctLmX1ASlBdQnVJVXXx4kVu340dO5Z7f9u2beWuI6WPlJb+v23bNi792LFjy6UXmuDo3LlzmDJlCnx9fVGnTh0YGhrCyckJQUFBWLVqFRe0UVRGRgb+/PNP9OvXD+7u7jAzM+Py7N69OxYvXoxHjx5JrdOlSxeIRCJuolEAWLx4scxraNlrsKy6qNSdO3e4ZVZWVlLjxQrJycmBubk5t65QvckYw8GDBzF69Gh4enrC0tISRkZGqFevHgYMGIBt27ahqKhIoe1WpuzsbKxZswb9+vWDm5sbTExMYG5uDg8PD4wbNw7nz5+Xm4esurG4uBjbt29Hz5494ezsDAMDAzg5OWH48OEy92NmZib+/PNPtG/fHvb29jA2Noanpydmz56NlJQUuWUoW78CJd/7pEmT0LhxY5iZmcHa2hqtWrXC0qVL8e7dO8V3EkraIjt27MCQIUPQoEEDmJubw9TUFPXr18ewYcNw8OBBuZN2Sp7bkkPu/Pfffxg2bBg8PDxgZmYGkUiEFStWlNv+qVOn8OWXX6Jr167c9d/Y2BguLi7o06cP/vjjD2RlZSm0fUl87RTJydL5yi5E1neiSJrw8HDMnDkTfn5+qFOnDkQiEQYMGCBz/bdv3+K3335Dz549Ua9ePRgZGcHKygo+Pj6YNm0abt++rVBZqwJql1C7RJ3i4uK4vyV/m1SUIue1pNJ6XagNIitNYmIili1bBn9/fzg4OEBXVxdWVlZKt5HKUvfxXyovLw+bN2/GkCFD0LBhQ+4YtrOzQ6dOnfD111/j5s2bCuWl7vaYkH/++Ufqxt369etVDoY3atQI5ubmMpfJaqPl5uZi06ZNCAwMhKurK/eb+N69e1Lrvnv3Drt378bkyZPRpk0b2NrawsDAABYWFmjUqBE+/vhj/PPPP4LXBk21A4XanqXLSicaBYCQkBCZ9W1prCcsLIx7z9raWuHv+t27dzAxMeHWjYyMVGi9chipMQICAhgABoCtWbOG5efnszp16jAAzN3dnYnFYpnrRUdHM5FIxACw999/nzHG2NChQ7m8Fi5cyLvN1q1bc+mEXvr6+uynn36S+xkOHTrErK2tFcrT2dlZZh5JSUmsXbt2CuUBgJ05c0b+zpUhLy+PWVlZcfmEhoYqtN7SpUu5dfr3719u+bVr15izs7PC5S8sLFSp/Kpwc3PjtrtlyxaV8tiyZQuXx+jRo+Wmj46O5tK7ubkplKaoqIhNnz5dcL9988033PqvX79mbdu2FTx+//nnH8Fyjh49Wu6+WbhwodR5FRcXx9q3by9YzrFjx7Li4mK5++nGjRusYcOGco+Xnj17suTkZMG81HEeCn3PkssUeZUKDg7m3luyZIncfcIYYydOnJA6NhTZl7Ud1SUlqC7RHHXUJYwxduHCBYU/38KFC6WuwfJeFy5cKLe9V69esS5dushd18nJiV26dEmhz7BmzRqFj9UTJ05w60mep/JeZa/BZeuisry9vbnl8uq+Ujt37uTWadKkCW+68PBw1qxZM7llbty4MXv06JFC21aVvO9b0r59+5iDg4Pccvft25elp6fz5lO2bnz9+jXr3Lkzb34GBgbsv//+49a/fv06c3Jy4k3v4ODAIiIiFP7cjDG2ePFipqOjw5uno6MjCwkJUWifXrhwQaG2SNu2bVlcXJxgPqVpAwICWHp6Ohs4cKDMvJYvX86t9+rVK2ZjY6PQeWFra8tOnz4td/uKvKKjo3nLroiy34kiaRYuXMh0dXXLleWDDz4ot+6qVauYpaWl4GcQiURs3LhxLD8/X6EyVyZql5Sgdolm+Pj4cNv88ssv1ZavIue1JMnjnK9OKpuG77iytLRUuo0kSd3Hf6kDBw4ofAysWbOGNx9NtMfkkdyej4+PWvKUpWwb7fHjx8zX11fm5wsLC+PWO3DgADM0NFRo3zZr1kyq3ipLE+1AobanMu1zyXhQy5Ytuff//vtvhcq5Zs0abp1OnToptI4sNNloDWZgYIAhQ4Zg7dq1iImJwaVLlxAQEFAu3fbt27meIZJ3+BVRehfS0NCQm6XY0tISjDEkJibi5s2bSElJQWFhIb766isAwJdffikzr9u3b2PQoEFcLyRjY2O0bdsW7u7uMDQ0REZGBp4/f44HDx5IzZAsqbi4GO+//z7u3LnDvefn5wc/Pz/ujlpSUhLCw8MrPHu7oaEhBg0ahI0bNwIAdu7ciVatWsldb+fOndzfZSfCio2NRVBQEDIzMwGUPP7TunVrNGrUCCYmJsjOzkZMTAzCw8ORkZFRofLXZPPmzcOqVaugo6ODdu3aoXHjxsjLy8OFCxe4733JkiXw9vbGgAED0LNnT9y/fx8mJiYICAiAi4sL3rx5g7NnzyIrKwuFhYUYMWIEmjdvrrZH+LKystCrVy88fPgQJiYm6NSpE+rVq4fMzExcuHABycnJAIAtW7agcePG3Pkjy6VLl9C7d2+p88Lf3x++vr4oKCjAjRs38Pz5cwDAmTNn0LFjR1y5ckXmWG7qOA/l8fb2xrRp05CZmcn1FDI3N5d7/Zk0aRI3FuHmzZsxb948ub08Nm3axP09duxYepxRBVSXlKC6pOpxdnbGtGnTAAARERE4d+4cAMDLywvdu3eXSuvv7w8AXPpz584hIiICANC9e/dyve6cnZ2l/n/y5Am6d+/Ofd8ikQjNmjWDr68vTExMEB8fj0uXLiEzMxMJCQno2bMnTpw4IdW7pqwZM2bgzz//5P7X1dVF69at4eHhASMjI7x58wb37t3jertK9goaOHAg/Pz8cOvWLYSGhgIAWrduzX1OSW3btuUtgyzDhw/HN998A6DkOBs0aJDcdYSOx1KXLl1Cv379uGNOT08PrVq1QuPGjaGvr4+YmBhcuXIFeXl5ePr0Kdq3b4/r16/D29tbqfKr2/LlyzF79mzu+mZubo527dqhXr16KC4uxuPHjxEaGgrGGI4dO4aAgABcu3ZN7hioRUVF+PDDD3H16lWYmJigS5cucHZ2RnJyMs6ePYvs7GwUFBTgww8/xMOHD1FQUIDAwEBkZmbC3t4enTp1grW1NZ4/f46LFy9CLBYjKSkJAwcORHh4ODeOtJCVK1di4cKFAEqGKGjTpg0MDQ3x6NEj3Lp1C0BJb8c+ffrg/PnzMo+vUv/88w+GDx/OPfZuZGTEXXt1dXURGRmJ69evo6ioCDdu3EC7du0QGhoqdxgFxhhGjBiBY8eOQSQSoXXr1vD29gZjDA8fPpRqB2RnZ3O9YK2treHr6ws3NzeYmZmhoKAA0dHRuHHjBvLy8pCSkoI+ffogJCQE7du3l9qm5LVFclLa0vfKsrCwEPwM6vbLL79wT6M0bNgQ/v7+MDExQUxMTLnvfdasWVK99m1sbNC2bVs4OTkhLy8PYWFhePjwIRhj2Lx5MxISEnD8+PEq3V6idkkJapeoR6NGjbgJaLdt24bZs2fDzs6uUsugimvXrmHRokUoLCyEjY0NOnfuDFtbWyQnJyMsLEylNlIpdR7/pX777Td88cUX3DkpEonQtGlT+Pj4wMzMDKmpqXjw4AGePn0KALw9oTXRHpMnLy8P169f5/4fOnSoynkp4+3bt+jVqxdevXoFIyMjdOrUCW5ubsjMzMSNGzek0iYnJyM/Px8A4OLiAh8fHzg4OMDExARZWVl48uQJ7t69C8YY7t27h06dOuHevXuwsbEpt11NtQP5+Pv7Y9q0aYiPj+fmtHBycsLAgQPLpZUs76RJkzB58mQAJb/5hw8fLndbkrGB8ePHK1VOKSqH4EmVU/ZuPWMld35L3xs3bpzM9Ro1asQAMCsrK5abm8sYU/xu/SeffMKOHz/OcnJyZC4vKipiW7ZsYaamptxdyxcvXshM+8EHH3DbDA4OZqmpqTLT5eXlsePHj7PJkyeXW3bw4EEuD0dHR3bjxg3esj98+JB99dVX7ObNm7xp5Ll48SK3PQcHB1ZUVCSY/u7du1x6CwsLbn+XmjlzptQdsvj4eJn5FBYWsosXL7Lhw4fL3aY6VYce6fr6+kwkEjFfX1/28OFDqXS5ubls2LBhXFoPDw+u5/qQIUNYSkqKVPqEhASpO8BjxozhLaeyPdJL7xiPHj2avX37Vipddna2VDnNzMxYVlaWzDxTU1Ol7uw3bNhQZs+Rv//+mxkbG3Pp+vXrJzM/dZyHjCn2PSvy3UoSi8Wsfv363Drnz58XTJ+SksIMDAwYAKajo8NevnwpdxuE6hLGqC7RNHX1SJekbN2iyDW7VHZ2tlTvnB49erCnT5+WS/fu3Ts2ZcoUqWOHr3eyZI+Y0jooNjZWZtoHDx6wGTNmsFOnTpVbJq93uSzy1omJieF6cRoYGPCeQ6WSk5OZnp4ed62V1dM4MTGR2dvbc9sdNmyYzHRJSUlSPY/fe+89jR2bkvufr/ff2bNnud7a+vr6bMmSJTLr47CwMKlejZ988onM/CSP09L6KTg4uFz7Iz4+Xqr9MWrUKNayZUsmEonY0qVLWUFBgVT6q1evMnNzcy791q1bFfrcBgYGzMjIiO3YsaNcuhs3bkidq40bNy53rSn18OFDZmJiwqX97LPPyrVtGGPs+fPnrGPHjly63r17y8xPsidl6bH13nvvsfv375dLm5eXx/0dExPDPv30U3bz5k3eJ9DevXvH5syZI9UWFHpaTXJ/KaIyeqTr6ekxS0tLdvDgwXLpJPfHpk2bpNqSpb25yzp//rxUW1KZHqaVgdol1C7RpLJPytavX59t2LBB5jVMGcpeO5Ttka6np8dEIhH7/vvvy9UJktcBxpRvI6nz+GeMsePHj3PtCgCsW7du7MmTJzLTvnjxgn377bcy6zFNtMcUcenSJanv8+TJkyrnJY9kG620/hs0aBB78+aNVLri4mKp7/3IkSPshx9+YM+ePePN+8WLFywoKIjLf/z48TLTaaIdqEh7Vdn6MzMzk5mZmTGg5KmqqKgowfT379+Xuk5lZ2fL3QYfCqTXILIaGYwx5unpyR0sZS+GV69e5daZNGkS976ijQxF7dmzh8uP75Gp0kcxDQ0NWWZmpkrbmT17NredDRs2VKTIChGLxczV1ZXbpqwfuXzlk9Xok3w8RegiqC3VIZAOgNnZ2bHXr1/LTJuZmVnusd+ePXvy/oiSbKibmZnxPmaobCAdKAkk8MnNzWX16tXj0u7Zs0dmugULFnBprKysBIPF//77r9T2ZT2qrY7zkDHNBNIZY2zJkiXcOsOHDxdMu3z5ci5tUFCQkp+g9qK6hOoSTZOsS7p3786mTZum8IuPJgPp3333HZe2V69ech83HzVqFJf+xx9/LLc8NTVVKug5ZcoUueXlo4lAOmNMKti5bt06wfz+/PNPqR/HsowbN45LM2HCBMH8ioqKWNeuXeXWfxUlWR/KCloUFxczDw8PLo28x4YTExOZnZ0dF1SQdWOkbMCmW7duvO2P69evS6UFwL7//nve7S9btkzqOFXkc8vbv0+fPpUKkK9evVpmum7dunFp5A29lpWVJRUIkRUQLDskgYODQ7kgQkVJBlkkh88pS7IciqiMQLpIJJI73E5GRgY3PIeurq7c9I8fP2ZGRkYMALOxsalQgEHdqF1C7RJNKiwsZK1atSp3bdTV1WVNmjRhEyZMYGvXrmXh4eG8wwjJouy1Q9lAuiLX21LKtpEUpcjxX1hYyNzd3bl0ffv2VXnYHnW3xxS1Y8cOqf2uyc5ZZeMFgYGBah2atKCggDVp0oQBYEZGRrxBcnW3AzURSGeMsQkTJnDrzJ8/XzCt5A09vs6Aiqq6z2wRtRk5ciSAksmsSh+VKCU5QYuyj7wpY9CgQTAzMwMAnD17Vmaa0se4TExMuLTKknwUTNawFeomEomkJq6RfKSlLLFYjN27d3P/y3rkpbLLX1PNnz+f95E8MzMzvP/++1Lv/frrr7yPsLZr1w6urq4ASoZjKR0KoKIMDAzw+++/8y43MjLCsGHDuP9LH92XxBjD+vXruf+//fZbrqyyDBw4EL179+b+X7NmTbk06jgPNWncuHHQ0ysZlezAgQNIT0/nTbt582bu7wkTJmi6aDUe1SWaU5vrknPnzmH16tUKvypbYWEhVq1aBQDQ0dHB2rVruWsQnx9++IEbbkLWd7l+/Xru8XY3N7dykyVWBZLHldDxCAB///23zPVKvXnzhsvD0tISy5cvF8xPV1cXy5YtU3j7mnL06FFuYrHu3bvLfWTYwcEBs2bNAlBy3Ozbt0/uNn7//Xfe9kfbtm3h5uYmlb/QMG8fffQR97esNoMsnTt3FnxE3dPTk/tMALBhw4ZyacLDw7mJVj09PfH1118LbtPU1BQLFizg/lfk+12wYAFsbW3lplOG5OR7fPVJVTV48GB07txZMM3mzZu5NtKYMWPkpvf29sbo0aMBlAwncPLkSbWUVZOoXaI5taldoqenh+PHj5ebHLi4uBj379/Hxo0bMWXKFDRt2hS2traYOHEiwsLCtFNYCc7OzoJ1QmVQ5Pg/cOAAN0SdqakptmzZIrcdJYsm2mOKSk1NlfrfyspK7joLFy7E9OnTeV9//PGHQttesWKFWofa0tfX59ozeXl5uHLlisx06mwHatKkSZO4v7du3Yri4mKZ6QoKCqTKWdHYAI2RXguMHDkSCxYsAGMM27dv54Jz+fn52Lt3L4CS8fU6dOhQoe08ePAAd+/eRUxMDDIyMrgxmkqVXsQePHgAsVhc7oLg6uqK58+fIy0tDbt27VJ6ZvXSPEqtW7cO77//vkoXamWMHDkSP/74IwDg4MGDWLt2LYyNjculu3DhAhISEgCUjFslayw/V1dX7kfb6tWrMW/ePA2WvOYKDg4WXO7n58f97eHhgSZNmshNXzpWXHR0tNT6qurYsSMcHBwE0zRv3pz7u7QBIunJkydISkoCUNKgKP0BJGTChAk4ceIEAMicEV4d56EmOTo6om/fvjh06BDy8vKwa9cuTJ06tVy60NBQbrZwW1tb9O/fv7KLWuNQXUJ1SW10+/Ztbs6Kdu3aSQU2+Tg5OcHLywtPnjzBw4cPkZ6eLvWjSzJANXHiRBgaGqq93BU1ZMgQzJgxAwUFBbh8+TJevXol80bt8+fPcfPmTQAlN4Bl1b9nz57lzuO+ffsqFEhq06YNTExMkJOTw/sjT9P+++8/7m/JILWQbt26cX9fuXIFn3/+OW/aRo0aoWnTpoL5+fr64uXLlwCAfv36CY57Xr9+fZiamnLjhGdmZsLc3Fwwf0UCjKNHj8bSpUsBlATN09LSYG1tzS2X3E+DBw+Grq6u3DzL7ichIpFIpfFoCwsLcfPmTYSHhyMpKQmZmZncGNUAuJtZAHDv3j2l89cmRY5HVY/fdevWASj5Xj788EPVClhJqF1C7RJ1sbOzw/nz57F//36sXLkSV69e5cbylpSamoqNGzdi06ZNGD9+PP78808YGRlpocQlv3c1fQwAFT/+Jds8w4YNU/mmqCbaY4qSrC+AkhsC8mzbto2rv2UJCAjAzJkzBfNo0qSJSvPEpKen48aNG3j06BHevn2LrKwsiMVibrlkx8B79+6hX79+5fJQZztQk1q3bo1mzZrh3r17iI+Px6lTp9CnT59y6Q4fPszNodKkSROF5n0QQoH0WsDNzQ2dO3dGSEgIzpw5g6SkJDg4OODIkSNcT4XSO/qq2LZtG5YtW4bIyEiF0hcWFuLdu3dSjXCgZNKG0h5II0aMwJ49ezB06FB069YNjo6OCuU9aNAgLFy4EGKxGCdOnICPjw/Gjh2L3r17o0mTJhqZOMfHx4c7eTMzM3H48GGZDVbJO2Aff/yxzLIMHTqUmwhk/vz5OH36NIYPH46ePXvC3d1d7WWviSwtLctNEleW5LHn4+MjN0/J9OqaAOe9996Tm0ZyMo13796VWy7ZG8LLy0vmZCFlSf6YSEpKQkJCApycnLj31HEeatrEiRO5nkebNm2SGUiXnEhk1KhRMDAwqKzi1VhUl1BdoglbtmzBmDFjtF0MXpKTS6WkpGD69OkKrVd6TjDGEB8fL/XDrfQHB4AKTX6lSdbW1ujTpw8OHToExhh27dols6ex5PHYv39/mZMuSu7DyMhIhfdh6Y/ztLQ0ZGdnK/TDVZ0ky338+HGFgq2SdXVsbKxgWl9fX7n5KdtesbKyQnZ2NoCS9oq8QLoiE9F6eHjAxsYGb9++5SYpkzxuJffT9evXFfp+JQNU8vaTu7s76tSpIzfPUrm5uVi2bBnWrl2LlJQUhdZRNF1V0bJlS7lpJL+X7du3l+uxLUtcXBz3t7zvpSqgdgm1S9RJJBJh8ODBGDx4MBITE3Hx4kVcv34dd+7cwb1796QmgmWMYePGjXjx4gVOnTpVKQHtshS5DlSEuo5/yUkxK9Lm0UR7TFFl69Ls7OxKmWRa2e84Li4OX3/9Nfbv31/uhgcfvvpPne1ATZs4cSI3se6mTZtkBtIlYwNqeVK9QgPDkCqFb/w4xqQnm/n1118ZY4z17duXG2fv+fPnUukVGT9OLBazsWPHlhtPTJGXrHGlsrOzWYcOHWSmr1+/Phs1ahTbvn07e/funeB+WL58udRkFqUvCwsLFhQUxH744QcWERGhxJ6V79dff5Ua96us3NxcZmFhwaWRNVkSYyXjcQ4aNEjmPnBycmJDhw5l69atY8nJyWotv6Kqwxjprq6uSpVh1KhRctMrMpausmOkL1iwQO525Y0TJjkGON+EXbKUjoEJgIWHh0stU9d5qKkx0hkrOU8kx228d++e1PKcnBxmaWnJLX/06JHCeROqS0pRXaI51WmyUclxX1V9Xb58mcvv3bt3UssSExMr9Lk1NUY6Y4zt37+fS+fn5yczTekYxQDYkSNHZKYJDg6u8D7km4i1IiTzlzUebdn5VJR9NWzYsFyeFWl/bN68WW56yXMrOjpa7ufOyMiQmydjjDVr1oxbZ+/evVLLJMc+VuWlq6tbbnuS7R9/f3+FyshYyfwDkmVV9FW/fn3ePCXTKaIyxkjnmwCwVGZmZoXPue7duytU9spA7ZIS1C7RnoKCAhYSEsLGjBnDdHV1pcr/22+/yVxH2WuHsmOkC83tUJYybSR1H//W1tbc8uvXrytc5rLU3R5ThrrGSJdsf/HVD5Jp+Madl+Xu3btS+1rR19ixY3nzVFc7sOznUucY6YyVtK1L53PR19cvdx2JjY3lJo43NDSs8ETCjNEY6bXG4MGDYWJiAqCkV0JycjL3mE3Hjh3RoEEDpfPcsGEDtmzZwv3ft29f7NixAw8fPkRaWhry8/PBSia0BWNM6vEbyUdLSpmYmODChQtYvnw5GjZsKLUsOjoa27dvx6hRo+Do6IgvvvgCubm5Msv12Wef4dKlSwgKCpK6I56RkYFTp05h7ty58PLyQvfu3bmhHypK8u77qVOnuMdGSh05coTrydykSRPe3sg6OjrYt28ftm7dWm64kYSEBOzduxeTJ0+Gk5MTJkyYUG68LvK/HmyaSq8u6thuVlYW97cyPfUk05Z9VE1d56Em6ejoYPz48dz/kneYAWD//v1cr8B27dop1IuPKIbqEqpLahtZTwMpi284CQBVci6KUn379uV6bj18+BD379+XWn7r1i2up5qNjQ169eolMx9178PKUtFyyytzVWivlF7P5RFqN1R0P/GNZ1pK1lASfKZNm8Y9OWBoaIjJkyfjyJEjePbsGTe0S2ldEh0dza0nqy6pyuTtk+p6zqmC2iXULqkM+vr66Ny5M7Zs2YILFy5IXRMVHeta3ZS5NipD3ce/ZJ1RkTaPNq9rZYeRefLkSYXLoghFv+P8/HwEBwcjLS0NAGBvb4+FCxfi4sWLiI2NRXZ2NsRiMfedSX6/QvWfutqBmmZhYcENAVdYWIgdO3ZILd+yZQv3OT/88EOlnnLjQ4H0WsLc3BwDBgwAANy/fx9fffUVdyFRdQKWX3/9lft76dKlOHr0KEaMGAFfX19YWVmVG0qhbMNbFn19fXz22WeIiorCgwcPsHr1anz00UdSQ3Xk5OTg119/RdeuXXkbGh07dsTJkyfx+vVr7N+/HzNnzkSLFi2kGh3nz59HmzZtcPXqVWU/ejmOjo7ceI+yJpiSnKBB3gQMIpEIo0ePRnh4OKKiorBp0yaMHj1aqiFYVFSETZs2wd/fH2/evKlw+auy6vbjpjJJNkZKH+VWhGRaWY99q+s81KTx48dz47Du3LlT6vE1ycC6ZMCdVBzVJVSX1DaSP5Y/++wzqR+Pir4kJzAre82VvCFa1RgaGmLQoEHc/2Unm5L8f+jQobzjd0vuwxUrVqi0D7Xx6L9kue/du6d0mWXNbVLVSA5VIESo3SC5n0ofAVf2pQ7x8fHYs2cPgJIJa0+fPo21a9eiX79+aNSoEczMzKTGb1ekLqkMmmjnlu1ckZ6ervR3ImsenaqI2iXULqlsnTp1khrj/dWrV9xcWhVRVX7zqvv4l6wzKtLmUXd7TBmtW7eWms/m1q1bKn8OTThw4AB3c9jFxQXh4eFYtGgRAgIC4OLiAhMTE6mb8YrWf+pqB1YGyUlHJWMBZW8cqCs2QIH0WkSyMbF161YAJRMCDB48WOm8YmNjuQlDrK2t8eWXXwqmz8jI4O6QKcrPzw9Tp07F7t27ERcXh7CwMKkD/+bNm1i9erVgHra2tggODsaKFStw584dvH79GitXruQmucjNzcXkyZOVKhcfycaD5FhRqampXM8IHR0dpSaYadiwIcaNG4etW7fi+fPniIyMxJw5c7hx2J4/f47FixerpfyVRfICq8hdYXXcfa6pJGe9V7QBl5ycjLy8PO5/eRO+qOM81ARnZ2f07t0bQMk5dvDgQQAl58SlS5cAlNxoUGWCMiKM6hKqS2oTe3t77u/SY7UiLCwspHoYSfaKrYokj8ddu3ZxQc/i4mJuMr+y6cpS9z6sLNW13MpQtO0gOV522XZDVdlP58+f547PPn36oHPnzoLphSaBq4iq0M61srKSCvrU1OO3FLVLqF1S2Up/g5RKTEwsl0Zy3PTq8ptXE8e/ZB1RkTaPNusaIyMjtGvXjvtfsv1TFZTOPwAAs2bNktpXsihT/6mjHVgZ2rZtyz0R8/jxY25s/gsXLnDHXf369aUmO68ICqTXIj169JCaVBAAPvjgA1haWiqdV+nM3ADQuHFjuRNsXLlypcI9Tpo1a4aNGzdK3W06cuSIUnnY2tri008/lVrv0aNHePHiRYXKBpTMnF36aOG1a9e4E3bfvn0oKCgAAHTp0kXuRJhCPDw88Msvv+C7777j3lN2H2ib5F3pso8HyqKuRxNroubNm3N/R0REKPQYpGTvFAcHh3LXBHnUcR6Wquhj6rLuPG/evJm71gwdOrRKD5tQXVFdQnVJbdKmTRvu75CQEIUnb1I0z/Pnz1coL00PT9a5c2e4uroCKJnEKiQkBABw5swZvH79GkBJQETyB2ZZkp/31KlTGiytelXXcitDcvI2Ps+ePePaayKRSKrtAVSd/SRZnygykWvpTXd1qyrtXH9/f+7vmnr8lqJ2CbVLKpuRkZHU/5I3rkopcy0oKChQeFJPTdLE8S85qXVF2jyaaI8pQ/LG1KNHj3DmzJlK3b4QTdZ/6mgHKqqibdqJEydyf5fGBiR7p48bN05t7WYKpNciurq65e4Uq/rIm+TjY4o8FrpmzRqVtiNL3759ub9LT15ltWvXTmpsJFXzkWRmZob+/ftz/+/atQuA9J17dd2pU8c+0Jb69etzf4eHh8utfMs+Qkj+x9vbGw4ODgBK7gpLHmt8JB9tqsjM6eo4BiUboYWFhUqv36dPH67Rfu7cOTx//hzbtm3jltOwLppBdcn/UF1S83Xo0IEbHzIrKwsbNmyocJ6SPdk2bNhQoR+DFb2OyiMSiaTO99LHeCWPx+HDhwvmERQUxP0Yj4qKwrFjx9ReTk2QPD927dqF5ORkLZZGM8qOIypLaQ9fAGjatCmsra2llkvup3PnzmmtA4Qy9UlOTg62b9+uUL7KnmPu7u7cD/WoqCi5Qxloqp0r+b2sXbtW6mnEmobaJf9D7ZLKUToXA1BST7q4uJRLI/mbVzK9LEeOHKkS56gmjn/JNs+ePXuQkpKiUtk00R5TxuDBg9GoUSPu/0mTJnHzA2ibMt/bnTt3EBoaqnDe6mgHKqqibdqRI0dyT33u3bsX8fHx3FPrurq6GDNmjFrKCVAgvdaZP38+QkNDuVdQUJBK+dSvX59rJD58+BDPnz/nTbt37165P5ry8/MVHjNL8jFUyaEtACh8YU5LS5PaXtl8VCXZiNi5cydiYmJw7do1ACUXhuDgYMH1FS2/0D6o6ry9vbk79ImJiTh9+jRv2uPHj+P48eOVVbRqRyQSSfVe+e677xAfH8+b/vjx4zh69Cj3/5QpU6SWq+s8VJSVlRVX8ScnJytdYerq6mLcuHEASsY/GzlyJPf5fXx81HJnnMhGdUkJqktqPkNDQ3z22Wfc//PmzVMqUCgrEDBx4kTuaZmXL19K5a8sGxsb7m+h639FjBw5kvt7//79SE1NxaFDh7j35AVQnJ2dpdJMmTJF4bKKxWKtjZMbHBzM/WjOycnBiBEjFK6nsrKylJq7RFsuXbok+Ij6s2fPsGLFCu7/CRMmlEvj7+/PjTvLGMOIESMUDi4UFBQoPSwGH8lxlo8fPy44lMLs2bMVDtIpe45ZWFigcePGAEqGcyg7pqyksLAwjQWDJk+ezAWd4uLiMHXqVIV7TqekpMidBLaqoXZJCWqXKG/hwoVKBRazs7OxdOlS7v9WrVrJHCpT8qkQyRuSZWVkZODrr79WePuapO7jHyiZ3LF0ss6srCyMHTtWpUk/NdEeU4auri62bNnCdQyIiYlBjx49qsRNdsn67/Dhw7zpcnJypGIHiqpoO1BRFW3TWllZcWO6Z2ZmYvDgwdx8E0FBQTJveKmKAum1jJWVFVq1asW9JCfdUYatrS33eI1YLMbgwYPx9OlTqTRisRirV6/GyJEjoaurW+4RKEmJiYmoV68eZs+ezTt5A2MMJ0+exIIFC7j3+vTpI5VmyJAheP/99/HPP//w/oB59eoVPvroI+5RNA8PD6m7ixURFBTEVfpPnjzB7NmzuUZr//79YWFhIbi+q6srJk2ahIsXL/I2YG/cuIHp06dz/5fdB6W6dOkCkUgEkUik1rtvFaWnpyc1ZuHEiRPx+PFjqTSMMezYsQNDhgyR+agc+Z/PPvuM65X99u1bdO/eXWavh71790qNF96vX79y44eq6zxUlKGhITw9PQGU/NgsvWOsjAkTJnDBeMlH1Kk3umZRXUJ1SW0ye/Zs7lHZzMxMdOzYERs2bOC++7Levn2LjRs3omXLlvjll1/KLbe2tsZPP/3E/b927VoMHToUcXFxMvN79OgRZs6cKfPGc+l4kEDJ8A2aGGPVx8cHzZo1A1AyaeHEiRO588Lf3x8eHh5y81i2bBkcHR0BlPw4at26Nfbv3887uVp8fDz++OMPeHl5aW0sUl1dXaxZs4a7vp05cwadO3cWDLjcv38fc+fOhaura5Uf/x4ADAwMMGbMGJlPtN26dQs9e/bkerd5eHjw1q1//vknd3Po/v378Pf3x9mzZ3m3GxUVhaVLl6J+/fpqmRARALp168YNP/H8+XOMGTMG6enpUmkyMjIwadIkrF27ttyEnHwkzzFFe49L9t77+uuvceXKlXJpTpw4gcDAQI0Nz2RpaYnly5dz/2/ZsgX9+vVDRESEzPSMMVy/fh3Tp0+Hm5ubViaSrwhql1C7RFWnTp3ibghu2bJFcKjMa9euoXPnznj06BH33ty5c2WmlbwO7NmzB6tWrSqXJiIiAt26dcPz58+rxG9edR//QMlv/9WrV3PXumPHjiEoKIj3WhQTE4MFCxbIfGpI3e0xZXXs2FHquhoaGgpfX1/8/PPPgjf909PTsXbtWo3dOJV8kmP79u347bffyp1nUVFRCAwMxN27dxWu/0qpox2oiAYNGnBle/nypUqTukreKNBkbEB40CNCBCxZsgSBgYEQi8UICwvDe++9hw4dOqBBgwbIysrC5cuXuYk3li5divXr1wtObJCeno7ff/8dv//+O+rUqYPmzZvD2dkZhoaGSE5Oxv3796V+lHh6emLmzJlSeYjFYvz333/477//oK+vDz8/P3h6esLS0hKZmZl4+fIlbty4wf1w09XVxcqVK9W2T/T09DB06FCuovz333+5ZYrcqcvNzcWGDRuwYcMGmJubo1mzZnB1dYWpqSlSUlIQEREhFXSuW7cuFi1apLbyK2PBggVSvZTk2bhxI1q1agUA+Pbbb7F3715kZ2cjNjYWzZo1Q0BAABo0aICMjAxcu3YNr169gq6uLtatWyezBxQpYW1tjV27dqF3797IycnB06dP0aJFC7Rp0wY+Pj4oKCjAzZs3pSZl8fDwkBovTJI6zkNlBAcHc706RowYgW3btqFRo0ZSk3VJzh5flqurK4KCgnDixAnuPQMDA5Uf6SWVj+qS8mpTXfL333/j9u3bCqd3cXGp9J5bZmZmOHLkCHr06IHo6GguGPfFF1+gXbt2cHZ2hkgkQmpqKp48eYKnT59yxwbfEFpTp07Fw4cPucei9+3bhwMHDqB169bw9PSEkZER3rx5g7CwMMTExPDm1bp1a7i6uuLVq1dISkqCl5cXAgMDYWtry/1obd26dYUnXh4xYgR3k1bZ4xEAHB0dcfjwYfTp0wcpKSlITEzE4MGDYWdnhzZt2sDe3h5isRhv377Fw4cP8eLFiwqPO6wOPXr0wJo1a/DJJ5+guLgYN27c4H40Nm/eHNbW1sjNzUVSUhLu3btXJXqmKeOXX37BzJkzMXLkSCxatAjt2rWDgYEBHj16hJs3b3LpTExMsH37dt6AiZ+fH3bv3o2hQ4dybZGePXuiXr16aN26NWxtbVFQUIA3b94gPDyc96ZRRVhbW2POnDncGMs7d+7EiRMn0KZNGzg7OyMxMREXL15EdnY2dHV18ddff2H06NFy8w0ODuYmVPz6669x8uRJ+Pr6SgW95s+fLzXkzYwZM7B27VokJCQgPT0dnTt3RocOHeDl5YW8vDzcvn2bCyJt2bIFY8eOVeeu4IwZMwYvXrzA999/D6Ckp/5///0HPz8/+Pn5wcLCAtnZ2YiPj0dYWFi5Gw+1FbVLyqsN7ZKQkBCEhIRAJBLB09MT3t7esLGxgY6ODlcfl/2eP/30UwwcOFBmfp06dcL777/PPV396aefYvXq1Wjbti1EIhGePn3KfWdjxoxBdHQ0N/60Nqn7+AeA999/Hz/88APXfjt//jx8fHzQtGlT+Pr6wszMDKmpqbh//z4XuJcMWJfSRHtMWdOnT4eNjQ3Gjx+P3NxcpKSk4KuvvsLXX3+N9957Dw0bNoSNjQ1EIhHevXuHqKgoPHjwQOqJNkNDQ/Tr108t5QFKbnQFBAQgJCQEjDHMmTMHq1evRosWLWBpaYlnz57h2rVrKC4uhrOzM2bOnCl3ItmyKtoOVISOjg4GDBjAPcnVtWtX9OrVC66urtzN0Tp16mDevHm8eXTs2BE+Pj5S1xM7Ozu17m8AACM1RkBAAAPAALA1a9ZUKK+hQ4dyeS1cuJA33Zo1a5ienh6XtuxLR0eHLViwgInFYubm5sa9Hx0dLZVPXFwcMzQ05M2n7KtLly4sMTGxXHn69u2rcB52dnbs0KFDFdpPsty4caPctmxsbFhBQYHcdc3MzBQuf9OmTdmTJ09485I8HkaPHq2Wzyb5HSr7unDhglReJ06cYCYmJrzpLSws2IEDB1h0dDT3npubm8xyKZJG0pYtW5TaN6NHj+bSb9myReU0CxcuVOi8KnXhwgUufUBAgGDa69evswYNGsj9Hnr06MGSk5Nl5qGu85Axxffxu3fvmI+Pj+B25Dl48KBU+kGDBsldh/CjuoTqEklVrS5p2rSpzDw1cV0v6+3bt2zw4MFMJBIpVFYrKyu2detWwTxXrFjBLCws5OYlEonYqVOnZOZx/PhxweO+7P5Qti5ijLGEhASmo6Mjla+enh57/fq1QuuXiomJYd27d1f4+7a3t2cnT55UahuKktxO2TZKWefPn2ceHh4Kl9vX15fFx8eXy0fTx6nQ9VHW52as5HgQOqYdHBzk7p9S9+7dYy1btlR4P7m7u7OwsLBy+SjT/pFUVFTERo0aJfe8PHjwoMJtx8LCQta1a1fBPGXt6zt37jBbW1vedQwMDNjq1asZY+W/E1kUScNn7969zMnJSeHvxd/fn+Xl5Sm9HU2hdgm1SySpu13y3XffMQcHB4XLB4BZW1uzv/76S27eb9++Za1atRLMa9y4cSwvL0/qc/FdcxVJI4uydY+6jv+y9uzZw+zt7RXax+vXr+fNRxPtMWU9efKEffTRR+XaRkIvc3NzNn78ePbixQvefFVpozHGWFJSEmvRooXg9n18fNijR4+UPh4Yq3g7UNHP9erVK8H6SpFYz/Lly6XWmTNnjkJlVAb1SCcVMmXKFHTo0AHLly/HhQsXkJCQAGNjYzg7O6Nbt24YN24cmjdvLjcfZ2dnvH37FufPn8fly5dx584dREVF4c2bNygoKIC5uTnc3Ny4XlU9evSQmc+RI0cQFhaGc+fO4ebNm3jy5Ani4uKQnZ0NQ0ND1K1bF02aNEGfPn3w8ccfy30MTRVt2rSBp6en1MzbQ4cOlephy+ft27e4dOkSQkJCEBoaimfPnuH169fIy8uDiYkJXFxc0LJlSwQHB6N///5SE0tUN7169UJERAR+/fVXnDp1CrGxsdDV1YWrqyv69euHTz75BK6urlxPPCKsbdu2ePLkCf7++28cOnSI6xmnr68PBwcHdOzYEcOGDUNgYCBvHuo6D5VhYWGBW7duYc2aNTh69CiePHmC9PR0pcZLf//992FoaMhN2EdPMFQ/VJeUR3VJ1VOnTh3s27cPDx8+xO7du3Hx4kVER0fj7du30NHRgZWVFRo1aoQWLVqgR48e6Nmzp9xHnmfOnIkRI0Zg69atOHXqFB4/fsyNJWtrawtvb28EBARg6NChvI/O9unTB3fu3MGqVatw5coVvHz5EllZWWrt0e3o6Iju3bvjzJkz3HuBgYGws7NTKh83NzecPXsW169fxz///INLly4hNjYWaWlp0NPTg42NDTw8PNCqVSsEBgaiS5cu3Hik2tS1a1dERETg4MGDOH78OG7cuIGkpCRkZGTAxMQE9vb28PLyQvv27dG7d2/uEejqYNGiRejduzfWrVuHy5cvIyEhAfr6+mjYsCEGDhyI6dOnc2Nty9O0aVPcvn0bp0+fxqFDh3D16lWuV3bptdPT0xNt27ZFUFAQ2rVrp9ahTXR1dbFt2zYMHjwY69evx82bN5GWlgZra2u4urrigw8+wLhx4+Dk5KRw+1JPTw+nTp3C5s2bceDAATx48ACpqam8QwmUatGiBSIiIvD777/j6NGjiI6OhlgshouLC3r27ImpU6fCx8dHDZ9aviFDhuCDDz7Anj17cOrUKYSGhuLNmzfIysqCqakpnJ2d4e3tjU6dOqFPnz7csHu1GbVLyqup7ZJvv/0W33zzDW7fvo1Lly7h1q1bePr0KeLi4pCRkQGRSAQLCwu4uLigSZMmCAoKwgcffKDQ8Bh16tTBtWvXsHHjRuzevRuPHj1CVlYWHB0d0bp1a0yaNAk9e/ashE+pHHUd/2UNHToUffv2xfbt23HixAmEh4fjzZs3KC4uhrW1NRo3boyOHTti0KBBgvlroj2mLC8vL+zevRtLly7Ff//9h/Pnz+PJkyd4+/Yt0tLSYGxsDGtra9SrVw+tWrVC+/bt0bdvX24IMnWzt7fnjrU9e/bg4cOHyMnJgZ2dHRo3boyhQ4di+PDhMDExUWnIFHW1A+WpV68ewsPD8eeff+L06dN4+vQpMjMzlRpXPzg4GLNmzeL+18SQryKmzlY2IYQQUskuXrzIPa5Xr149xMTEUGCQEEIIkUEycE0/AwkhhBBSk2zdupUbMq1Dhw4y5ympKIo0EEIIqdY2btzI/T1u3DgKohNCCCGEEEIIIbWMZGxAU0+qU490Qggh1VZCQgIaNGiA/Px86OrqIiYmBi4uLtouFiGEEFIlUY90QgghhNREd+7cQatWrQCUTEIeHx8PY2NjtW+Huu0RQgiploqLi/HZZ59xY6MPGTKEguiEEEIIIYQQQkgtkpeXhxkzZnD/T5kyRSNBdIB6pBNCCKlG9u7di9DQUGRlZSEkJAQREREAAENDQ9y/f58myCKEEEIEUI90QgghhNQEa9aswfPnz5Geno4zZ87g1atXAABbW1s8ffoUderU0ch29TSSKyGEEKIBJ06cwLZt28q9/9tvv1EQnRBCCCGEEEIIqQX27t2LkJAQqfd0dXWxadMmjQXRARrahRBCSDVlbm6OLl264MiRI5g2bZq2i0MIIYQQQgghhJBKJBKJYG1tjT59+iAkJAT9+/fX7PZoaBfNE4vFSEhIgLm5udTjlIQQQmoGxhgyMzPh5OQEHR26R11TUP1NCCE1G9XfNRPV34QQUrNps/6moV0qQUJCAurVq6ftYhBCCNGw2NhYmvC0BqH6mxBCageqv2sWqr8JIaR20Eb9TYH0SmBubg4AiI6O1ug4PdVRXl4eHj16BD09Pejpqf9wFIvFePXqFVxdXamXSSUqKChAQkICAgICYGZmpu3i1CqFhYU4ffo0AgMDoa+vr+3i1BqpqamoX78+d70nNQPV3/yo/q6ZqP7WHqq/tYPq75qJ6m9+VH/XTFR/aw/V39qhzfqbAumVoPRxMnNzc1hYWGi5NFWLgYEBzMzMYGRkBAMDA7XnX1xcjJSUFFhbW0NXV1ft+RPZ8vLykJ6eDgsLC6rIK1lhYSFMTExgYWFBFXklKiwsBAB6fLiGofqbH9XfNRPV39pD9bd2UP1dM1H9zY/q75qJ6m/tofpbO7RZf9MtQkIIIYQQQgghhBBCCCFEAAXSCSGEEEIIIYQQQgghhBABFEgnhBBCCCGEEEIIIYQQQgRQIJ0QQgghhBBCCCGEEEIIEUCBdEIIIYQQQgghhBBCCCFEgJ62C0AIIYQQQgghhBBSVRWKiwSX6ev8L7Qi+TchhJCaha7whBBCCCGEEEIIIWUUiotwNyUKc29vBmPicsvFjEEMBh3oQEcE6Ih08EOrcWhu2wgABdUJIaSmoas6IYQQQgghhBBCCP7X+/xuShS+Dt2EIlYsdx0xxBAzAEyM2bfWQ0+kIxVUp4A6IYTUDHQ1J4QQQgghhBBCSK13MzkCc29vhlhcjGIwlfMpYmIuqG6go0cBdUIIqSHoKk4IIYQQQgghhJBa7WrSI3x9e5Pa8y0QF1FAnRBCaggdbReAEEIIIYQQQgghRFs0FUSXVBpQ73VyHm4lRwhOYEoIIaRqokA6IYQQQgghhBBCaqWbyREaD6JLKhtQJ4QQUn1QIJ0QQgghhBBCCCG1TqG4CIvubtfKtgvERZhzaz2uvX6kle0TQghRHgXSCSGEEEIIIYQQUussCduFrKI8rW2fAfgqdBMuJd7XWhkIIYQojgLphBBCCCGEEEIIqVVOxd3G+cR72i4GAGD+na3Y8eystotBCCFEDgqkE0IIIYQQQgghpNYoFBdh6b3d2i6GlPVP/6Oe6YQQUsVRIJ0QQgghhBBCCCG1xvd3d4KBabsY5cy/s5XGTCeEkCqMAumEEEIIIYQQQgipFeKz3+JCUri2i8Hrq9BNFEwnhJAqigLphBBCCCGEEEIIqRU+u7FG20WQqzSYXigu0nZRCCGESKBAOiGEEEIIIYQQQmq8G6+fICk3VdvFUMhXoZvQ6+Q83EqO0HZRCCGE/D8KpBNCCCFE4+7evYvFixejf//+8PLygo2NDfT19WFjY4MOHTpg6dKlSE2t+A/bpKQkfPvtt2jZsiXq1KkDY2NjuLm5oVevXvjxxx9RWFiohk9DCCGEkOqmUFyE7+/t1HYxlFIgLsKcW+tpqBdCCKki9LRdAEIIIYTUfJs3b8bq1au5/42MjGBsbIzU1FRcu3YN165dw4oVK3DkyBG0a9dOpW3s3bsXkyZNQkZGBgDAwMAAxsbGePXqFV69eoVTp05hypQpsLKyUsdHIoQQQkg1su9FCDIKc7RdDKUxlPRO/6n1eLS399V2cQghpFajHumEEEII0Th/f3/88ssvuH79OtLS0pCbm4uMjAxkZmZi69atqFu3LlJSUjBgwAC8e/dO6fz/+ecffPzxx8jIyMDQoUMRFhaG/Px8pKenIzMzE5cvX8asWbOgr6+vgU9HCCGEkKruWOzNCq2vJ9Ip99KBCACgAx3o/v/fmkKTkBJCiPZRj3QZMjMz8dtvv+HAgQOIjo6Grq4uPD098dFHH+HTTz+FgYGBtotICCGEVCujRo2S+b6ZmRlGjx4NR0dHBAUFITk5GceOHcPw4cMVzjsxMRGTJ0+GWCzGrFmz8Pvvv5fbRseOHdGxY8cKfQZCCCGEVF+9XVpjw9MTSq9noKOHH1qNQ3PbRjKXF4qLoK9TElq5mxKFubc3QywuRjFYhcori2TPdMntEkIIqRx01S3j5cuX6NKlC2JiYgAAJiYmyM/Px+3bt3H79m3s3LkT586dg7W1tXYLSgghhNQgbdu25f6Oi4tTat2VK1ciLS0NLi4u+PHHH9VdNEIIIYTUAPHZKQqn1RPpQEekwwXQhQLWksva2HnhVK9lAEqC6l+HbkIRK1a90DJ8FboJExv3xo6oc/ip9Xi0sPVQa/6EEEL40dAuEoqLi9GvXz/ExMTA0dERZ86cQXZ2NnJycrBnzx6Ym5sjLCxMqV5yhBBCCJHv8uXL3N8NGzZUat3t27cDAEaMGEFPjRFCCCGknEJxEU7F31EorY2BOU73/hEney2Dv52X0r2+9XX0oK+jhzZ2Xjjd+wf86j8JeiJdVYrNa8PTE8grLsDP9/ehUFyk1rwJIYTwo0C6hK1bt+LBgwcAgAMHDqBHjx4AAB0dHQwdOhTr1q0DAJw4cQLnzp3TWjkJIYSQmiA/Px8xMTFYtWoVRo4cCQBo1KgR+vXrp3Ae0dHRSEhIAAAEBAQgLCwMQ4cOhYODAwwNDVGvXj189NFHuH79ukY+AyGEEEKqvn0vQlDMxAqlHdwggAuGV5SmA+rxOW/x+4P9as2TEEIIPxraRcK2bdsAAF27dkW7du3KLf/oo48wf/58REdHY/v27ejevXtlF5EQQkgly3mWg+JM4UdyszKyKqk0NYORkRHy8/PLvd+hQwfs2rULhoaGCucVGRnJ/X3r1i0sWbIEhYWFMDIygrGxMeLi4rB3717s27cPS5cuxdy5c3nzys/PlypXRkYGAKCwsBCFhYUKl6k2KCoqglgshlgsRnGxeh9ZB8DlqYm8CT+xuCTIVFRURMd8JSvd37TfKxft79pD0YlGdUU6GNKgs9q3LxlQV/eQL8dib6GJdQP0dvVXS36EEEL4USD9/+Xk5ODq1asAgN69e8tMIxKJ0KtXL6xZswanT5+uzOIRQgjRgpxnObjleUtuuiwjCqQrw8HBAXl5ecjKykJ2djaAkpvYP//8M1xdXZXKKy0tjft78eLFcHJywsaNG9GzZ0/o6OggIiIC06dPx7lz5zBv3jx4e3tjwIABMvP64YcfsHjx4nLvX7hwASYmJkqVi6jHixcvtF2EWikkJETbRai1zpw5o+0i1Co5OTnaLgKpJM3qNEScAmOk93bx1+gEnpIB9VvJT/H17U1qyXfZ/T2wNDRFe3tfteRHCCFENgqk/78nT55wvXD8/Px405UuS0pKQmpqKurUqVMp5SOEEKJeivQ0z3lCP7A1oXRCbwBITk7Gjh07sHTpUvj7++Obb77Bd999p3BepXV36d///POP1MSlXl5eOHz4MDw9PZGQkIBFixbxBtLnzp2Lzz//nPs/IyMD9erVQ9euXWFjY6P4B6wF8vPz8ejRIxgZGUFfX1/t+RcXF+PFixdo0KABdHXV+xg84Zefn49Xr14hICAApqam2i5OrVJYWIgzZ86gZ8+eGjmniGxv377VdhFIJSgUF+FuyjO56Yx0DfD5ex9WQolKAuodHHyxpMUYfHN3q1ry/Cp0E5a2HIPOjk3Ukh8hhJDyKJD+/0rHVwUAZ2dn3nSSyxISEiiQTggh1ZCiPc2J5tnZ2WH27Nno1KkT2rVrh++//x7+/v7o27evQuubm5tzf3fs2FEqiF7K1NQUU6dOxTfffIPw8HC8fv0a9vb25dIZGhrKHFZGX1+fAltlFBcXQ0dHBzo6OhoNdOvq6lIgvRLp6JRMn6Snp0fHvJbQ9aZy0b6uHfa9CEFCbqrcdB3sfDXaG12WAKcmmJDdGxufnlBLfvPvbMWkxn0w0qOHWvIjhBAijQLp/y8zM5P7W+jxbcllkutIojFWFUdjrNZMNMaq9tT2MVZzn+eiOEv++Z4XlwdmzNS2XWbEgDy1ZVcr+fv7o2PHjrh06RLWr1+vcCBd8ga3t7c3bzrJZS9fvpQZSCeEEEJIzaPo+OhPM2I1XBLZRnv0RH0ze8y/s1Ut+a1/+h/czOyoZzohhGgABdI1gMZYrXpojFXtoDFWtYfGWJVDBGC3+rLLyckBPlZffrVVaVA8KipK4XV8fHygq6uL4uJiiEQi3nSM/e/GiVA6QgghNQNNFk5KKTo+et965Z9qqyydHZvgx1bj1TZm+vw7W/FT6/E0ZjohhKgZBdL/n+Sj4UKTzkguk1xHEo2xqjgaY7VmojFWtaemjbGa+zwXd1rc0XYx5NIx0tF2EWqE0puefPWrLEZGRujcuTMuXLiAx48f86Z78uQJgJIguru7e4XKSQghRHsUCZDnvcrDo4GP5OZFk4XXfIqOj+5sYoMhDTpXQon4dXDwVWsw/avQTfip9Xi0rtu40oesIYSQmoqupv/PycmJ+zs+Ph5Nmsh+DCo+Pl7mOpJojFXF0RirNRONsap9Vf16o8iPYADIf5UPUW7V7z0sYlW/jNpUeq0X6gl+7tw53LpVMm59ly5dlMp/7NixuHDhAq5cuYLr16+jXbt2UstzcnKwZs0aAECbNm1Qt25d5T4AIYQQjVK0XVD4thD3A+9XQolITbHvRQgSFRgfvbmNR5UINmsimG6go4cfWo2Dv52XWvIkhJDaTPs1RRXh7e0NHR0diMViPHz4EL1795aZ7uHDhwAABwcHmmiUEEJUQBN91j6xsbEYMGAAPvnkE/Ts2RP169fnguqxsbHYuXMnlixZAsYY6tSpg1mzZkmtv2jRIm7ItOjo6HI9yocPH45Vq1bh1q1bGDp0KDZs2ICePXtCR0cHERERmD59OhISEqCjo4OlS5dWymcmhJDajoLjpCo4FnsTisyKcy9V8WHlNK2Dgy9+9Z+Er0M3oYhVfK6vAnER5txajx9bj0cLy4ZqKCEhhNReFEj/fyYmJujQoQMuX76MkydP4osvviiXhjGGU6dOAQACAwMru4iEEFLlKfKjOecJ//BZpOYKDw/HlClTAAAGBgawsLBAbm4usrOzuTT169fHgQMH4ODgoFTeOjo6OHz4MLp3747Hjx+jV69eMDY2hoGBAd69eweg5CmN1atXo1u3bur7UIQQUosoGhjXNS95CpRumpOqoG+9NlgbcVyBdNobH12WNnZeON37B9xKfqqW3ukMJb3TlzQZBZq1jRBCVEeBdAmjR4/G5cuXceHCBdy8eRNt2rSRWv7PP/9w47eOGjVKG0UkhJAqi3qaEz5OTk7Yt28fLl68iJs3byIxMREpKSnQ1dWFq6srmjZtig8++AAff/wxjI2NVdqGg4MD7t69i1WrVmHv3r2IjIxEbm4u3N3d0a1bN8yaNQt+fn5q/mSEEFIzyAuSKzrmeCnfgzTBIakahjQIwNZnZ5BXXCBzuQiAk4mt1sdHl0VfR08tQ72wYjHSwl4i5eozDM08hs4eLeBgbYfOnTvDz8+PJmEnhBAlUCBdwujRo/HHH3/gwYMHCA4OxrZt29C9e3eIxWIcOHAAEydOBAD07t0b3bt313JpCSGk8lBPc1IRBgYGGDx4MAYPHqzS+osWLcKiRYvkpjM0NMTs2bMxe/ZslbZDCCE1jTon5lSGOFus1vwIUVX42xe8QXSgpKf2nCaDqsT46HxUDaYzxvD6zCPE7ruJ/NcZAEraSi+KLPA47yFOnToFFxcXDB8+HB06dNBE0QkhpMapurWFFujp6eHIkSPo2rUrYmJi0KNHD5iYmEAsFiMvLw8A0Lx5c+zcuVPLJSWEkMpDPc0B77+9YeLN/yBsakYq0LUSC0QIIaRW01aAnJDqpFBchF8f/AMdiCDmGSndSNcATes0qOSSKU/ZYDpjDNEbQ5BwJEwwXVxcHH766SeMGjUKgwYNUkdRCSGkRqNAehnu7u64f/8+fv31V/z777+Ijo6Gvr4+fH19MWzYMHz66acwMDDQdjEJIUQtqKe5Ysz9zWHiwR9IL3jL39OJEEIIUVRWeBb05PSMpck5CVHM0Vc3kJDzVnCy0bziAux7cQnDG1X9OVRKg+lzb2+SO4Fq/IHbUkF0q2aucOzTFHUau8AhwxDv4t7A8PE7PHsaCQDYvn07rK2t6cl7QgiRgwLpMpibm2Px4sVYvHixtotCCCEaU9t7mvse9IWRq5HcdLrmuoJBdEIIIaSicp/nAgDudb4HUS6NV0zU5+7duzh69Cju3LmDyMhIvHnzBhkZGbCwsICXlxf69OmDTz75BHXq1FE670WLFin0m/nZs2do1KgR7/Lnz5/j559/xunTp5GYmAgLCws0b94ckyZNQnBwsNLlKnU2IQxMgdPpWOyNahFIB0qC6b/4T8L821sgggh54vKdOQozcvFy13Xu/0af9oRDYMk8MbpFgF6RPiy8nVCvbTN4XvLA8eMlk7GuWrUKYWFhaNOmDdq1awc9PQoXEUJIWXRlJISQWkpeT/SaztTXlALkhBBCNEaRp76Akhu2xVm1u04mmrN582asXr2a+9/IyAjGxsZITU3FtWvXcO3aNaxYsQJHjhxBu3btVNqGvr6+YCBeKCD733//YfDgwcjJKXkC0sLCAm/fvsXp06dx+vRpjB07Fps2bVJpQsweTs2xIzFEsPe2CEDfem2Vzlub2th54USvpbibEoU5t9aXW/767COwwpJrimOfplwQXVLOqxRcOH8JeQlp3HvFxcW4dOkSLl26BCsrK3zwwQcYOHAgdHR0NPdhCCGkmqFAOiGE1ECKPBqe9yqvkkpTeZqcbgJ9G3256aiXOSGEEGUpExgHoNRTX40PNi6J6BGiZv7+/nB3d0fHjh3h5eUFKysrAEBWVhYOHDiAL774Am/evMGAAQMQGRkJS0tLpbfRvn17XLx4Uen1oqOjMWTIEOTk5KBDhw7YvHkzPD09kZWVhV9++QXfffcdtmzZAi8vL3z55ZdK59/PtS3OvXuIxJxUmWOk60AERxMbDGnQWem8tU1fRw9t7Lxkjpv++uz/5kZw+qB5uXUzIpMQ999dFOTzD02Ynp6Obdu2ISoqCnPmzIGurq76Ck8IIdUYBdIJIaQGqYmPhtMQLIQQQtRNmaC4iYeJ0sOh+R70Vao84mwxYKbUKkQB8toQtWGy8FGjRsl838zMDKNHj4ajoyOCgoKQnJyMY8eOYfjw4ZVWtgULFiA7OxsODg44duwYF+Q3MzPD4sWLkZSUhPXr12Pp0qWYOHEirK2tlcpfX0cPc94bjFk318pcLgbDnCaDoC+n80lVVnYSUsYY8hLSAQAmrjYwdpLeZ+8exiHrXDSYuOTGgmFdc3QK7Irrh84hOzsbFhYW8PPzw7Vr1wAAV69eRVJSEjp27Ag/Pz94enqq9HQAIYTUFNW3xiCEkFqitj8aTkOwEEIIUSdlg+L+kf5KD4cmzhYrWyyiIHU+fUaThQNt2/5vWJO4uLhK2252djYOHDgAAPjkk0+4ILqkuXPnYv369cjIyMChQ4cwduxYpbfjamYHEURgZXqk64hECHBogla2niqVvyrp4OCLJS3G4Ju7WwExAysuuf7oGkufJ4wxxGy7goaWTgAA6xZucOjdBPnGljA9Y4rs7GwAQP369fHgwQNkZmYCKBnD/vnz59yyvn37onv37jTkCyGkVqJAOiGEVGHK/tivTo+Ge//tDRNv4R+41MucEEKIPMr2Llc2KF7b5xRRhY6pcgE2GppNey5fvsz93bBhw0rb7pUrV5CbW/IkZe/evWWmcXd3h7e3N548ecKNl66sPx8fhggoP7ALA6b7fKB0flVVgFMTTMjujY1PT0DX1BDF2fnIiUuDuLAIOvolYZ+Mx/HIjU0FLJ1gaG8Jh95NINLRwXsm9XDt7VsAQG5uLnbu3Mm7nejoaPz5558ICwvD559/ThOSEkJqHbrqEUJIFaZSD7hq8mi4ub85/RgmhBACQPlguOR6yvYuJ5pn6muqcE9+Co5Xvvz8fCQmJuLYsWNYsGABAKBRo0bo16+fSvk9evQIfn5+eP78OXR1deHs7IzOnTtj6tSpaN68/BjdAPDw4UPub19f/qGQ/Pz88OTJEzx69Ig3DZ/wty9wMTFc5jIxGF5lJ8PO2ErpfKuq0R49Ud/MHsEt/0PKpacozs5HytUo2HXxAgC8PvO/fWjdyh0iHR3YGFog52ECiotLztXCwkIAgEgkQosWLRAeHo6ioiLo6+ujfv36iIyMBFByI+T27duwt7eHm5sbgoKC4OdXflJTQgipaSiQTgghlUzZycqqG+ppTgghVVtRURFEIhE3eVxqaiouX76Mt2/fon379vj999/Rr18/3gCYuqkSDC+tQ6h3eeWTN+441fFVl5GREfLz88u936FDB+zatQuGhoYq5ZuSkoLU1FRYWVkhIyMDkZGRiIyMxKZNmzBv3jwsWbKk3DoJCQkAAGtra5iY8B8vzs7OUullyc/Pl/pcGRkZAICNT/6DkY6ezIlGRQBWhP+L9R1nVusx0stqZ+uN72bNw+zQKQCANwfuwr5FA+ibGwOpeTA2NoaBgQHqeDoB0EUXUy+sObJc6ru3tbXFl19+CVdXV/z444+4d+8eAMDY2FgqHWMMSUlJSEpKws2bN+Hs7IzJkydDJBJxgXlSOcTikuF8ioqKuJshpHKU7m/a75VLm/u75tQYhBBSDWh6srKqgHqaE0KqioKCAsTFxSE3NxfGxsZwdnaGoaEhMjMz8fjxY2RmZkJfXx/Ozs5o2LAh8vLyEBYWhtTUVIhEItja2qJ58+bQ1dXFgwcP8Pr1axQXF8PCwgJNmzaFubk54uPj8fLlS+Tl5cHU1BSenp6wtrZGbm4uXrx4gezsbBgaGqJevXqoU6cOxGIxEhIS8O7dO+jp6cHOzo6bQC81NRWpqalgjMHKygp169bl3k9KSkJBQQEsLCzg6uoKPT09xMfHIyoqCnl5JcERDw8P2NnZITQ0FOHh4cjKyoKBgQFcXFxQv359XL58GdeuXePGwTU3N4eFhQWSkpJQXFwMMzMztG/fHsuXL8fChQvRrl07fPXVV/jgA/nDH6jaoxygYHhVocjk3hQkr94cHByQl5eHrKws7jrQtWtX/Pzzz3B1dVU6Pw8PD/z888/44IMPUL9+fejr66OgoAAXL17EvHnzcOfOHSxduhTW1taYPXu21Lql428LBdEll5eml+WHH37A4sWLy73fL8dTOP8s4MzJ04Lbr46cRJbYvXu39JtiAAs7lk9cCHz55Zfl3s7Pz8ezZ88QHByM4OBgpcvw4sULpdchFRcSEqLtItRaZ86c0XYRapWcnBytbZsC6YQQUomq82Rl1NOcEKJNqampuHr1KlJSUiAWi2FlZYXmzZsjKioKp0+fRlxcHAoLC2FmZoZGjRrBwMAAYWFhUg1tAwMD1K1bF69fv0ZRUZFU/mZmZsjPzy/Xw0VfXx96enrcWL6ldHV1YW5ujvT0dKn3dXR0YGdnh9TUVBQU/G8iQ5FIBFdXV2RmZiI1NVVqnfr160MsFuPly5dS7zs5OcHQ0BAxMTFg7H89Kk1NTWFiYoI3b96U20/6+voK99LJzMwUDE5dv34dAwYMwJcTvsT8KfOhZ6En8xpfkR7lRPMoQE5KxcTEcH8nJydjx44dWLp0Kfz9/fHNN9/gu+++Uyq/4cOHl3vPwMAAgYGB6Ny5Mzp37ozQ0FAsWrQIEyZMgKWlZUU/gkxz587F559/zv2fkZGBevXqYbPoFsRyeps7mdpgc6fZgmmqo1evXiGgWxekvnnLvadjpA99pgNvb28U6THkpWeXW699+/aYMWMGgJIe57Nnz5Z6GqBOnTro27cvjh8/jrf/P656165dce3aNeTn58PQ0BDffPMNlixZgvz8fOjr66Ndu3b48MMP4eDgoOFPXXvl5+eXfOcBATA1NdV2cWqVwsJCnDlzBj179oS+vvy5Poh6lF5/tIEC6YQQUgEV6YFX3VBPc0KIOonFYiQlJSE7OxsGBgawt7dHUlISTpw4gXv37mHWrFkYNWoUjI2NYWxsjLi4uHKPim/btq1cvmlpaQgNDZW5zYKCAsTHx8tclpWVJfP9wsJCmYHp4uLickF0yc9VFmOsXKC8VHR0tMz3+YYyyM7O5nqTyiqvPI6OjkhKSpIKzgOAA0qCHOMwDodwCC9Q0qPw540/I3NjJoZgiMwgOPUorzianJNUNjs7O8yePRudOnVCu3bt8P3338Pf3x99+/ZVS/5GRkZYtmwZ/o+9+w6PouoeOP7d3fRKAoEQSICEEroghE7oTRBQioCIFF+7iCKioAKCKIjgay8URVGavPQOofcaaqQGSAIpQBLStszvj/x22CUBkrBp5Hyeh8fd2Zk7d1fYu3Pm3nM6duxIcnIymzdv5plnnlFfd3d3Bx4+q9D8unn/7Dg6OmablkavMWLUaO57nAboEhDyWAa/goKC2LYljG5PPUXE2bOZG1NTcXR0JCMjg7TENHW88PLy4ubNm0BmIN2c9uv06dNW41PlypWZPHkyHh4e3Lx5kyVLlgCwbt26LOc3p9tJT09n48aNbNu2jTZt2lCuXDlKlSpFSEhIvt1YKYm02swxxM7O7rH8+1wc2Nvby2dfgArzs5ZAuhBC5NHjUOAsJ7PUQC7EhRB5l5GRQXR0tJr6xMnJic2bN7Nu3Tqr2SQajUYN7JoDIhkZGQ+cMW3Jzc0NOzu7LMFte3t7goKCOHPmTJZjqlWrxvnz59XcomZeXl5kZGRkCVbb2dnh4+NDdHR0lraqVq3KuXPnsmyvUqUKkZGRWW4ClCtXjqSkpCxBJHd3d+zs7NSghuX7KF26dLZBej8/v2yD7p6enty+fRuw/nyvX7+uPq7oW5HomGiMGIkhs+0udKE73VnMYn7gBwB+4Rfa016C4PnEqaITXIMntj+B3UNm0MqYLGwpJCSEli1bsn37dn7++WebBdIBmjVrpj6+N9WHn58fkHnzMyUl5b4pWMw3P83750Y5Zy9iSM42R7oWDeVdStMvsHWu2y0ugoKCOH7sGEuXLuXzWV8SfuCI1evVq1enT58+HD16lDVr1gBQqlQpIHP8nT17ttX+o0ePxsPDw2o/S5UqVVLHYI1GQ6tWrdizZw8Gg4GMjAw2bLibRsfOzo4WLVrwzDPPUKVKFRu9YyGEyH8SSBdCiDx6HGbgOQU44d7w/jN8hBAiryIjI1mzZg1btmwhLS3toftbzo42z6zSZDOTsEqVKtnO4L53Rrmrqyt37txBr9dbBdFr1KjB2f+fnffvv/+q2+vWrcu1a9dISEiwCmJXrFgRNzc3zpw5g8FgUIPoLi4u1KxZk0OHDgFYBdHr1q1LeHg4YD3bvGLFisTExGAwGLh+/bq63dPTE71eT0pKitWNAycnJ9zc3IiLi0Ov16tB9HtvDpiD6I6OjnTv3p0VK1ag1+vVILqHhweTJk3i119/5cSJE+qNg0qVKvHZG58R9l4Yc5ijnjeJJLzxph/9iCGGZSzDgIHVrKYTnbJ89iKr3M4w17llzgB1q+8mM9pEgTMX9MzuZmB+qVOnjvr45MmTNG7cONv9Tpw4AUDt2rmvG/RycHc+ifgr29dMKIyu1+exKjSaHUdHRwYOHMjAgQNZHb6dV956A/5/mImKiiI9PR1nZ2d1/7i4OOzt7fnpp5+IiIhQt9etW9cqj35kZKTVef7zn//w1FNPsWDBAiBzTN+7d2+WNGpmBoOBbdu2sXPnTpo3b06FChVwd3fniSeewN/f31ZvXwghbO7xHjWEECIXHoc0Lbm+cHfX5VNPhBAlxZ07d9i6dSubNm1SC2La29tnySmenYoVK5Kampolz6E50GsOrleoUIGoqCgURbEKTA8YMACj0ciiRYusjh88eDBdu3blww8/tMoJ3LVrV1599VVWrFjBr7/+qm6vU6cOkyZNIiYmhrfeektd7u7h4cHUqVNxdnbm3XfftUrN8uGHH1K3bl2++uorq+Jeffv2ZfDgwSxdutQq9UytWrWYPHkyR44c4dNPP1W3lypViq+++orU1FRGjRql5lXXarV8+umnVKhQgXfffddqFvzo0aNp2rQp06ZNY9euXer2V155hfbt21O5cmVmzJhh9XkEBgYybtw4XnjhBfX9dejQATs7OypTmSY04TjHAdjCFvrQB4D+9Gc5yzFhYhWrmKXMoiTK7XjpWtuVkIiQHP+usK9sD2fz2jshHo15tviD0qfkxd69e9XH9846btmyJc7OzqSmprJu3bpsA+mXL1/m9OnTAHTqlPubePVLB9KmfH22Rx+3mpWu02hp7VuXRmWq57rN4uypuq05vHQLr7/yGqdOnSI5OZkZM2bg5uam7vPNN99km26nYcOG6mNzENysS5cu6kqGevXqqdvNY42zszMajUZtt1WrVuzduxe9Xo/RaGTHjh1W56pbty49e/YkJKToreYVQggJpAshBI9HmhaQpeFCiPyVcPsm69auIyrqGoYMA5cvX2bXrl2kp6db7Wc5A02j0VCvXj3Cw8OzpFC5evWq+tjT05OOHTuybNkyq32qVq3KtGnTOHjwIJ999pm6vUqVKjz33HNoNBoiIyPVgI2LiwvPPPMMOp2O119/nffee0/tR//+/QHo3r07v//+uxq0bt++PTqdjgoVKhAQEMD58+eBzNnr5hyujRo1UgPpXl5earDg6aefVgMKGo2Gvn37qtv/+OMPNaVL3759sbOzo3HjxpQuXVq9edCsWTPKlCkDZM64PHLkiPr+atSoofbvjz/+ADJn2jdt2hSNRkOfPn3UQLp5GT1A69at+eqrr9QbEYGBgeqxbm5u6ox7y2BJU5qqgfT97FcD6eUoR21qE044scRyO/k2HnhQnOU2KG4eL3MTGM/t+JrTArFC5IbRaESr1Wa7usds8+bN7N+f+Ru4TZs2OW5bUZQHtpuens64ceOAzO+e9u3bW73u6urKs88+yx9//MEPP/zAW2+9lSVn9hdffAFkBvh79eqV475ZejX4KcKij1lt02m0vFGrZ57aK+7cXdx48803+emnn9R6IpYruizHBRcXF/W5nd3d64rVq1er475Go+G5555TXzPnVzdr0qQJb7/9Nlu3buXnn38G4NChQw/8zgsPDyc8PJyaNWsSHByMi4sL1atXp379+uqKNSGEKCwSSBdCCIpumhZZGi6EKEx6U2ZA/Ny5c4yfOpHlC5diTNXj6OhInTp1SEtLs7oYLlWqVJYc5Yqi8O+//6pB9JYtW+Lm5palONmwYcNo27YtVatWZebMmer2Z555Bjs7O5o2bYq/vz9XrlwBMoPD5iBOtWrV1EC6nZ2deiFvuVxdURR1lrxWq7UK6lumnrG8SLcMElk+tsx3npiYaHWO9PR0nJycyMjIsNrP8rHluS0L5Dk4OGR7Pst9MjIy1NfMNwLM575+/Tr+/v7o9XqrVDkXL16kWrVqWdqyzLdemtI4kVkz4za3seTJ3eBWatrDVxoUdXkNisvNZ1HcXLlyhV69evHqq6/SsWNHqlSpon5/XLlyhT///JPJkyejKAre3t6MGjXK6vgJEyYwceJEIPN7pHLlyupr27dv59NPP+XFF1+kTZs2VKxYEci8KbR9+3Y++OADNVD78ccfZ5tTe9KkSSxbtozo6Gh69OjB7NmzqVatGnfu3GHGjBn8+OOPAIwfPx4vL688fQZbo49n2Ta8RhfKOmftT0nh5OTEmDFjOHPmDGvWrGHfvn1W45JWq6Vbt2488cQTTJ48GYAzZ87QvXt31q5dy5w5d1OBBQUF4e3trT7fvn27ekPG29ub999/Hzs7O0JDQ9VAujk4r9Pp1MKlkPn74Pjx4+q4evr0aXVFAoCvry/dunWje/fuVoF9IYQoSPLtI4QQRZhTgJMsDRdCFBhz4FxvMhCecIkPDs4h/uAFTn6+AmPqg2fMOjo64uHhoQbSW7duTXh4uFpIDjJze7/yyit4eHjg6OjI8uXL1e0tW7YEoHnz5nz//fdqu66ururjMmXKqIF0y1QnlgHoxMRETCYTWq02S7HQhIQEKlSogMlkspo1bzkb737Bb8tgu+Wx954jJSUFT0/PLEVSLWf5Wd58sHxsGfy2PLfljH+9Xq++P8tAuuX7uHfGnuW5LZfw39t3E9YrBsyiuFvI1MOtaM1Gz8vscpCguCg5jh07xiuvvAJkfld6eHiQmppq9e+/SpUqLF26FF9f3xy3qygKmzdvZvPmzUDmjUtXV1du376tfq9ptVrGjh3LmDFjsm2jSpUqLFq0iL59+7Jjxw6qV6+Op6cnycnJ6nfgiy++qK4syq24tETmRKzLsj3Ut36e2nucaDQa6tevT/369UlOTiYmJobvvvtOLcC9atUqTp48iaOjI+np6ezevZvhw4eTkJBg1Y5lEViTycSuXbvUQLr5RjhYjz2QWaPjo48+IjY2lg8++ACAw4cPZ5tWxiwmJoY5c+awZs0aGjVqhKurKxUrVqRZs2ZWvwOEECI/SSBdCPFYeRzynN8rN/2UpeFCiNwwB84BDsed44ODczCajGou2dsnr3Fi8v9Q9Jnfq1one3xa1yD58N0iY3Z2dhgMBtLT09XiYwEBAbz77rvcvHmTUaNGqbPNqlWrhodHZiC2R48eaiDdYDCQmpqqrqSxXFFz7NgxGjRoAKAeC6iFNIEsF9AZGRk4OTlZzUi3pNVq1X7fe7zlLDfLJer32+feczs5OT1wu/n8ZpYzxC23Wz62PFaj0agzSu8NTJjbsrOzw8nJKdsir+XLl1cLCloGRKKIIoPMwLw3d2cXnuEMF8jMn1yNarg6u2JreQ2GQ95nlwtREvj5+bFo0SLCwsLYt28f0dHRxMXFodPpCAgIoH79+vTs2ZOBAwfe9/vyfurWrcuXX37Jnj17CA8PJy4ujlu3buHi4kKtWrVo1aoV//nPf6hbt+4D2+nWrRvHjx/niy++YOPGjURFRVGqVCkaNmzIyy+/zLPPPpvn9z83Yj1GxfoGoRYNP51ZxaQnh+S53ceNm5sbVatWZcqUKUydOpVjxzJT4VjWJFEUJUsQHaxXNi1cuFAdd8yz2s0sC9nqdDomTZqEl5cXPj4+lClThri4ODWIrtFo8PX1VW+Y169fn1u3bqnp1WJiYli1apVV/9u3b0+fPn2ypAcSQghbk0C6EOKxkZc85wV9Qf0owQIhhHgUlkFzuBs4VxQTiqJgtCjEBqAYTUTMXKcG0b2bBlF9ZCdun7rGrW3/AuBaxYepH05i3g+/sG/fPvXYjh07otFo8Pb2pmPHjmoxUHNAHawDyIqicPLkSZo1awZYF7yzPMZyab9lkLh8+fJWfTcHsX19fdFoNOpMb8tAkbOzszpr3HL2tyXL7Zaz0C2D5C4ud8cRnU6nXsR7eHhYnducB928n5llwNxyRroly8/DwcFBDaTf+/4sP1NnZ2f1M7p+/bq63XK2X1RUFAm3MwMje9ijbm9J5uoAI0ZmM1vd3pP8ySn8qMFwCY4LkT0HBwf69u2r1m7IrQkTJjBhwoRsXytdujTvvvvuI/TurqCgIDXthy3tuXEKk6t1yMOEwtboYxyMiyhxxUYfxsXFhQkTJhAWFsbq1autgt9mbm5u9O3bl40bN3L16lUiIiLYv38/u3fvZsuWLeo45OHhYXXT2bL4dvXq1dXx3PLmMGTWS/noo4+oUKECL774Iunp6Zw4ceK+4zRkrsZavnw5W7ZsoW3btnh6euLj40NISIjVGC2EELYggXQhxGOjqOY5tyQz54QQBSW72ebK/8/Myy5wfq+EQxdJv56Zp9Q9uDzB7z+F1k5H3I4IdZ9SDQIISzjB+++/z/Dhw9Wgt2XKkeDgYPVxdHQ0SUlJuLu74+bmhk6nUy+OLQPm5ly7ADdu3FAf+/v7q49TUlLUYnf3pjKJjIykcuXKREZGWgWnDx8+TNWqVYmOjrZK5xIeHs4zzzxDcnIy165dU7dfvHiRO3fu4OjoyMmTJ9XtN2/eJCoqCj8/P7XYJ2QG3g8dOkTjxo3Zt29ftueOi4uzyqt++fJlFEXBZDJZnTs2Npa0tDScnJw4deqUuj09PZ3Tp09Ts2ZNdu7caXWOJUuW8NZbb3H8+HGrz3PTpk2EhoaiKAqHDx+2+gwnfTeJ2tTmCEdwJXO2eStakUACs5jFQQ4CmXnS22NdLNDMFjeJZbwTQtiaFk22Cau0aPjy+BLmtxmDvVZCIpZ0Oh3t27enffv2XLhwgUuXLnH+/HnWrFmD0WgkOTmZuXPnWt3gNedRt5Samoper8fe3p64uDirMXTAgAHqY5PJpBbfBhg3bhzVq2fe4AgJCWHHjh1WQXQ/Pz+iojLTjZUtW5aAgAAOHTqEoigkJSWxYsUKdV9HR0fatm3LM888k6u0RUII8SAyagghRAGTYIEQIr/oTQar/Ob3m22eEzFrw9XH/v2boLXLDH5m3LybV9e5ojfhNy/SpGwN6tSpw44dOwDYs2ePOgOyQoUKVu3eunULd3d3dDodlStX5vz58wDqf8F65vjly5fV4PvRo0fV7ampqfz999/07duXBQsWWJ3js88+Y+zYsXzzzTdW2//8808cHBzYsGGDVQD6yJEjzJgxg2vXrlnlDU5ISGDChAm4urpa9c9oNDJhwgQaNWrE2rVrrc4xc+ZMBgwYwPz58622//XXX3h6erJq1SqroMCBAwdYsGABN27c4OrVq+r2xMREpk6dypNPPsn69eut2poyZQr9+/e3muEHsHnzZm7dusWZM2estqenp/PBBx9YzV53dXXlzp07RMdGE0201f4/8AOb2ISBzJsxOnR8xEc44XTfILjcJBZCFDWm+4x9JhSiUuJYdGE7g6q2K+BeFR+BgYEEBgbSrl07mjdvzrRp09SbtPfWAYHMtGxBQUFA5rizZ88e/Pz8mDZtmtW4V6VKFfXxihUr1EKnrq6uVjffLWm1WkaNGkVoaCijR48mIiKCGzduEBcXd9/VXOnp6axbt46tW7fSs2dP/Pz88PT0pHbt2lYp04QQIjckkC6EKFKKU45zSdMihCgKzDPPD8ed4/0Dv2bJB5tXSRGZeU/tPJzxalDpgfsuu7SLBk82VAPp5jymkJl2xdPTU81pfvLkSXVmufniGWDXrl306tULb29vDh06RMeOHYHMJdsfffQRoaGhavtmf/31F+vWrVMv7M0z3GNiYhg1apR6ce3i4qLOYJ8zZ456vLu7O8nJySiKYtW2s7MzJpOJ9PR0zp69W8FZp9Ph7u7OrVu3suRo9fHxITY2luTkZH755Zcs241GI99995263c3NTZ0Vv3DhQnW7VqvF3t6e9PR0jhw5wpEjR9TXzHlkExMTrc5RqVIl9TM/dOiQ1bnNs/0sgxiWy+2zs5e9ahDd1dmV3774jU4tOj1w7C3sMVkIIXJDAVZd2SuB9ByqXbs2v/76K7t372bNmjWcOXMm2wC25Y3wb775xqpYtllsbCyenp4YjUbWrFmjbm/VqpXVfqdPn1Yf9+rVi9DQUABCQ0OJiMhcHWf+HeHu7k5qaioGgwGdTkfLli3ZtWuXWsPFnGIOMn8TtG/fnu7du2dJDSeEEA8jgXQhRJFRHHKcW5IZeEKIwmIZPP/g4BxMJmOeZp0/iDE1Mz2Lg7crGt3d1CkO3m6kkZluJeVqAq7VyxGfnoQSGKjOeNbr9SQkJODt7Y3JZLK62P7jjz8IDg5Gr9dbFTJLSUnhvffew8PDQ50Vbg4oX7hwgQsXLqj7BgQEqIVNLVOYlCpVioSEBBRFsTqnZV5wS9nNqIPM2e7ZfiZGI7du3cr2tdjY2Fxtt0wtY8kcwM9OXFxcttstb1zk5NwGg8Eq57vlTHUzHx8fRowYwauvvmqVUkcIIR4HGqC7f9PC7kaxYm9vT2hoKKGhoZhMJlJTUzly5AgzZ85Er9ej1+s5ceIE/fv3B7Aay7y8vNTxeuvWrZQvX55vv/3WqlipubA4oBbGhcwxyjLP/715z7t168awYcP47bffWLlyJUajkSNHjliNc5ZSUlJYuXIl69at48UXX6RmzZq4uLjg6+ubJVWcEELcSwLpQogiozjkOL+XBMeFEAWlIILnlnRO9hj0RvQ376CYFDTazGJgPq2qk7g7M6h96/AlXKuXA+DIrfPY2dmh1+uBzJypEyZM4OzZs1Y5wRMTE3nnnXesZkWbL7BTUlJISUlRi5U5OTllG3A2B9HvZZln9X6yCxpD5mzze4P+kDlDXKvVZntBbmdnl+12rVZrNdv+YefOi/udw9XVVZ2BZ3neevXqUbp0aSIjI7l16xYajQYfHx/atGlDaGgoUVFRalHSRYsW0bp1a6vipUIIUdw8KEd6eZfS9AtsXeB9elxotVpcXV1p2bIlVatWZfXq1WzatCnbMVGn01GzZk3279+PwWBgzZo1rF+/3qqeCliP4X/88Yf6OCAgAFdXV/W5ZXHzwMBAXn75ZTQaDaGhoaxcuRLA6neHo6OjOiY2bNiQ48ePYzAY0Ov1Vqu7SpcuTZcuXejUqZNVcXMhhLAkgXQhhEDStAghii69yVBgwXNLbtXKcevwZfS3U7l19DJeDSsD4NWwMo4+mUXGUi7Fk3DwIt6NquBzQ6sG0QHOnTvHK6+8YhXsNacmuXdGtOWsckuWM8azC0K7u7tTvXp1bt++zY0bN0hPT8fNzY369evTtm1bIiMjiYiIIDU1FWdnZ6pWrUq7du24fv06R48eJSkpCXt7e/z8/GjevDnp6ens37+fmzdvotFoKF26NE2aNEGn07Fv3z5u3LiBwWDAw8ODJ598ktKlS7N3714uX75MWloarq6u1KxZk9q1a7N//35OnTqlFiutXLkyLVu25NSpUxw8eJDbt29jb29P2bJladu2LXfu3GHbtm0kJCRgMpnw8vKiRYsW+Pn5sXXrVqKiotDr9bi5udGgQQMaNWpEeHg4586dIy0tDWdnZ6pVq0a9evUwGAxcunSJ5ORkHBwcKF++PKVLl37g/++goCAqVKjApUuXaNasmQTRhRDF3oNypI+u10cKjdqIr68vw4cP5/nnn1fTsfTv358lS5aQkpKC0Whk9+7d6v5Go1FNN2Zvb6/+dti0aROdO3dm8eLFVunWatWqpT5WFMUqhVnfvn3RaDJv9JcqVcqqX5UqVWLMmDGcO3eOmTNnAnD9+vX7zlSPj4/nzz//ZPHixYwaNYpmzZrJDHUhRBYycgghbCI9PZ3t27ezefNmkpKSePPNN2nfvj1t2rThlVdesSoqUxRJmhYhRFFiOft87IHZGJSCX4FTvks9bh3OTBlyZeE+StUPQKPTotFpqfhsY9iZmd4lZu1xTDFJpNy6G7Aw5/++dzb5/VKTWPL19UWny7xZ6e7uTsWKFWnXrh1t2rTh6tWrxMbGYjKZKFWqFLVr18be3v6+bdWvXz/b7e7u7lStWjXLdicnJzU3+73MuVnv1apVqyx5XQFatGhBixYtsmxv0qQJTZo0ybatmjVrZrvdvEz+Xg0bNqRhw4ZZtjs4OFC9evVsjxFCiJKiWdla7E05Z1U7RKfR0tq3Lo3KyHekrTk6OlK3bl3+/fdfevfuTZs2bVixYgWbNm26bzqzoKAg4uPjiY2N5cKFCwwePDhLOjbL51u3blUD746Ojlbj7KlTp6z6MnnyZDw9PSlXrhz//e9/MRqNXLt2Td3HXFcFwN/fnytXrgCQkZHBF198AWSOp4GBgXTp0oWWLVvi4ODwKB+REOIxIIF0IUSemWcZrlu3jvnz56s5bd3c3ACIiIhg//79TJ8+nV7te/Hf8f/F092zyAaii2KfhBAlS2HNPs+Od0ggjj7upMcmkXgqirPT11D1rY7YuTji0yYY+zvORO/JLMR548jdXOc6ne6+Ob7N7O3tqVatGp6enqSnp+Po6Ii/vz8dO3akXLlyGI1G/v33X3755Rc1qA6Zgeb7BZuFEEIIS0Ord+bAsQtZAulv1OpZiL0qOcqUKcOwYcN4/vnnOXz4MPHx8SQlJbFx40a1hseZM2esjrEMmptnq+/Zs4ekpCT27dtnVbTb19fX6tglS5aojxs1aoSnpyeQGQx3cHBQ65/odDqGDBlC9erVGTt2LJA5S7506dJZUsRlZGRw5swZzpw5w+zZs3njjTdo1qzZo340QohiTALpQohciYyMZO3atezcuZPbt29nu48TTgBoyVwKpygKyzYt49imY8xiFu64F3qhUCGEKGr23ThTaLPPs6PRaak2qjMnP/4HxWAibte/3DxymbLtalGmdgD+Pl44+3mhv3zD6jjLJduQeaEbEBCA0WjExcWF4OBg2rZtq950FUIIIfJDGScPhlXvwk9nVqm3pYfX6EJZ51KF2a0Sx8HBgaZN7xZ27dWrF+vWrWPt2rVER0dne0zp0qUpXbo0ERERZGRk8NJLL2WZqa7X61EUBY1Gw+XLl9UZ5QBDhw5VH6elpVkVER89erQ6k71y5cpcunSJqKiobPvh4uKinjcpKYmpU6dSrlw5vL29KV++PB06dKB27dpqehkhxONPAulCiPsyGAxcvnyZpKQkTCYTK1eutMpJl50a1KA/mUvQf+RH1rOehSwkmWQucIEJTOBLvsyXQqGS51wIUVztijnJ2IOzC7sbWZSq60/ND3pwZtpqTOkGjCkZRK86SsLG0zjUqUNaWpq6r0ajwcnJCZPJpOYQ79atG5UrVy68NyCEEKJE6xfYmpWRe7mWEkdF1zL0rSIFRgubk5MTvXr14umnn+b06dNcu3aN1NRUdu3apc5Qj4+Pt5odbhlE9/DwIDExkaioKCIiIjAYDEybNk2to6LT6Shbtqy6/4IFC9TH5vojls8vXbqkPq9RowY9e/bkq6++wmAwoNFoaN68OXv37lVXY1+/fp3r169z+vRptmzZgr+/P8OHD8821ZoQ4vEjgXQhRBbx8fGsW7eODRs23LcAnL29PYqiZCnWcpazLGEJL/IipSjF8zxPa1rzNm9zk5sc5jDHOEYjGtm835LnXAhR3OhNBvbfOFskg+hm3iGBPDFrENeWHSI27AymDOvvfUdHR0JDQ+nduzcVKlQopF4KIYQQWdlr7XivXl/GHpjNe3X7SoHRIkSr1VK7dm1q164NwNNPP82+fftYvXo1x44dy/YYFxcXqlSpor7+ySefZJmpbjQaSUtLw8nJibS0NDZt2qS+1q1bN6t9LYPoDRo04KOPPsLOzo49e/awY8cO7ty5Y1UoNTtXrlxh4sSJ1KlThwoVKlC6dGlatGhBxYoVc/xZCCGKDxlFhCjhMjIy2LVrFxEREaSkpJCYmMixY8fuW83crGLFily8mJkTt379+tSqWIslq5egR08kkVb7BhDAq7zKZ3wGwP/4H8MZni/vR4LjQojioqilcnkQl4reVHuzI1WGtiJh/wWUW2lUcA+gY/UmNG/WDFdX18LuohBCCJGtJ8tUY03nyRJEL+I0Gg1NmzaladOmREVFcf78edLS0ggPDycsLAzInJluGWS3DKKXKlWKW7duAbB9+3YaNWrE9OnTrQqdWhb7vnLlitWksVGjRmFnl/l3pHLlyuzYsUN9zc7Ojnbt2rF3714SExPRaDR0796dzZs3k5KSgqIohIeHEx4eDsCff/5JvXr16NevH/Xq1bPdhySEKHQykghRwuj1ejIyMtBoNCxatIgNGzbct4q6RqOhZs2aagV0jUajLpkzB9EhM89drVK18Fztya/8qm6/yU3Kkrmsrg1t+IEfuMlNdrKTlNQU3HHPr7cphBBFVnGYhX4/dm5O+LWvg71RyxD3UKqVCcDBwaGwuyWEEEI8kATRixc/Pz/8/PwA6NixIz169GDFihXs2rUr2wlf9vb2BAcHs3fvXgDmzJnDDz/8YFWzBbDKlT579t3fYVWqVKFUqVLqc8t8646Ojnz22WdUq1YNLy8vFi5ciKIorFq1Sr02zs7x48cJDw+nWbNmBAUF4eHhQYMGDazSzgghih8ZTYQoAW7dusXGjRvZsGED169fz/FxiqIQGXl3dvnQoUPx8fHh22+/5c6dO+r2evXqYYg04I8/LWjBITLzqG9mMwMYAIA99tSlLtvZjhEjsTdjKUc5q/NJjnMhxOOuOM1CN7PTZBaO1mq0TG00jAZlqpKWlsaZk6cLuWdCCCHEw+lNBgmkF3PVqlXj3XffZcSIEZw+fZqkpCQuX77M2rVr0ev16PV6NYgO1jPVzSleAPbt20eNGjVYvHgxhw8fVvdp3fpu7nxFUdi/f7/6vH///lSrVg1ATUNj3g8yi6rr9Xo1p3unTp3YvXs3ycnJKIrC7t271fQwGo2GRo0a0bNnT5mpLkQxJaOJEI+ZpKQk9uzZQ1xcHEajkcuXL3Po0KEsd+MtOTk5Ua9ePfUHQ9myZUlISMBgMKiz1e3t7enQoQNubm74+PgwevRo9Xi9Xo+GzErlIYRwmMwfJXvZqwbSAbRo1cfmYi2WJMe5EOJxVlQLit5LiwYTCnYaHV80Hk6DMlXV18yBCKMEJIQQQhQDh+L+ZeyB2XzReDgNy1Qr7O6IR+Tp6UnTpk3V5507d2b58uVs27aN9PT0LPvb29vTtGlTduzYgdFoZPXq1ezatSvL5DInJyf18d69e9VAvE6no2fPnupr956jZ8+eDB06lLCwMGbNmgXAjh07rGa+W1IUhQMHDnDgwAGaNm1KnTp1cHZ2Jjg4GH9//9x9GEKIQiFXQUI8Jq5du8bSpUvZtm0ber0+2300Gg1lypQhNjbWantaWhoHDhxQ95kyZQomk4mPPvqIGzduAODu7o6bmxsA1atXx8fHR21n8eLF9GvZD4BSlFJTttzilnoOI0ZO8f8pYtDg4+2TbR8lOC6EeBwV5SB6djPOzbP3ZAafEEKI4kpvMjA9fDFpxgymhy/m99AxMq49Zvz9/XnjjTcYOnQoR44c4datW0RHR7NhwwbS09PR6/VqfnXIDIRnt0I7PDycp556isuXL/PNN9+o26tVq4a9vb36fP369erjSpUqMWzYMHWWuZk5iK7T6fD09CQhIQGAkJAQTp06pU5U27t3r9Us+tq1a9O9e3eaN2+ORqN5xE9GCJFfZBQRohgyL2Pbt28ft27dQqPRYDQaH5ijDTLvgFsG2V966SW2bNnC+fPn1WPr1KlDuXKZKVc++ugj3nzzTQBu376N0WhEp8tMp9KuXTsWLlwIZBZzMQfSAUxkzjY3z1IH2MMebpAZlA8hBDcXt0f6DIQQorgoSkF0c9AcrAPnZuYAgwQahBBCFHcrI/cRlRIHwLU7cSy+uJ2BQe0KuVciP7i6utKyZUv1ebdu3Vi8eDE7duzIdpKZvb09LVu25NChQyQmJrJ3715mzJjB7t27rfY352mHzCD8kSNH1OfPP/+8GvC+t15M5cqVGTduHAkJCbz//vsAVkH07Jw8eZKTJ09StWpVGjRogJOTEwEBATz55JNqEVQhROGTf41CFHHx8fGcPn2aO3fuYDKZ2LZtm1r8836CgoJITExUZ4y7urqqOc3Nlczr1KlDjx496Ny5M6NHj+bSpUsAVilgKlWqhIODAxkZGRiNRnbs2EGbNm2AzGV05kB6bGwse47uwQcfYoklmcwfCN54Z74H4vmO79R2e3J3eZwQQjzOCjuInt1sc0sSMBdCCPG4WnQhDMU587ECzD67jg5+DSnrXKowuyUKQIUKFXj77bcZPnw4O3fuJDY2lhs3brB3714yMjLQ6/Vs3bpV3d98nX2vixcvoigKJpOJb7/9Vr1WdnBwICQkRN3PMsBuZ2fHp59+iqenJ2XLlsXb25uEhASrIHqpUqXU6/LatWsTHR2tzlw/d+4c586dU/f18vKic+fO9OzZE1dXV9t8QEKIPJOrJyGKqJMnT7J8+XL279+fbT5xQF1mdu9d9vPnz6uPg4OD+eyzz/j999/53//+p243D/wODg4MGDCAqVOnApk/FkwmE1ptZvClXr16HDx4EICtW7eqgfSMjAyrc379+9e0oAV3uFuEtBnNWMta5jKXWDKD+tWoRgghUihUCPHY23fjTKEF0R+U31wIIUTRcPv2bWJjY4mPj8fZ2RkfHx/KlCljlUpC5J1RMYFFjSajYuLbU8uZ9OSQwuuUKFDu7u507dpVfX716lXmzJnDoUOHsl3NrdFoqF+/Prdv3+bixYtcvHiRZcuWsXfvXs6cOaPu5+3tbZV+5a+//lIfN2zYEE9PT7U9y/O4uLgwZswYAgMDGTp0KEajkYiIiPumZgW4efMmf//9NytWrKBq1ao4OTnh4+ND27ZtqV69et4+GCFEnskVlRBFQGxsLBs2bODChQukpaURHx9PVFTUQ4/z8/Pj8uXLAFSsWJEGDRqwevVqq8B7t27dsLOzY+jQoRw7doyLFy8CqMdBZrDcPMinpqZy/vx5tTJ5s2bN1ED68ePHOXnyJLVr11Yrj5spisJOdgLgRmbaluXOy7l265q6T0D5AFbOWYl/kL/kQhdCPNb0JgMTDv9eYOez02jVWed1vCtLfnMhhCiCdu7cSVhYGDt27GDPnj3qitF7Va9enVatWtGqVSs6d+5M2bJlC7inj4fsAulbo49xMC6CRmUkAFkSVaxYkY8//pjr16+zceNGIiMjuXnzJufOnVNTpR49etQqlcq8efOytHP79m30ej329vYcO3ZMXd0NmSlfzPR6vTrzHODDDz+kXr16QGbA/cCBA1ZBdMs6ZAEBAeh0OvX6PSUlhePHj6v7rl69mqCgIF544QX12v1hqV6FEI9OrrCEKETXr1/n119/Zf/+/fcd9BwcHGjUqJEauNbpdOqSMstgeP/+/QkNDaVu3bp89tln6vbKlSsDmXfDmzdvrg7E5uA4ZKZ+8ff3JzIyEoCwsDB1MLb8gW80Gvnoo4/o2rUrO3fuVLe3atWKnTt3ZnkPd1LvHtuyZUsWLlxolWdOCCEeV5OPLCDZkJav57AMnptnnkvwXAghipbo6Gh++OEH5s2bx7VrdyeYPCjgdfbsWSIiIpg9ezY6nY6OHTvy2muv8dRTTxVElx9rWjR8eXwJ89tI4dGSrFy5clYB78jISL766isuXLgAgMFgyHKMl5cX/v7+HD9+nNTUVLZu3UpKSgrz58+32s/X11d9vHDhQvXfuoeHhxpEB6xmtOt0OkaOHEnr1q155ZVXiImJUa/NH+T8+fN88sknuLm58fHHHxMcHEz79u157bXXaNKkiRQtFSIfyMghRAFJSUlh69atbN68mZiYGNLT0zEYDA+9a5yRkWGVc+3NN9+kTJkyTJ8+ndu3bwN3g+QATZs2xdfXl5iYGAD27dunBtPbtGnDn3/+CWTeRTcYDOrd9sqVK6uD9dq1a3nyySdp0KABGzZssOqPwWBg5cqV6nN7e3t27NhhtY95wPb09KR79+68/vrrMpALIUqM9VcPsiX6aL60LcFzIYQoHq5du8bkyZOZO3cuer1e/c2v0+moXbs2Tz75pJo/2cvLi9TUVBISErh58yYREREcPHiQuLg4DAYDa9euZd26dQQHB/PJJ5/Qr1+/Qn53xZcJhaiUOBZd2M6gqlJ4VGQKCAhg5syZnDlzhjVr1nDmzBmSkpJITU1V/+3evHnTaqb6d999l+21fHx8PBUqVCA1NZV169ap2zt06GC137///qs+7tevn5pCtV27dixYsMBq33r16nHy5EmMRiNOTk5UrlzZKtWMmTmwP3/+fNq2bcvChQvx8fHJ/QcihLgvm115vfLKKwwfPpzGjRvbqkkhij2TyYSiKKxevZo//viDtLT7z0709fWlYsWK6kzxmjVrcvnyZVJSUkhNTQXAzc2Nli1b4uDgwIQJExg1apR6vGWQunbt2mogPSwsjP79+wOZd97d3d1JSkoCYPny5Tz77LMoimI1u91gMDBhwgSqVKnC1atXAahSpQr29vZERERY9dtyKZqDgwO9evWid+/eXLlyhRMnTuDm5pb7D04I8dg5fPgwK1eu5NChQ0RERBAbG0tiYiIeHh4EBwfTrVs3Xn31Vby9vW12zldeeYWffvoJyCyebLnsNr/oTQamHP3r4TvmkoPWToLnQghRTEycOJHp06erQbiyZcvSv39/nn32WRo3boyzs3OO2rl48SKbN29mwYIFbN++ndOnTzNgwABmzpzJL7/8Qp06dfL5nTyeFGDVlb0SSBdWNBoNNWvWpGbNmuq2ixcvMnnyZDXdivm/YL2qJCgoSK1TtmXLFtq1a8esWbNITExU93niiSfUx5cuXeLmzZvqeZ955hn1NQcHB6s+jRkzhhYtWvDf//6XTZs2kZaWlm0QHcDNxU1NJbN161aaN27OhtkbKBdQTlKrCmEjNrsK+/nnn/nll1+oWbMmI0aM4Pnnn6dMmTK2al6IYuPKlSusXbuWnTt3WuVDs+Ts7KwGx81iYmK4fv06kFmEZNKkSdy+fZsPP/yQGzduAJnVvc0Da1BQEC4uLqSkpKAoCtu3b6ddu8wfg71792bz5s1AZvoYc/FQRVGsihf9/vvvmEwmgoKC1EC6m5ubWlHcnAbm3seQOai7ublhb29P6dKlCQ0NpV27dri5uT3whoEQomSaM2cO3333nfrcyckJZ2dnEhIS2L17N7t372bWrFmsWLGCZs2aPfL5wsLC+Pnnnx+5ndx6e8+PKNgmP+W9s88leC6EEMXDxIkTAejYsSPvvvsuHTp0QKvVPuSorKpUqcKIESMYMWIEUVFRzJkzh1mzZrFv3z7++ecfCaTnkQbo7t+0sLshioEqVarw448/snv3blavXn3fALajo6Nac+yff/5h8eLFWfYxB84BqxnngYGBODo6qs8PHDigPm7cuDEtWrQAMleXb9q0SX3Nwd6BNvo2nOY0N8ls+9WUV7nNbX7lVxJI4Nzlc/Tu0JuZzKRJRBMJpgthAza7IrO3t0ev13Pq1Cneffddxo4dS/fu3Rk6dChdu3bN0w8HIYqTtLQ0vvnmmyxpTu5Vv359dRmXvb09zz33HKtWreLmzZvqXe2WLVvi6OhI2bJleeuttxg/fjyQeQfcHBQHCA4O5vDhw8DdO98A/v7+6kBuMBj46aefePnllzl79iwJCQlqXxRFYf78+Vaz2atUqUJERATp6enZ9l+j0dCkSRMGDx6Mv79/Xj4qIUQJFBISQuXKlWnZsiXBwcGUKlUKgOTkZJYuXcp7771HbGwsvXr1IiIiAk9PzzyfKyUlhREjRmBnZ0f9+vWtakLkpzWR+zh+84JN2vqi8XAa+9QAZPa5EEIUN127duXjjz+mSZMmNmvTz8+P8ePHM2rUKL777jvc3d1t1nZJokVDeZfS9AtsXdhdEcWEvb09oaGhhIaGcufOHZKSkoiJieHrr78mPj4egFOnTqn7m+uZgfUktbCwMFq1asWiRYvYu3evuk/dunXVxyaTidOnT6vPe/fubdUPM41Gw4evfIj9N/b44MNylgOwgx28z/s0oAGv8zoJJHCMY5zkJI2SGtnqIxGiRLPZlVl0dDR//PEH8+bN4+jRo2RkZLBs2TKWLVtG+fLlGTJkCEOHDqVq1aq2OqUQher69eusW7eOQ4cOcfv2bZKTk63SnJgHOsttAMeOHVMfh4aG0rdvX9q1a8fIkSPVpV+WQew6deqoQfH09HSOHz+uLgvr1KmTGkj/999/uXPnDq6urpw5c8ZqqdnatWu5dOmS1Sz4Bg0aqLnXLfcNDw+36m/lypXx9/fH3t4ePz8/2rVrJ6tNhBC59sILL2S73c3NjSFDhlC+fHk6d+7MjRs3WLVqFYMGDcrzucaNG8f58+cZN24cV69eLZBAut5k4PPjix65HXuNjs8bDyekbLANeiWEEKIwrF69Ot/adnV1ZcyYMfnW/uPOhMLoen3kJrXIE1dXV1xdXfH19eXbb79l48aNrFmzRk2rakmj0VC9enXOnTtHYmIiR44c4YUXXlAD62aW1+hhYWGYTCYgc5V67dq11dfMK84h8xq9ZmBNznGOYIJZwQoAYsjshy++vMRLfMEXACxnOUMYYqNPQYiSzWajh7e3N2+99RZvvfUWx44dY/bs2fz111/Ex8cTFRXF559/zueff06LFi0YMWIEffr0wcVFlpWI4ufOnTt8++237N69+76FQp2cnGjXrh1r1qwBMu8yN27cmL/++stqoDTPUildujQ9evRQC4Fa3tHWarUEBQVx7tw5ALZv364G0l1dXdX9UlNT+eSTT3jvvfesfrybg/CWd7Y1Go1VAdN76XQ6WrZsSbdu3axyxAkhRH5p2vTuEmtzbYa82Lt3L//973+pXr0648eP55VXXrFF9x7q08N/PlJKFw3w+f/PQpeLeyGEEOLR6TRaTPc8b+1bl0Zlqhdan8Tjw9XVlV69evH0009z/vx5EhISSEhIYNGiRcTHx6MoijrpzcwyiK7T6TAajezcuZMRI0Zw7NgxfvjhB/V1X19fq2MtZ7GbC5MCaNFi9/+hPT13J/G1ox3f8z1JJLGDHfeNXaT8m4IxyZjta5Z07jpJDSMENgykW6pfvz7//e9/mTFjBitWrGDOnDls2LABo9HIrl272LVrF2+++Sb9+/dn2LBhVhfPQhQ1iqIQHh7O2bNnuXnzJjt27OD27dsPPCYtLU0NogM8/fTTNGnShHr16vHuu++qy70sg+odO3ZUA+mxsbHcunVLTX3wxBNPqIH0HTt20Lt3b/z9/dm2bZvVeSMiIvjPf/6jDpKOjo7UqFGD8PBwq4HT8rFGo6Fhw4Z07twZnU6Hk5MTlSpVwsPDI7cflRBC5JllWqygoKA8tZGens6wYcNQFIWffvoJJycnW3Xvga7diWdrzLGH73gf5kKiMgtdCCGEsB2dRov+nudv1OpZaP0RjyetVku1atXU561bt2blypWsX79eTf1yr6CgIOzt7Tlz5gx37tzhlVdeybJvUlKSmtb14sWL6up1jUZD9+7dMUQaALjJTTWA7oaberwDDlSlKkc4QjrpJKck44H1NX7Kvynsr74/x+81JCJEgumixMvXKU/29vY8++yzPPvss0RHR/Pbb7/x22+/cfbsWZKSkpg9ezazZ8+mRo0aDB8+nMGDB1O2bNn87JIQOWY0Glm3bh2rVq3i2rVrWV7XaDTUq1cPX19f1q9fD2SmWrl9+zb79u2z2tdcCCgwMJD69eurd6Y3bdqk3k329vbGx8dHrQS+du1aBgwYAFgH3NPT03nvvffo0qWLGkh3cnLCwcGBxMREqyC5ORWMmVarxdvbG51Oh5ubG/Xr16dLly5Z7nYLIURBSE9PJzo6mlWrVvHxxx8DULVqVXr06JGn9iZNmsTp06cZMWKE1Uyd/Pb23u/zdJzMQhdCiJKlZs2aDBs2jBdeeIFy5coVdncee/0C2zA/OkxdLza8RhfKOpcqzC6JEsDV1ZXnnnuOvn37cvToUWJiYkhOTmbr1q1ERUUBcP78eatjLIPoHh4eJCYmEhsby7Fjx3B0dOSLL75QX9fpdNjZ2WEgM5B+gLvFSetRz6pdI3dnmuu0uix9zclM9EfZX4jHUYFdtZUvX56xY8cyduxY1q1bx7Bhw7h+/TqKonD27FnGjBnDhx9+SO/evRkzZgwNGzYsqK4JkUV6ejpffPHFA/PqKorCsWPH1DQsOp2OwYMH4+npyaJFi/jjjz+s9jXr06ePGkg/efIkiqKoxT4rVqyoBtIXLVqEv78/jRs3JiwsDLibpiUlJYV//vlHbVOj0ah3qLPj4uJCu3bt6NGjB+XLl8/lpyGEELbl5OSUbUHjFi1asGDBAhwdHXPd5pEjR5g2bRrlypVj2rRptuhmjuy9fpqY1Ju5Pk5moQshRMlz9uxZxo4dy7hx4+jWrRvDhg3jqaeeQqfLGuASj65HQBM23z7BtZQ4KrqWoW8VKTAqCo5Op+PJJ59Unz/zzDOsXbuWNWvWqAH1e7m5uVGrVi01jctnn32W5TezwWAgIiKCALsAYollL3uxJ7M+Wwc6qPslkcRZzgLgjjvOTs42fX9ClFQFOv1p+/btzJ07lyVLlpCSkqIGFz08PLh9+zZ6vZ7FixezZMkSXnvtNWbNmoVWqy3ILooSymQycfDgQTZs2MDly5dJSEiwKhJap04d7ty5w8WLF4HMpVgXLlxAURR1P19fXzw9PQHo27cvK1asUIPbq1evpn///mpbDg4OZGRkYDQaWbVqFT169MBkMlnNfDcajUybNo1SpUpx584dAIKDg7lx40aWZV+WM9bt7e1p1aoVtWvXxmQyUapUKerXr19gKQ6EEOJhfH19SUtLIzk5Wf1+a9u2LdOmTSMgICDX7RkMBoYNG4bBYOC///0vXl5eeepXenq61cWK+Ttcr9dnKRwNmQVGPzu8AAcldwGQiQ1foGGZqthr7bJttzgwGAyYTCZMJpOarsyWzG3mR9vi/swFzgwGQ7H9u1lcmT9v+dwLVkF/3g0aNODIkSMYDAZWrlzJypUr8fHx4YUXXmDo0KFSm8jG7LV2vFevL2MPzOa9un1l9ZcoVPb29jz99NN0796dEydOcOHCBVJTUzl9+jRHjx4FMnOoW+ZCt/xd6uvrqxY1XblyJY2CGvErv5JOuhpI9+Lub+A1rCGdzOM70EGdvGdrkmNdlDT5PpJcuXKF3377jXnz5qlBSEVR0Gq1dO7cmREjRvD0009z+fJl5syZwy+//EJcXBzfffcdVatW5a233srvLooS7uDBg/zwww/qTPDsmEwmLl26BECZMmX48ssvuXz5MpMmTSIhIQHAaka4RqOhQ4cO6qzxdevWqYF0yFzulZGRAcAvv/zCzZs3qVy5Mjdu3ADAy8uLmzczZzjeunVLPc6yYCiAnZ0dDg4O2NnZ4ePjQ9u2bWnfvr1VEVIhhChqzN+nADdu3GD+/PlMmTKFkJAQxo8fz6RJk3LV3ueff87Ro0fp3r07/fr1y3O/pk6dysSJE7Ns37p1630LpA+nUa7PE3/wAhu5kOvjSqILF+RzKgz31mARBWfjxo2F3YUSJSUlpUDPd+jQIcLDw5kzZw4LFiwgNjaWGzduMGPGDGbMmEGTJk0YPnw4/fv3x83N7eENiod6skw11nSeLEF0UWRotVrq1atHvXp307AcP36clStXsn///vsWBXV2dsbe3h69Xs/27dvZvn27+poPPlb7nuQk85inPu9J/tQGkBzroiTKl9EkPT2df/75h7lz57JlyxYURVG/DAICAhg2bBjDhg2jYsWK6jFBQUFMmTKFsWPH0rt3b7Zs2cLPP/9cYIH0lJQUtm3bxqFDhzh8+DCHDh0iMjISgE8++YQJEyYUSD9EwQoLC2PmzJlZim/eO3iZ07cA1K1bF51OR2BgIB9//DFvv/02kFkMJCYmRs033rt3bzWQHh8fz9q1a+natSs3btywCo4DLFmyxOp5qVKluHXr1n0H0VKlStGpUyd69+4tQXMhRLFWtmxZ3n33XVq1akWzZs349NNPCQkJoXv37jk6/tSpU3z66ae4ubnx/fd5y1Vu9sEHH/DOO++ozxMTE/H396dt27aULl06y/6Dwj4nPu3+abXuNbHhCzR5TFK5pKenc/LkSZycnLC3t7d5+0ajkQsXLhAYGCgpDwpQeno6kZGRhIaGyu+LAqbX69m4cSMdO3bMl39TInv3KwSYn+rWrcvMmTOZPn06K1euZO7cuaxbtw6DwcC+ffvYt28fb7/9Nn379mXo0KG0atWqwPv4uJEguijqzIH1uLg4Tp48SXJyMklJSaxZs0aNHZgnpt7LDz+e4zkAoolmNatZxjK1AGlHOlKJSvnS70fNsS6z2UVxZNMRZf/+/cydO5e///5bnZ2rKIq6hGXEiBF06tTpgUtK3N3dmTBhAlu2bMlSgCE/7d+/n27duhXY+UThSEpKYtOmTWzdupXo6GirpVLVq1enXbt2/Pjjj0BmvvJu3brxzz//EBcXp+5nXvYMmcVD/fz81Bxn8+bNY+zYsUDmbHFLP/zwA//++y9wN2d63bp1OXHiRJaAueUgqdPpaNq0KeXLl8fR0ZGAgAAaN26cpX0hhCjOQkJCaNmyJdu3b+fnn3/OcSD99ddfJyMjg4kTJ+Ll5UVycrLV6wZDZiEmRVHU1xwdHbMNVDk6Omabn93e3j7L/nqTgTumdDI0ObuAeL9OX1pWqJujfYsDo9GIVqtFq9Xma6Bbp9NJIL0AmVMq2tnZSTC3kGT3fSPyT2F+1nZ2dvTu3ZvevXtz/fp1fvvtN3777TdOnz7NnTt31OdBQUEMHz6cF154QWodCfGYK1OmDKGhoerzXr16sX37dlavXn3fQHoUUfzIj3zAB4xkJLe4pb7WgAaMZjSQGYwuSmQ2uyiubBaJq127NmfOnAHuBglr1KjB8OHDGTJkCD4+Pg863Ir5B4I59UVB8fLyomHDhuqfUaNGqTmoRPFmMplYuHAhS5cuvW8uxIiICKsgedu2benevTutWrVi7Nixav7yY8eOWRUI7dmzJz/88AMAe/fu5ejRozzxxBPqvwdLmzZtsnp+5syZ+8469/b2pnPnznTu3Blvb+/cv2khhChmKlSoAMC5c+dyfIz5ouKDDz7ggw8+uO9+kZGRuLu7AzBz5kx1NVFeLbqwjUR9zlIC1PcOpHvlZo90PiGEEI+vcuXKMWbMGMaMGcO+ffuYPXs2ixYtIjExkXPnzvHhhx/y0Ucf0blzZ4YPH06PHj3kJqMQJYCTkxOdOnWiY8eOxMbGcvv2bXQ6Hfv372f58uVqrSHz7HMzB3sHhvQewmejPsPRwbFIzujO62x2mcUuCpvNAunm3M3Ozs707duXESNG0LJlyzy15eHhwQsvvJBvxRCy06pVKzXXtZl5ZrEo/n7++WfWr1//0P0sgzelSpUCwNPTk9GjRzNq1CggM2d5eHi4mtPMHPiBzID9xIkT6datm1UO4KZNm3LgwIEsRdMsg/p169bl+eefx9HRERcXF8qWLSvFdoUQJYo5H7Y54F2UrYzcl+N9ZzZ9JR97IoQQ4nHSpEkTmjRpQs+ePXnppZe4fv06kLnCas2aNaxZs4by5cszevRo3njjDVmlKkQJoNFoKFu2LGXLlgUyV8b37t2b7du3ExYWpgbUn3jiCbp06cKwYcNyNZm1uMjrLHYJvhcvOfn/lZyY/MDX85PNRt0GDRowYsQIBg0ahIeHxyO15ePjw7x582zTsRySO/qPF0VRCA8PZ//+/bRs2ZKwsDAgcwDq1q0bWq2WlStXAvDcc8/h7OzM4sWLrVICmH+0QmYOf8sULt9++y0zZszA3d3daj/IXO5ubtt8TsvK25D5902r1eLh4cGTTz5Jt27dCAwMtOlnIIQQRYU5DciDbpBv3ryZ/fszfxi3adMmx21b3rTMzosvvshvv/1GpUqVHrpvblR0LcO1lLiH7hfoVl5yswohhMiRy5cv89tvvzFv3jwuX74MZF7X2NnZ0bFjR06ePElkZCRRUVG8++67/PHHH2zcuBEvL69C7rkQoqA5OjrSsWNHOnbsSFpaGpcuXWLlypWPdaHivMxiz0vwPafn0rnrsK8s6dhyKicB8rTINE72PvnQtpKdHoNA+qFDh2zVlBCPZMuWLSxevJhr167h6OhotTJCURTOnz9vNbg0adKEoKAgQkJCeO+999Rg+saNGxk4cKA6K7xFixYsXrwYgJiYGN59912ef/55q3QtISEhHDp0yGrmuWXqFnt7e3r06MELL7wgs82FECXGlStX6NWrF6+++iodO3akSpUqalD9ypUr/Pnnn0yePBlFUfD29lZXAJlNmDCBiRMnApmpXCpXrlzQbyGLc4lROdov3VSwaeqEEEIUL2lpaSxdupS5c+cSFhaGoijq9UO1atUYPnw4L774ojoTdePGjUyfPp1NmzZx5MgRJk6cyKxZswrxHQghiqvc5k0vannWcyK3wfc7J+/kKJBr1vB0QwCSjyVj95DJM+bPLyd90sfrsS/98CB9TvZLi0wDwCnA6aH9y+0Mfsj5+zne6fhD9ysObBZInzRpEgCvvfYaZcqUydExN2/e5JtvvgHg448/tlVXRAmlKApz5sxh+fLlD9zv3tzl5oB2hQoVGDp0qPp38ubNm2zYsIEuXboAWYsRxcTE8OWXX6rP7e3tOXDggFXg3NHRETc3N7y9vWnVqhXt27cvFikLhBDC1o4dO8Yrr2SmOHFwcMDDw4PU1FR1KSpAlSpVWLp0Kb6+voXVzRzRmwzoTYYc7dsjQHKjCyGEyGrv3r3MnTuXhQsXkpSUBGRezzg5OfHss8/y0ksv0bp16yzHmWegjhw5km+++YYVK1ZIIF0IkScu1VwIiQiRtCcWTHdMD9/Jwp0zd0ADR1sfRZNacOmp80u9DfUem4B3frFZIH3ChAloNBr69OmT40B6QkKCepwE0sWjWrx4sVUQvU6dOlYFa3v37s3Bgwe5cuWK1XFnzpyhSpUqAISGhvLLL7+QlpZ5x+6nn34CoFOnTmodAMgMupuLj5pZ5jvX6XT07duXAQMGFGiufyGEKIr8/PxYtGgRYWFh7Nu3j+joaOLi4tDpdAQEBFC/fn169uzJwIEDcXZ2LuzuPlROC4162LvQLzBrEEQIIUTJFB0dzfz585k7dy4RERHA3dWr9evXZ8SIETz//PN4eno+tK0XX3yRb775Jsu1jRBC5EZJCI7nJ9MdEzxG2XT0N/QP36mEk6Sd+SA9PZ309HT1eWJiIpAZaLUMtorMojkmkwmTyZSlEOfDmEwmTp48yYYNGwgPDyctLQ1HR0cAmjdvzogRI5g8eTIALi4uDB48mOeff5758+ezdu1atZ2NGzfSsWNHNBoNOp2OunXrcvz43Ttws2fPZtmyZSQkJKiFQB0dHdVzWSpbtixt2rShbdu2eHl5YTLl7m7m48L8vg0Gg/ydL2Dmz1s+94Iln/eDOTg40LdvX/r27Zun4ydMmMCECRPydOy8efNsXndl1ZWcFRp10NlJfnQhhBCqgIAATCaTGjz38PDgueeeY8SIETRq1ChXbZnrkpXU6w0hhBCiMBTq1Z058HBvyoycmDdvHkOHDs3zudeuXaum7LC1qVOnqrlcLW3duhUXF7nbZ0tOTk48/fTTPP3001leu3btmvp35IMPPuDcuXNAZpC9efPmVvuaXwMeKdhjFhcXR1zcw4vQPe62bdtW2F0osTZu3FjYXShRUlIePjtZPD6e8A7i6p2Hf8f3qSyz0YUQQtxlnjjUrFkzXnrpJfr165fn68Ny5coxd+5cW3ZPCCFypSTkWBfiXoUaSD969CgAPj4+hdkNm/vggw9455131OeJiYn4+/vTtm1bSpcuXYg9K3rS09M5efIkTk5OOb6hEhkZycSJE63y6mo0Gqvc5GaOjo6MHz+eyZMnM378eKpVqwbA4cOHmTZtmtW+rVu3pkuXLkyZMoU7d+5gZ2dHr169WLNmTbZBsuDgYDp16kTTpk2lcOg90tPTiYyMJDQ0FFdX18LuTomi1+vVVRZ5uUkp8iY+Pr6wuyAKiN5k4HDcvw/dz9eplKR1EUIIYWXUqFGMGDGCmjVrPnJbbm5uDBkyxAa9EkKIvJEc66IkynMg/ffff892+/Llyzl48OADj01PT+f8+fPMmTMHjUZD48aNc33+AQMG0L1791wfZ5aTvHN5db+0H/b29hLYuofRaESr1aLVatHpHn53MjU1lU8//ZSEhAQA/P39ee6555g+fToAVatWZeTIkSxbtowtW7aox6Wnp7N27VqCg4MBePLJJzGZTFbpGDZu3Gg1i1dRFP7880/1ub29PT179qRSpUoEBgbi7+//aG/+MWa+sWBnZyd/5wuJfN8ULPmsS45FF7YRlZrw0P0a+QRLWhchhBBWZsyYUdhdEEIIm8prcDwvs9lzErAXIr/l+QrvxRdfzFJEUVEUxo8fn+M2FEVBq9UycuTIXJ//fsFq8fgxmUwcP36cdevWcfz4cZKTk4HMWRjDhg2zmtHh6upKpUqVePvttwkICOCvv/5SXwsLC6NJkyY0b94crVaLs7Mzer0ed3d3MjIyrPLaA1aFSsuXL8/7779PYGBgPr9bIYQQRVlO86MfTTj38J2EEEKUKO3atUOj0TBnzhwqVaqUo2OioqJ4/vnn0Wg0bN68OZ97KIQQBSMvs9mTDicVQM+EeLBHmiqVXSqN7LZlx8HBgcaNG/PBBx8QGhr6KN0Qj7GzZ8/y9ddfc/Xq1SyvJScnM3HiRCpVqoROp8NoNHL16lVMJhNarZbevXuzd+9edX9FUfjiiy946qmnCA0NVYvAli5dmhYtWrB48WKr4Lm9vT316tWjW7duPPnkk5K+RQghRI7zo3f3b1oAvRFCCFGchIWFodForFJUPkxqaqp6XF4cPnyYlStXcujQISIiIoiNjSUxMREPDw+Cg4Pp1q0br776Kt7e3rlu+9q1ayxfvpytW7dy5MgRrl27BoCvry9NmzblpZdeol27dvc9/sUXX+S333576Hn0ej12drLKS4jHTW5ns8ssdlEU5Hk0unjxovpYURQCAwPRaDSsX79ezUOdHY1Gg5OTE6VLl85RKg9Rch09epRPP/3UKv2KmWVO9MuXL6tB7vj4eI4cOcKTTz6JRqOhU6dOVscpisKqVatYtWqVui0yMpJLly6pzytVqsSkSZPw8vLKh3clhBBFh8FgID4+HmdnZzw8PAq7O0VeTvOjV3ApLfnRhRBCFAlz5szhu+++U587OTnh7OxMQkICu3fvZvfu3cyaNYsVK1bQrFmzHLd75coVKlWqZDWRzsXFBUVRuHTpEpcuXeLvv/9m2LBh/Pzzzw+89ndycnpg6tW83kQQQjxe8jKLPeXfrPXuHkTrKhMoxYPlOZB+v6Vofn5+OV6mVtTcvHlTraQOmSlFAFJSUoiLuzv7zMnJCTc3twLvX0kSFRXFZ599pgbRg4KCCAkJUVO19OrVi6CgIBYvXszly5fV/1eQmb+/du3aODk5ERwcrKaCqVSpElFRUVkC85bHNm3alJEjR0qBTCHEY+fKlSts27aNHTt2sHv3bq5evaquzIHMVThlypShbt26tGrVilatWtGsWTOZAWZh0YVtROcgP3qD0tUkP7oQQgibMM9ed3JyytPxISEhVK5cmZYtWxIcHEypUqWAzNW9S5cu5b333iM2NpZevXoRERGR41piRqMRRVFo3749L7zwAh06dMDPzw+TycSZM2f48MMPWb58OXPmzMHPz49PP/30vm3179+fefPm5en9CSFKltzOYs9t8D23JPCeP2ovq41TwP3HvYTEBGhbgB2yYLOrPMtgZHHVoEEDLl++nGX79OnT1WKWAEOGDJGBPp8oisLFixeZO3cuaWlpQGZh0HHjxnHq1Cl1P41GQ+vWrWnatCmTJ0/m6NGj6msXL15k/PjxvPHGG1bpWCpXrsyAAQP45ptv1B+kWq2W0qVL07RpU7p27UrFihUL5o0KIUQBSE9P588//2T27NlZUl3dKyMjg6ioKKKjo9mwYQMAXl5e9O/fn1dffZU6deoUWL+LqlVX9pGTBHaSH10IIYStrF27FiDP1ykvvPBCttvd3NwYMmQI5cuXp3Pnzty4cYNVq1YxaNCgHLXr5eXFoUOHaNiwodV2rVZLrVq1WLZsGd26dWPdunXMmjWLcePG5flmwKPQmwxyc1uIEi63wffcBN4NRgOczWvPih77svb51na9DfWwL/3w9s2rCR4kIz7jga/nJxlRRJFgMBjYunUra9assUobBBAeHs6PP/5ISEiIuu3YsWNAZq79d955h+HDh6PX69WULxEREbz11lsEBATwxhtvAJn5Abdt26a24efnx7Rp0ySdgRDisZOSksI333zDjBkziI+PtwqclypVigYNGlC2bFm8vb3x8vIiNTWVhIQEbt68SUREBGfPnkVRFBISEvjxxx/58ccf6dSpExMnTrT6Li5puvs34cczq3Own+RHF0IIAcOGDct2+/jx49WZ4feTnp7O+fPnOXDgABqNJt/qijVtenfMyq4u1f14enpmCaJb0mg0DBs2jHXr1pGcnMzp06dp0KDBI/U1t44nXOSz/Uv4ovFwGpa5f/pZIYSwlJvAu16vh7PwxPYnsHvITTvzjPecBOn18focBZ1zsl9aZOYk1QfN8Db3Ly8z+HOTaudxIIF0C5Z5skXBSUpK4uuvv+bMmTPZvp6RkcGGDRvYuHEjPj4+xMbGcv78ec6ePUuNGjUoVaoULVq0ICwsDEVRcHd3Jykps5rz9evXrc5jFhQUxEcffSRBdCHEY+e3337jww8/JCYmBkVRsLe3p0uXLjz77LM0bdqU6tWrP7SNpKQkDh48yObNm/nrr7+4ePEi69evZ8OGDfTp04cZM2aUyBU8/QJDmffvRtKM2c+A0AB+LmUkP7oQQggA5s2blyW/t6IoLF++PEfHm2+Ee3t788EHH9i8fwA7duxQHwcFBdm0bcsZ6JYpVAvKj6dXkabNYHr4Yn4PHSMz04UQ+catvhv29vk3m/tRuDd0z9X+j0vAO7/keiQx31XXaDTMnj07y/a8uLctUXKYg+jnz59Xt5UuXZr4+HgAnnjiCc6ePUtqaiqKohAbG6vu9/XXXzN16lQ8PT2pX78+YWFhAPTp0wd7e3v++usvMjKsgx21atWiW7duNG/eXPL+CiEeS0OHDgUgODiYt99+m759++a6eLK7uztt27albdu2TJ48mb179/Lrr78yf/58lixZQu3atfn444/zo/tF2rH4C/cNogMowOh6feRCXQghBAABAQFWgfTLly+j0WgoX778AwMuGo0GJycnypcvT/PmzXn11Vfx8/OzWb/S09OJjo5m1apV6nhetWpVevToYbNzAOr1mYODwwNv5G/evJnq1asTGRmJg4MDlSpVon379rz++utUq5b3meTXUxPA1Y5rd+JYfHE7A4Pa5bktIYQQAvIQSLe8q24Z/M7ubntOKIoigfQSbNKkSVy7dg3IDKC/9957nDx5kvnz5wPQrVs3PvzwQ+bPn8/KlSutjr169aoaJLKsAn/jxg1iYmJISkrC0dERyMxrHxISQtmyZQvonQkhROGoU6cO48aNo1+/fnkal7PTtGlTmjZtyieffMLUqVMLJcdpYdObDHwZvhgtGkz3yZTupHOgvndgAfdMCCFEUXXvimdz/aYNGzZQq1atAu+Pk5MT6enpWba3aNGCBQsWqNdOtnDx4kV+/PFHILOY6INWAl+9ehWdToeHhweJiYmcOHGCEydO8MMPPzBr1ixeffXVPPVBsfjv7LPr6ODXkLLOpfLUlhBCCAF5CKTfe1f9YduFyI6iKGzfvp3ff/+datWqYWdnx6RJk/D391cD6wCnTp2iadOmjBgxgvT0dLUAnrOzM6mpqcTHx/Pjjz9a/d1bvTpr/tquXbtaBduFEOJxdfz48Xxr29/fn++//z7f2i/KFl3YRlRK/AOLjaYZM1h0YTuDqsqMNyGEEFm1bt0ajUaDq6troZzf19eXtLQ0kpOTuXPnDgBt27Zl2rRpBAQE2Ow8qamp9O3bl5SUFEqXLs3UqVOz3a9hw4Y0btyY7t27U7FiRXQ6HSkpKaxbt44xY8Zw/vx5XnvtNXx8fOjTp899z5eenm51gyAxMREAB0WHScm8BtSZNHx/Yjnjnhhos/dZHBkMBkwmEyaTKV/S7ZjbLIxUPiWZyWQCMv//6vX6Qu5NyWL+vOVzL1iF+XnnOpB+vzzikl9c5ERiYiJz5szhhx9+ICIiQp31YDAYmDhxIl26dKFp06bY2dlhMBjYvHkzzz//PA4ODgwcOJDNmzdjNBqxs7Ojbt26hIeHA1gV0jPz8vLi5ZdfLtD3J4QQj6Pk5GTc3NwKuxuFatWVfQ8Mot/db68E0oUQQmTLnOqksFhes9+4cYP58+czZcoUQkJCGD9+PJMmTXrkcxgMBgYOHMihQ4ewt7dnwYIFVKhQIdt933rrrSzbXFxceOaZZwgNDaVRo0ZcunSJ0aNH8+yzz9534t7UqVOZOHFilu3DlBBcTBa5fqNgTdSavL0xkSsXLlwo7C6USNu2bSvsLpRYGzduLOwulCgpKSmFdm5J4ikKzOnTp+natSuXL1/O9vUbN27w+++/s2LFCurVq8fhw4dJSkrixx9/5I033sDb25uGDRty4MABkpKSeOWVV8jIyOCLL74gJiYGyFyuGBwcTKdOnWjatCkajYZ///23IN+mEEIUuhMnTlCnTh2btHXr1i26dOnC3r17bdJecdXdvwk/nVn9wGC6Buju37SguiSEEELkWdmyZXn33Xdp1aoVzZo149NPPyUkJITu3bvnuU2j0cjzzz/P//73P+zs7FiwYAGdOnXKU1ulS5dm3LhxvPTSS1y+fJkjR47QsGHDbPf94IMPeOedd9TniYmJ+Pv7M0ezH5NF3RItGsq5ePNzy5Eltp5Jeno6J0+exMnJKV8KIxqNRi5cuEBgYKCsCC9A6enpREZGEhoaWmgrXkoqvV7Pxo0b6dixY5EtNvo4MtdVLAwlc/QQBe7ChQu0adOGGzduqNvs3JxAnxmSqFC1EpfPnEdRFG7dusXJkyfR6XQYjUY2bdpEfHw8/fv3p1y5curxx44dY/fu3WoQ3dHRkVmzZlkV4pElZUKIkqhDhw5s27aNGjVqPFI78fHxdOjQIV/TxRQX/QJD+d/l3cSk3sz2dS0ayruUpl9g6wLumRBCCJF3ISEhtGzZku3bt/Pzzz/nOZBuDqIvXLgQnU7HH3/88cB0LDnRrFkz9fGFCxfuG0h3dHTMNr97hsaI6Z5Z7JGpN1h2ZU+JXT1mNBrRarVotdp8DXTrdDoJpBcgc/0FOzs7CeYWEnt7e/nsC1BhftYFHkhPT0/n1q1b+Pj4qP/YxeNv8ODBahDdNdCH6u90IfKvvSQfjATAKbQK0157nbk//MKpU6dIT0/H29ubW7duYTKZOHLkCEeOHLH6gfTzzz+rj3U6HWPGjLFpNXshhCiubty4Qfv27dm2bRtBQUF5aiMmJoYOHTpw6tQpqYEC2GvtKOvsdd9AugmF0fX6lNgZbkIIIe4KDMwsPK3RaDh//nyW7Xlxb1u2ZE69cu7cuTwdbzQaGTRokFUQvX///rbsos0oSBo2IYQQeWezq73k5GS2b98OZBZRuTeXalxcHC+//DKrVq3CYDDg5ubGSy+9xGeffYaDg4OtuiGKoAMHDrB7924AnHw9qTulD3ZuTrhW8VED6bdOXeFg9ctMmDCBMWPGcOnSJRISEhg0aBCrVq3i9u3bANlWmff29uadd96hXr16BfemhBCiCNNoNERHR9OuXTu2bdtG5cqVc3X81atXadeunXrB3qFDh3zoZfFyMDaC4wn3z/f5hHcgjcpUL8AeCSGEKKrMucjvvRH9KHXF8vOmtjmftbu7e66PzS6I/txzz9mkX5Zp5apUqWKTNiUNmxBCiEdhs0D60qVLGTp0KAEBAVkKS5hMJrp27crhw4fVopBJSUnMnDmTyMhIFi1aZKtuiCLou++/Ux9X7BuSmdIFKNehNteXHgbg1tFIjoScpUnZGgwcOJDPPvsMgH///Zc5c+awa9cu5s+fT2xsLJA5A71WrVp07dpVLU4qhBAi09dff81bb72lBsS3b99OxYoVc3TshQsXaN++vVrPonv37ixZsiQ/u1vk6U0GvgxfjBYNpvtkSY9JuYneZJAZ6UIIIRgyZEiutucXcxqPBwXhN2/ezP79+wFo06ZNrtsfOHAgixYtws7OLlcz0RVFeWC/EhIS1GvCihUr0qBBg1z1LTuShk0IIcSjstnV3vr16wF49tlns6RsWbhwIYcOHUKj0dCwYUNCQ0PZtm0bhw8fZunSpaxbt44uXbrYqiuiCDkU9y8L1/0PAK2DDp/Wd/P1OpZ2w7txINwBY6qeK3/tZaGdK++FPIeHhweJiYmcOHECOzs7TCYTcXFx6rFfffWVzWYlCCHE4+aNN95Ar9fz7rvvcvnyZdq2bcu2bdsemv7qzJkzdOjQgaioKAD69OnDggULSvzNypWRe4lKiX9godHraTdZdGG7LBUXQgjB3Llzc7U9v1y5coVevXrx6quv0rFjR6pUqaIGr69cucKff/7J5MmTURQFb29vRo0aZXX8hAkTmDhxIgAXL160WuFmNBoZPHiwGkRfsGABffv2zXHf/vjjD5YtW8agQYNo1aoVZcuWBSA1NZX169czZswYdYLel19+aZO0sJKGTQghxKOy2Qhy4sQJNBqNVUEQs/nz5wPw5JNPsnv3buzs7NDr9bRq1YoDBw7w+++/SyD9MaQ3GZh0+A8MdzLTsTh4u6Fzsi4IUGlwc0y/nYS0NFKv3WT/jGVManoZNzc3EhMTSUlJ4Z133rHKB9i7d28JogshxEOMGjWKjIwMPvjgAy5cuKDOTDdfqN7r+PHjdOrUSa1nMXjwYObOnSv1TIBNUUdQHrKiXnKuCiGEKIqOHTvGK6+8AoCDgwMeHh6kpqZy584ddZ8qVaqwdOlSfH19c9zurl27+Ouvv4DMtDNvvvkmb7755n33//rrr61mqxuNRpYtW8ayZcsAcHV1xcnJiVu3bmE0GoHMIqJfffWVTfKt6zRaWvvWlTRsQgghHonNAunmlBuVKlWy2q7X69m2bRsajYbXXntNndVmb2/PK6+8wv79+9m3b5+tuiGKkOnHFpOQkYTW0Q6SwJCchmJS0GjvRiMcy7jj9XQDLizZg/6WHkVv5PiOA1btWAbRu3btWuBLIoUQorh6//33SU9PZ8KECfz777+0a9eOrVu34uPjY7XfgQMH6Nq1KwkJCQD85z//4YcffpAio/+vg18D5kdve+CMdMm5KoQQoqjx8/Nj0aJFhIWFsW/fPqKjo4mLi0On0xEQEED9+vXp2bMnAwcOxNnZOVdtm0wm9bFer+f69esP3D81NdXqedu2bZkyZQp79uzh9OnTxMfHc/v2bTw8PKhatSrt2rXj5ZdfttkEKp1Gyxu1etqkLSGEECWXzQLp5otve3vrGccHDx4kNTUVjUZD165drV6rXj3zbnBMTIytuiGKiDWR+1l7LTMg7hJQmoy4ZAzJ6dw6FolXA+ubLY6l3aj8Qkuu7zrL7fArmDKMWdoLCgqiV69etG7dWgI7QgiRCx9//DEZGRl89tlnnD59mo4dO7Jlyxa8vb0B2LlzJ927dycxMRGAkSNHMnPmzMLscpHTI6Apm2+fIDolIdsc6ZJzVQghRE707duX559/nm7dumW5bs4PDg4O9O3bN1cpVyxNmDCBCRMmZPtamzZt1PpneVGpUiU+/PDDPB+fE5ZXjcNrdKGsc6l8PZ8QQojHn83Wa5vvYJuXhJtt27YNyAyElitXLttjxONlV8xJph7/W33u26mu+vjaPwdRTFl/cNm5OVG+W33qvtODoKpV1e0hISFMnz6dr776itDQUAmiCyFEHkyePJnRo0ejKArh4eF07NiR27dvs3nzZrp27aoG0T/44AMJomfDXmvH6Lp971toVHKuCiGEyImlS5fyzDPPUK5cOV5++WX1Wlnkj3LOmZMGKrqWoW8VudkthBDi0dkskB4UFARAWFiY1fZly5ah0WgIDQ3Ncow5Hcz98rWK4mdXzEnGHpxttc27SSAOpd0AuHU0kgs/b0UxmrIcq5gUfE6ncf7cOQBcXFwYPXo0NWrUkAC6EEI8omnTpjFy5EgUReHo0aO0aNGCHj16qDlSJ02axJQpUwq5l0VXgFtZNGQdi7QaDW3L15ecq0IIIR6qVKlSKIrCrVu3+PXXX2nXrh0BAQGMHTuW48ePF3b3Hjuv1OyOk86B9+r2lZvdQgghbMJmgfSOHTuiKArff/89a9euJTk5mW+++YYDBzLTe/To0SPLMeYfC35+frbqhihE2QXRAbR2OoJea6eurYtefYyDL8/l6tIDJJ+/QXpsIgmHLhL583Y2LV+nHvef//wHJyenguq+EEI89mbOnMmrr76KoiicPn2atLQ0AKZPn8748eMLuXdF2zenlmcTRgcUJOeqEEKIHLl+/TrLli2jT58+ODo6oigKV69eZfr06TRo0IA6derwxRdfcPny5cLu6mOhnncV1nSeTMMy1Qq7K0IIIR4TNgukjxw5Eg8PD5KSkujevTuenp68/fbbANSsWTPbQPrq1avRaDQ0a9bMVt0QheR+QXSz0iFBVBvZCf6/0Gj69UQuzdvJiXFLuLL4ALFbTnMn9haQWfV9xIgRtGvXriC6LoQQJcp3333HiBEjUBQFrVbLd999x7vvvlvY3SrSjsVfICz6WLapXUwoRN65kc1RQgghhDV7e3t69uzJokWLuH79OnPnzqVjx45otVoUReHUqVN8+OGHBAYG0rp1a37++We1FpnIG5mJLoQQwpZsFkgvX748K1euxNfXF0VR1D+BgYEsWbIkS2qO8+fPs2PHDiBzNrsonvQmw0OD6Gbl2tem7md9KXVPsVFLtWvXZsKECTz99NO27KYQQpQoOp3ugX9mz56NRqNBURTeeOONh+5vZ1eyL0J/OrMKbfbz0dEAXx5fgt5kKNhOCSGEKNbc3d0ZMmQI69ev5+rVq8ycOZPGjRur19G7du3i1Vdfxc/PTw2+CyGEEKJw2fTKuFWrVly8eJFdu3YRExND+fLladmyZbYX4NHR0Xz00UcA2eZPF0XfvhtnGHtgNgbFmONjPGtXwHPSM6RG3SR2RwTcSsOrtD9NK9WlVYuWVKp0/yC7EEKInFGU7Itiiry5nnoTk2v2P5kUIColjkUXtjOoqqykEkIIkXvlypVj5MiRjBw5kvPnz/PHH3/w119/ERERQUZGBitXrmTVqlX069evsLsqhBBClGg2n2Lm4OBA27ZtH7pfy5Ytadmypa1PLwpITmeh34+znxcB/ZugM8B/PNoS7FMZBwcHG/ZQCCFKrtatW0uRZht62G0JBVh1Za8E0oUQQjyyoKAgPvnkEz755BP+/vtvXnvtNW7dulXY3RJCCCEE+RBIF4+/Rw2iW5rY8AVcrutt0pYQQohMYWFhhd2Fx8rDbklogO7+TQuiK0IIIR5zsbGxLFy4kD///JP9+/cXdneEEEIIYSHfAul6vZ7Dhw9z4sQJtUCKt7c3derUoWHDhtjb2+fXqUU+smUQ/YvGw2noGUT49XCbtCeEEELkh3LOXsSQnG2xUS0ayruUpl9g60LomRBCiMfBnTt3+Oeff1iwYAGbN2/GaDSqado0Gg2tW7dm0KBBhdxLIYQQQtg8kJ6SksKnn37KL7/8ws2bN7Pdx8vLi//85z+MHz8eFxcXW3dB5BNbB9Gbl6tNWlqaTdoTQggh8svLwd35JOKvbF8zoTC6Xh/stbLITwghRM4ZDAbWrl3LggULWLlyJampqcDdOid16tRh0KBBDBo0iIoVKxZmV4UQQgjx/2x61RcZGUmHDh04f/78AwudJSQk8MUXX7B06VI2b94sPwyKgfwIogshhBDFQf3SgbQpX58dMeEYFZO6XafR0tq3Lo3KVC/E3gkhhChOduzYwZ9//smSJUvUiWfma2d/f38GDBjAoEGDqFu3bmF2UwghhBDZsFkgXa/X07VrV86dOwdAcHAwQ4cOpUmTJvj6+qIoCtevX2f//v3MmzePU6dO8e+//9K1a1eOHDmCnZ3M5CqqJIguhBDFR0pKSr6v9iqIcxQ1b9bqye7rJ7ME0t+o1bMQeyWEEKI4qVy5MleuXAHuBs9LlSpFnz59GDRoEKGhoYXZPSGEEEI8hNZWDf3666+cPn0ajUbDuHHjOHHiBO+99x6tW7emevXq1KhRg9atWzN69GiOHz/O+PHjATh16hS//vqrrbohbGzfjTMSRBdCiGKkSpUqTJ8+nZSUFJu3vW/fPrp168aMGTNs3nZRV9a5FJ0rNrLaNrxGF8o6lyqcDgkhhCh2IiMjURQFBwcHnnnmGZYuXUpMTAw///yzBNGFEEKIYsBmgfTFixej0Wjo1asXn376KVrt/ZvWarVMmjSJ3r17oygKixcvtlU3hA3pTQYmHP7dJm1JEF0IIQpGbGwsY8eOpXLlynz88cecPXv2kdpLS0tj4cKFdOrUiebNm7N+/Xob9bR40ZsMHIr9V31ewaUMfatIgVEhhBA516ZNG3799VeuX7/OkiVL6N27Nw4ODoXdLSGEsV6GPgAAvupJREFUEELkkM3yqZw4cQKAYcOG5fiY4cOHs2zZMsLDw23VDWFDnx39i2TDoxcDlSC6EEIUnK1btzJy5EiOHz/OlClTmDJlCg0bNuTZZ5+ladOmPPnkk7i7uz+wjdOnT7N//342b97M8uXLSU5OVmfQvf3224waNaqA3k3RsejCdqJT49XnT5apKgVGhRBC5MqWLVsKuwtCCCGEeAQ2uwK8ffs2AH5+fjk+pnz58gAkJibaqhvCRqLuxLEp6sgjtyNBdCGEKFihoaEcOXKEv//+mylTpnDq1CkOHTrE4cOHAdBoNAQGBlK2bFm8vLzw8vIiNTWVhIQEbt68yfnz50lOTlbbUxQFJycnhgwZwrhx40pkgfC4tETmRKzDsoz6uqsHGVKtk6R2EUIIIYQQQogSwmaBdG9vb27cuMHFixdp0KBBjo65cOGCeqwoWl7e8fUjtyFBdCGEKBwajYYBAwYwYMAANm3axK+//srKlStJTU1FURTOnTvH+fPnsxxnLnxmVqtWLQYPHszw4cMpU6ZMQXW/yJkbsd6qyCiAUTHx7anlTHpySCH1SgghhBBCCCFEQbJZIL1hw4asXbuW7777jmeeeSZHx3z33XdoNJocB95Fwfjvif9xy3DnkdqQILoQQhQNHTp0oEOHDqSnp7Nv3z527NjB7t27uXr1KrGxsSQkJODk5ISPjw8+Pj7UrVuXVq1a0apVKypVqlTY3S8S9tw4hcnV+ieTUTGxNfoYB+MiaFSmeiH1TAghRHF1/vx5VqxYwbFjx4iLi1Nvdt+PRqNh8+bNBdhDIYQQQtzLZoH0AQMGsHbtWsLCwhg2bBjffPMNrq6u2e57584d3nzzTcLCwtBoNAwaNMhW3RCPaFvUcRZf2v5IbUgQXQghih5HR0dat25N69ZSIDO3tGgw3Wf7l8eXML/NGMmXLoQQIkdSUlJ4/fXXmT9/fpbAuaIoaDSaLNuALNuFEEIIUfBsdtU3aNAgfvzxR3bv3s1vv/3G6tWr6devH02aNKFcuXJoNBpiYmLYt28fixcvJjY2FoAWLVowcOBAW3VDPIJdMScZf3jeI7UhQXQhhBCPGxPZzxA0oRCVEseiC9sZVLVdAfdKCCFEcaMoCr1792bTpk0oikKZMmWoWLEiR48eRaPR0KpVK27evMnZs2fR6/VoNBpq1KiBr69vYXddCCGEENgwkK7RaFi5ciVPPfUUe/fuJTY2lu+//57vv/8+y77mu+rNmjVj+fLltuqCeAS7Yk4y9uDsPB+vAT6XILoQQoj7OHz4MCtXruTQoUNEREQQGxtLYmIiHh4eBAcH061bN1599dU81U25du0ay5cvZ+vWrRw5coRr164B4OvrS9OmTXnppZdo1y5/At0KsOrKXgmkCyGEeKjFixezceNGNBoNn3zyCR999BGnTp2iXr16AGzbtg3IXMH966+/8tFHH5GQkMAvv/xCy5YtC7PrQgghhAC0tmzMy8uLnTt38s0331CzZk0URcn2T82aNfn222/ZsWMHXl5etuyCyINHDaI7aO34MuQ/EkQXQghxX3PmzGHChAmsXLmSs2fPkpKSgrOzMwkJCezevZvx48dTo0YN9uzZk6t2r1y5gr+/P6+//jpLlizh/PnzaLVaNBoNly5d4u+//6Z9+/YMHz4co9Fo8/elAbr7N7V5u0IIIR4/CxYsADInlH3yySfqeHUvV1dXRo4cyebNm0lKSuKZZ54hKiqqoLsrhBBCiHvYNJAOoNVqef311zl58iTXrl1j/fr1/PXXX/z111+sX7+ea9eucfLkSV577TW0WpufXuTSvhtnHimI/kXj4azr8hkhZYNt2CshhBCPm5CQEKZPn86ePXu4efMmqampJCYmkpSUxLx58/Dx8SEuLo5evXpx+/btHLdrNBpRFIX27dvz22+/ce3aNe7cuUNycjInT56kZ8+ewN1Afl5oyT4vrRYNfi5l6BcoeeeFEEI83MGDB9FoNLz00ks52r9x48a8+uqrxMXF8d///jefeyeEEEKIh8nXyljly5enfPny+XkK8Qj0JgMTDv+e5+MlH7oQQoiceuGFF7Ld7ubmxpAhQyhfvjydO3fmxo0brFq1KseFyL28vDh06BANGza02q7VaqlVqxbLli2jW7durFu3jlmzZjFu3DicnJxy1fcH5UgfXa+PFBoVQgiRI3FxcQAEBgaq2+zt7dXHqampODs7Wx3z1FNPMXPmTFatWsXnn39eMB0VQgghRLZkSngJNvnIApINaXk6VoLoQgghbKlp07vpUa5evZrj4zw9PbME0S1pNBqGDRsGQHJyMqdPn85135qVrYVOY/2TSafR0rZ8fRqVqZ7r9oQQQpRMdnaZN17d3d3VbZaPY2Jishzj6ekJZKYyE0IIIUThkkB6CbUmcj9boo/m6dgpT74oQXQhhBA2tWPHDvVxUFCQTdu2nIGelzzpQ6t3zjaQ/katno/cNyGEECWHn58fALGxseo2X19fdRb64cOHsxxz7tw5AAwGQwH0UAghhBAPYvO1yAaDgdWrV7Njxw4uXLhAUlLSQy9aNRoNmzdvtnVXxH3sijnJ1ON/5+nYYdU607p8PRv3SAghREmUnp5OdHQ0q1at4uOPPwagatWq9OjRw6bnCQsLA8DBwYHq1XM/g7yMkwfDqnfhpzOr1CQvw2t0oaxzKZv1UQghxOOvfv36nD9/nvDwcDp27AhkXgs3adKEbdu28f333/Pss8+q+xsMBmbOnAlAtWrVCqXPQgghhLjLpoH0nTt3MnjwYCIjI9VtipJ9XlHI/NGgKEq2lcpF/niU4qIVXUoztEZnG/dICCFESePk5ER6enqW7S1atGDBggU4Ojra7FwXL17kxx9/BKB///54eHjkqZ1+ga1ZGbmXaylxVHQtQ98qUmBUCCFE7rRr146lS5eybt063nnnHXX7sGHDCAsLIywsjNDQUPr160dKSgp///03R44cQaPR0K9fv0LsuRBCCCHAhoH0M2fO0KVLF1JTU1EUBQcHB6pVq4a3tzdarWSQKQr0JgOfHPotz8d/3ex1G/ZGCCFEflixYgUA7du3x9XVtZB7kz1fX1/S0tJITk7mzp07ALRt25Zp06YREBBgs/OkpqbSt29fUlJSKF26NFOnTn3g/unp6VYB/sTERAD0ej0YFUYG9+TTowt4t+YzYFTQG/U262txYzAYMJlMmEymPKXLeRhzm/nRtrg/k8kEZP7/1etL7t/vwmD+vOVzL1gF/Xn37t2bN954g61bt3LhwgW16Ojzzz/PggULWLduHTt37mTnzp1Wxz3xxBNWgXchhBBCFA6bBdI/++wzUlJS0Ol0TJw4kbfeegs3NzdbNS9sYPKRBdwxZp0BmBMDg9o98hJ2vV6f5YLYHLBQFEW9eLMlk8mEnZ0dJpNJVj4UIEVRsLOzIz09XS2qJAqGXq/Hzs6OtLQ0CUDlkU6nw97evrC7kWe9evVCq9Vy/PhxatWqpW4fNmwYGo2GyZMnU758+ULsIVy6dEl9fOPGDebPn8+UKVMICQlh/PjxTJo06ZHPYTAYGDhwIIcOHcLe3p4FCxZQoUKFBx4zdepUJk6cmGX71q1bcXFxAeBlQri27yzXOPvIfSwutFptoUyKsLOzs1rlKAqGnZ0du3btKuxulEh2dnZs3bq1sLtRbJlv7uVGSkpKPvUme76+vuj1ehRFQafTWb22bNkyJk+ezOzZs9Wio6VKlWLQoEFMmTLFqt6HEDkh198lh1x/Fx65/n50xe36W6M8KPdKLlSsWJHo6GhGjRrFl19+aYsmHxuJiYl4enoSFxdH6dKlC6UPayL35zkvurPOgdWdJ2OvzdsXcmJiInFxcdku41cUhYyMDDQaTb4NtHq9vlj9o3wcKIqCwWDAyclJVqQUMEVRSE1NxdnZWX68PgJHR0fKlCmT4zQg8fHxlClThtu3b+c5dYitaLVaNBoN4eHhVoH0+20vKvbv30+zZs0wmUysXLmS7t2757kto9HIoEGDWLhwIXZ2dvz111/06dPnocdlNyPd39+fK1FXGXtqPtEp8fi5luanFiPzPCYWJ0lJSSQkJJCRkZHlNUVR0Ov1+TZ+m8cROzs7+S4rQJbjt3zuBUtRFNLS0uSzf0QODg54e3vj7u6eo/3j4+MpX758kRi/LSUkJGAwGPDx8ZG/D3lQFK6/C5Ncf5c8cv1deOT62zaK0/W3za4C4+LigMzlaqJo2XfjTJ6D6ACTG734SEH0a9eu4ebmRpkyZbC3t7f6cjGZTKSlpaHRaPLlC9/8Q8HBwUG+1AqQyWRCr9fj6uqaZbaNyF8mk4nk5GTc3NzkR1QemIODt2/f5tq1awBF6sI6JxwdHcnIyCA5Obmwu5IrISEhtGzZku3bt/Pzzz/nOZBuNBp5/vnnWbhwITqdjj/++CNHQXTI/Oyyy8++NuoQkak3UDRwOeUG/7u6h4FB7fLUv+IiMTGR69ev4+bmRtmyZWX8LiFk/C48Mn4/Gsvx+/r169jZ2eVo/C7oYJ95lY2bmxve3t733e9BrwnxIHL9XTLJ+F14ZPx+NMXx+ttmgXQfHx+ioqJwdna2VZPCBvQmA+MOzMnz8V80Hk6IT3Cej4+Li8PNzY2KFStmO5CaTCa14Gx+DeSQGRyRgbzgmJfyOTk5yUBewEwmExkZGTIb4RE4Ozvj7u7O1atXiYuLK/ID+b0qVKjAxYsX2bFjByEhIYXdnVwxp145d+5cno63nIluDqL379//kfu16EIYyv//vFGA2WfX0cGv4SOnPCvKZPwumWT8Ljwyfj+64jB+V65cGY1GwzfffMNrr71W2N0RjyEZv0smGb8Lj4zfj644jN+WbBZIb9myJYsWLeLEiRM0bNjQVs2KRzTp8B+kK4Y8HftF4+E0L1c7z+fW6/Wkp6dTpkwZGUSFEMWKRqPB09OTa9euFbvlqe3bt+eXX37hww8/ZP/+/VSvXt2q/99//z1ly5bNdbsff/yxLbuZrQsXLgDkeEm+peyC6M8995xN+mVUTIDW6vm3p5Yz6ckhNmm/qJHxWwhRXBX18dvZ2Zm0tDQaN25c2F0RjyEZv4UQxVVRH78t2SyQ/s4777B06VK+/vprBg4cKAUOioA1kfsJizmep2MfNYgOqIUWivI/ACGEuB/zd5fRaCxW32Pjx4/nn3/+IT4+niVLlli9pigKP/zwQ57afZRAutFoVHO038/mzZvZv38/AG3atMl1+wMHDmTRokXY2dnZbCa62n42gfSt0cc4GBdBozLVbXaeokLGbyFEcVaUx+8KFSpw/vx5KUgn8oWM30KI4qwoj9+WbLbuoHHjxnz11VccPXqUZ555Rs2ZLgrHo+RFt0UQ3ZLcDRdCFEfF9bvL39+fw4cPM2LECCpXroy9vb26hBcyg+l5+fMorly5QoMGDfjpp5+4cOGCVXtXrlzh888/p2fPniiKgre3N6NGjbI6fsKECWpRrEuXLlm9ZjQaGTx4sBpEX7BggU2D6PejRcOXx5egN+Vt1VdxUFz/DQghSrai/N3VqVMnAHbu3FnIPRGPs6L8b0AIIe6nuHx32Wza+KRJkwBo0qQJq1atolKlSnTs2JHg4GBcXFweenxBLBkvKR4lL7qtg+hCCCEKnr+/Pz///LPVNvOM8PDwcGrVqlXgfTp27BivvPIKAA4ODnh4eJCamsqdO3fUfapUqcLSpUvx9fXNcbu7du3ir7/+AjJ/fL355pu8+eab993/66+/tkmg3YRCVEociy5sZ1DVx7vwqBBCCNsYOXIk8+bN48svv2TAgAFqbRAhhBBCFA82C6SbZ4tB5oVsamoqK1euZOXKlTk6vrAD6fHx8axYsYLNmzdz+PBhLl++jMFgwMfHh0aNGjFkyBB69+5dqH3MqV/OrM1TXvQP6z0nQXQhhBA25+fnx6JFiwgLC2Pfvn1ER0cTFxeHTqcjICCA+vXr07NnTwYOHJjrouUmk0l9rNfruX79+gP3T01NzdN7yI4CrLqyVwLpQgghcqRatWosWLCA559/nqZNm/LFF1/Qp08fHBwcCrtrQgghhMgBmyYyv3fp96MuBS9Ivr6+GAx3g89OTk7Y29tz7do1rl27xvLly+natStLlizJ0Qz7wnIj9RZ/Xdia6+Pal3+CrgEh+dAjIYQQBemdd94BYOzYsVZFRefOnYtGo6FixYoF3icHBwf69u1L375983T8hAkTmDBhQravtWnTptB+b2iA7v5NC+XcQgghip927TJvvPr4+HDx4kUGDx7M8OHDqVatGl5eXuh0uvseq9Fo2Lx5c0F1VQghhBDZsFmOdJPJ9Eh/CpvBYCAkJITvv/+e8+fPk5qaSnJyMhcvXmT48OEArF27lpdffrmQe/pgnxz6PdfHuNk5Ma7BwHzojcgPnTp1wtnZme3bt+fquJdeeglnZ2fmz59vtX3+/Pk4Ozvz0ksvWW2/fPkyzs7O1KhRI0tbNWrUwNnZmcuXL+f+DQgh8tWsWbP4+uuvs9QqmTBhAhMnTuTGjRuF1LPHixYNfi5l6BfYurC7IooJGb+FEGFhYWzbtk2t96EoCunp6Zw4cYIdO3YQFhaW5c+2bdvUx0KIgifjtxDCkk1npBdnW7ZsoW3btlm2V65cmV9//RU7Ozt++ukn/vjjDz777DP8/f0LoZcPtvf6aU7cupTr4yY++QL2WvmrUFhq1KhBZGSk1TYnJyfKly9Pq1atGDlyZKHkMxZCPF4uX76MRqMhIyOjsLvyWDChMLpeHxk/SzAZv4UQudW6detiU0xNiMeVjN9CiEchV3//L7sguqXhw4fz008/AXDw4MEiF0jXmwxMPDL/4Tve44vGwwnxCc6HHoncqlq1Kj4+PgDcvn2bc+fO8fvvv7Nw4UL+/PNPnnrqqUdq39fXl+rVq+Ph4fHIfQ0MDMTJyQk7O/kKEaKocXFxITU1NcuMdJF3Oo0W0z3PW/vWpVGZ6oXWJ1F0yPgthMgpmVUuRNEh47cQIi/kX2EOOTk5qY+NRmMh9iR7iy5sI9mQlqtjHvfionqTATvN/fMMFjVjxoxh8ODB6vPr168zbNgwtmzZwssvv8yZM2dwc3PLc/uffvopn376qS26ytq1a23SjhDC9qpWrUp4eDi///47rVq1kplvNqDTaNHf8/yNWj0LrT+POxm/rcn4LYQQojiQ8duajN9CPJ4kkJ5DlrMH6tatW3gduY8/zm3J1f5ty9d/rIuLHkk4z8fH5jOp3mBqulYo7O7kSbly5Zg9ezbBwcHEx8ezefNmevaUwI0Q4sF69+7N8ePHmTt3LmvXriUwMBB7e3v19aFDh+Lq6pqrNkt6gbN+gW2YHx2GuaTp8BpdKOtcqjC79NiS8VsIIYQofmT8FkKUFLkuNhoYGEhgYCBBQUHZbs/Ln3vbKmpu3brF1KlTAWjVqlW2xR8KU4ohjWRDao73d9I68FGDQfnYo8KlNxmYdfp/pBkz+PrM/9Cbit4Kgpzy9fWlatWqAJw7dy7L62fPnmXgwIFUrFgRLy8vmjdvzpIlS7Jt637FTvLifsVOLAuxHDp0iB49euDt7Y2rqyvNmzfnf//7X7btGQwGvv76a0JCQnB3d8fR0RE/Pz+aN2/OJ598wq1btx65z0KUFO+//z7NmjVDURSio6PZtWuXejNYURQOHDiQbTEzKXB2fz0CmuDnUgaAiq5l6FtFCozmBxm/ZfwWQghR/Mj4LeO3ECVJrmekmyuM37tU3Lw9L4rysnOTycTgwYOJjo7G0dGRb7755qHHpKenk56erj5PTEwEQK/Xo9fr73dYnr20/SsclJwvofq0wWAwKuiNtu+LJb1ej6IomEwmTCZTtvsoioKiKGg0GhRFyXaf3PonchdRqfEAXEuNZ2XUPp4LbGOTtvOT+bPIbnt2jw8fPsyUKVPQaDRUrVqVq1evcuTIEQYPHkxGRgYDBgzI0Xly+zgnfd69ezdffPEFDg4OBAcHc+3aNfbs2UPv3r358ssvGTVqlNX+/fv3559//gEgKCgIb29vYmJi2L9/P3v27KFnz5488cQT2fZBWDP//zD/2xN5ZzKZUBQFvV6PTvfg79j8+G7PKycnJ7Zt28bixYvZtGkT165dIz09nW3btqHRaHjyySdzPSO9pLPX2vFevb6MPTCb9+r2lQKj+eSfyN3q+B31/+P3gKAH17Apyu43dlqO39WqVePKlSvq+K3X6+87fheEB43fM2bM4J133rHa/7nnnmPp0qVA9uN37969ZfwWwsL27dtzfYxGo8HJyQlPT08qV66Mg4NDPvRMiLyT8VvGbyFKklxfCQ4ZMiRX2/PLvHnzGDp0aJ6PX7t2LV26dHnofiNHjmTVqlUAfP/999SvX/+hx0ydOpWJEydm2b5161ZcXFxy39mH6Efu8pxf33+ONWS9u2prdnZ2+Pr6kpycTEZGRr6fDyAuPZHfL1inH/gzMozWPnUo4/joRT7yg3mgNhgMVjdgIDNP2/nz5wEICAggPT1dDY5+/PHHvPjii0yaNAknJycURWHixIl8/fXXjB8/np49e1oF/8y5/e89j8FgUF+33G7+f6YoSpZ+mfuckZFh9Zq5b1OnTuXpp5/mv//9L66uriiKwi+//ML777/P2LFjCQkJUVMkHT16lH/++YcKFSqwdOlSqxUfiYmJ/O9//8PBwUG9ISVyJikpqbC7UOxlZGSQmprK9u3b1X8n95OSklJAvcoZOzs7BgwYYPWDXqvNXIQ2b948atWqVVhdK7aeLFONNZ0nSxA9n8Sm3eb3C5vU5wqZ43eHCg2LZRqdmJgYdfy+d+Xlxx9/zPDhw5k6dao6fn/00UfMmDGDcePG0a9fv4fevMsv5vF77ty5eHh4oCgK3377LW+99Rbvv/8+7du3V38LHzp0iKVLl+Lv78/69eupWbOm2k5iYiKLFi2idOnShfI+hCiq2rRp80iTyOzs7HjiiSd48cUXGTFihFXqtvs5fPgwK1eu5NChQ0RERBAbG0tiYiIeHh4EBwfTrVs3Xn31Vby9vfPcr+vXrzNt2jRWrVpFZGQkzs7O1K5dmyFDhjB8+PCHvufz588zbdo0NmzYQHR0NB4eHjRo0ID//Oc/PPvss3nul8h/Mn7L+C1ESZPrq8G5c+fmantxNnr0aL799lsAZs6cybBhw3J03AcffGB1xy8xMRF/f3/atm1r8y+kXhsnkGbMWZBah4b/dZpYYEGAtLQ0rly5gpubm1WxVkuKopCamopWq7XJyoS5EZswKtazcA0mI3MvbWJ8vcK7Q/wg5vdtZ2eHo6Ojuv3GjRu89tprpKen4+XlRefOnXF0dFSDYcHBwcycOVN9DjBp0iT+/vtvoqOjiYiIsLqLbB7U7z2PufK3Tqez2m6e7aLRaKy2W/bZwcHB6jVzX7y8vPjuu+8oU6aMum306NHs3buXZcuW8csvvzBv3jwAoqKiAOjbty+NGze2Oo+HhwdvvPHGwz9EoVIUhaSkJNzd3Yv0ap/iIC0tDWdnZ1q3bn3f7zCz+Pj4AuqVKEwSRM8/P0asznb8/unfNXxUb2Ah9Spvbty4wfDhw9Xxu3379lav16xZkxkzZqjjo0aj4ZNPPuHPP/8kOjqa8PDwQpsFZh6/zatWNBoNb775JmFhYfzzzz989dVX/PbbbwD8+++/APTp08fqIhwyx+8RI0YUbOeFKCYeZRWuXq/nwIEDHDx4kB9++IFVq1YREBDwwGPmzJnDd999pz53cnLC2dmZhIQEdu/eze7du5k1axYrVqygWbNmue7ToUOH6Ny5s/pbyM3NjaSkJHbu3MnOnTtZvHgxK1asyHI9YbZmzRr69u2rTkrw8PAgPj6eDRs2sGHDBoYOHcrs2bPld20RJeO3jN9ClDTF9opwwIABdO/ePc/He3p6PvD1MWPGMGPGDACmT5/O22+/neO2HR0ds/2hYG9vn6NZAzm1+vJeEk2pkMPfFJ83GoaLo7PNzv8wRqMRjUaDVqu1CvZaMplM6o+iR/1xdDj+HNtvnMh6DhS2x57gyM3zNPSu+kjnyE/Tp09Xg8u3b9/m3LlzZGRkYG9vz3fffYeHh/WM+iFDhmS54+3g4EC9evXYuHEjly5dokGDBlnOo9ForD7r3D5+UFuWfXNyclL//5u9/vrrLFu2jA0bNqjbK1WqBMCWLVu4devWI82GEXdXBdz72YvcM9/gy8l3ty2/2/PLxYsXAahQoXgWgBKPrweO3zdOcDjhXJEev6dNm6ZOKMlu/HZ3d7fa/4UXXsjy/Wxvb0/dunWJiYnh4sWLhXYhbh6/7/Xaa6/xzz//sH79enWbv78/AJs3byYhIUHGbyFyYOvWrej1ej766CP27duHn58fffv2pVGjRvj4+AAQGxvLwYMHWbx4MVFRUTRp0oSJEyeSmprKiRMnWLhwISdOnODEiRN069aNo0ePqhNjshMSEkLlypVp2bIlwcHBlCpVCoDk5GSWLl3Ke++9R2xsLL169SIiIuKh18mWbt++Tffu3YmPjyc4OJj58+fTqFEjMjIy+OWXXxg1ahQbNmxg1KhRfP/991mOv3jxIv369SMlJYUWLVowZ84cqlevTnJyMtOnT2fSpEnMnTuX4OBgxowZk7sPW+Q7Gb9l/BaiJCq2gfT7Batt4b333uPLL78EMr9cR48enS/neRR6k4EvwhfneH8vezda+NbJxx4VLr3JwNdn/ocWDSayzvLQouHr08v5tdnIIjuj8Ny5c2pBEwcHB8qVK0fLli0ZOXJktimFAgMDs23H/CP8zp07+dfZh7hfQV7zHe/r16+rS0qbNWtGkyZN2LdvH/7+/nTs2JHWrVsTGhpKw4YNZfaJEDZivmklRFEi4/ddMn4L8fgLDQ3l6aefZv/+/bz55ptMmzYt22vaQYMG8fnnnzN69Gi+++47Zs2axZo1a+jZsyfjxo3jo48+YsqUKZw+fZq5c+fy0ksv3fecL7zwQrbb3dzcGDJkCOXLl6dz5/9j777jmrj/P4C/krCnCiriAmUouFddOFp3tdVW1H7V2mrrbB0VtWpV3KPUvbd1FkfrqHtrHXW0OFBQQUAEFRDCTkg+vz/43TWBBBJIOALv5+ORh/Huc5/73Cfh3rnPfe7z6Y63b9/ixIkTGDx4sM7HExQUhPj4eFhbW+PkyZNwd3cHkHsuHDduHKRSKWbMmIHNmzdj4sSJ8PLyUtt+9uzZSE9Ph4uLC06cOME38tvZ2WHu3LmIj4/H5s2bsXDhQnz77beoWLGizmUjxkXx+z8UvwkpX6i7Yh4BAQFqjehTpkwRuESazf9nL5iGgKXNxvbjjVga4R2O/guvM5M0BnEg9654XGYijkTfKOGS6W7z5s3IzMxEZmYmUlJSEB4eju3bt2sdl1/bZIHcXXJDTd5aFNyPibyqVKnCv+fG8BaLxTh16hQmTJgAa2trHD16FJMnT0aLFi3g7u7O99InhBiOVCrF9u3b8e2336JPnz746KOPEBUVpZbm9evXCA0NRUREhEClJOUBxe//UPwmpOzbsWMHTpw4gV69emHVqlUFdgyztLTEmjVr0KtXL5w5cwabN2/m182fPx8dO3YEYwxHjhwpVplat27Nv3/16pVe2/76668Acicu5BrRVX3//fews7ODQqHA3r171dalp6fzkx2OGTOGb0RXNX36dAD/zZtESg+K3/+h+E1I+WKUW4NKpZK/+E5NTeUnOSyItjvlJSkgIIAfziUoKAiTJ08WuESayZU5uBQXonN6D3tXuNo6G7FEwjsde7fQNAzAqdg7GOjWwfgFKucSEhI0Ln/37h3/XvVRuYoVK2LlypVYsWIFQkJCcPXqVfzxxx+4dOkSvv76a9jZ2aF///5GLzch5cG6deswc+ZM/sc0YwwikShfL5orV65g8ODBsLKywqtXr+ixT2IUFL9LF4rfhBjX9u3bIRKJMHLkSJ23GTVqFE6ePIldu3apbffVV1/hypUrCAnR/bpQk2vXrvHv806uWJCwsDBER0cDAHr27KkxjZ2dHfz8/HDq1CmcPXsWc+fO5dddv34dmZmZBW7v5uaG+vXr48mTJ/x46aR0oPhdulD8JqTkGLRHekZGBmbMmAEXFxc0btwY/fr1w5dffomvv/66wJeuk3ga07Rp0/hG9OXLl5faRnQgtze6Pta1K/uTNfao3qLQNCIAPau3LDQdKb6wsDCNy588eQIAqFq1ar4x34Hccb2bNGmC8ePH4+LFi/jxxx8BAFu2bDFeYQkpRwIDAzF+/HhIpVJYWFigefPmWtMOHDgQ1apVQ3Z2Nt9jjBBDo/hdulD8JsS4uL+lGjVq6LwNl/bp06dqy7khG5KSkvQuR3Z2Nl6+fIm1a9di6NChAAAPDw/06dNH5zwePfpvbOwGDbQPIcqtCw0N1bq9r69vods/fvxY57IR46P4XbpQ/Cak5BisIT0tLQ0dO3bE0qVLkZCQAMaYXi8hRUdHY9myZQByH3NZunQpXFxctL64oV+EoG9v9P/V6QQbs/yTTpQ1n9dqB1frShBrmXlVDBGqWTvhs1ptS7hk5dOuXbuQnZ2dbzk3yVC3bt10yod71PT169eGKxwh5dQ///yD+fPnAwCGDBmC+Ph4/P3331rTi8Vi+Pv7gzGGc+fOlVQxSTlD8bt0ofhNiHFlZWUBAGJiYnTehhtuJe/fJjfRubW1tc55WVlZQSQSwcrKCu7u7vj+++/x/v17tGvXDhcuXNBrDjLVv++CJjLn1kmlUqSlpeXbvmLFirCxsSl0ezqflC4Uv0sXit+ElByDDe2yYMEC3Lt3D0DuH9/IkSPRuHFjVKhQId/MxqWNUqlUe//mzZsC06v+AChp+vRGtxKZY4zPJ0YsTelhLjbDhHp9Me2f7RrXK8Ewof6npXaik7ImKSkJ33//PbZu3QoHBwcwxrBhwwYcOXIEEokEP/zwA5927969iIiIwNChQ+Hm5sYvT0xMxOrVqwEAzZo1K+lDIKTMWbNmDRhjaNu2LT+maWHatGmD1atX4+HDh0YuHSmvKH6XLhS/CTGuOnXq4PHjx9iyZQs++US367RNmzYByD/sCtfQpW1sZE1cXFyQlZWFtLQ0fki3zp07Y9myZahVq5bO+QD/jbcMoMCGcNV1qampsLOzU9u+oG1V16vuL6/s7Gy1RkSpVAoAkMvlkMvlBeZflsjlcjDGoFQq1do4VHEdGUUiUbE6NJqJJBhf71P8+M8OjeuVYJhQ71OYiSSCd5zURt9OnYWl17Y+73J93+uyDy5+b9myBfb29mCMYePGjXz8njhxIv+d2Lt3LyIjIzFkyBCt8btp06Zav0NEHfd5cH97pOiUSiUYY5DL5ZBIJAWmFfLcbrCrkkOHDkEkEqFXr144evRoqW88V+Xm5lZqT+6q9O2Nvq3DD4UnKkOaOXmgQ5UG+OtdKBTsvxOYGCK0r+KLZpU8BCxd+TJ9+nQsXboUp0+fhre3N16/fs3/2F+8eDGaNGnCp3337h1mz56N2bNno3r16nB1dUVmZibCw8Mhk8lQvXp1vhctIaTorly5ApFIhO++0324L+7HdWxsrJFKZTrkyhxqzDUSit+lB8VvQoyrf//+ePToEU6ePImJEydi2bJlsLCw0JhWJpNhypQpOHnyJEQiEfz9/dXW//XXXwByh2TR1cuXL/n3b9++xe7du7Fw4UK0atUKP/30E+bNm6f/QZUCixcvVht/nXPp0qVCG+rLEjMzM7i4uCAtLQ0ymczo+/O1rYl2Tj64mfgUSqjH77bO9eFjW0NjL2mhcW0/OTk5OpWPaxyVy+Ua03NzAubNT9vynJwcfr3qcu4zY4zl2w9XZplMpraOK9vUqVMRFBSE06dPw8PDA/Hx8YiLiwMAzJ49G3Xq1OFvML169Qpz5szBnDlz4OrqChcXF2RmZuLFixeQyWRwdXXFlClT+PRENwXd7CO6kclkyMzMxNWrV/m/E20yMjJKqFT5GeyKkLvIHj9+vEk1opsSfXqju1o7oZZ9VSOWpnQa7fUxbiU8VbsQNxNLMMqzl4ClKn/atm2LkydPIigoCLdu3UJ2djZat26NqVOnol+/fmppP//8c8hkMpw/fx5hYWF4+PAhbG1t0aBBA3z22WcYN24cKlSoIMyBEFKGcD+mvb29dd6Ge8S7NF4ElaQHSZFY9PchLG05As2cPYUuTplE8bt0oPhNiHEFBARg9+7dePHiBdasWYPDhw9jwIABaN68OapUqQIgt4H77t27OHjwIH8jq27dumpzeCkUCuzbtw8ikQjdu3cvUlmqVKmCyZMnw8/PD23atMH8+fPRqlUr9O7dW6ftVScuzMjI0Dj+MrdO0zbc+8IaQ7j1qtvmNX36dLUnZqRSKWrWrAm/Th1Q1blKgfmXJVlZWYiJiYGdnR2srDQP78oYQ2ZmJsRiMUQizcOy6GNsvd64czMcMqV6/B7t9bFeQwWVJO64zczMdCoj175lbm6uMT3XczZvftqWm5mZ8etVl3M31UQiUb79cGW2sLBQW8eVzc/PDx9++GG++B0QEJAvfv/vf/+DWCzG+fPnER4ejtDQUD5+9+vXD2PHjqX4rQfGGFJTU2Fvb2+Qv6nyLCsrC9bW1ujQoYPWcxgnMTGxhEqVn4gZqCt27dq18erVK9y9exdNmzY1RJZlhlQqhaOjIxISEuDk5FSkPOTKHHx4cqrO6c/0WCT42OhZWVmIjIyEu7u71j8CpVKJzMxMiEQig92A+e3lVWx9fpr//9duXfBF3c50UisB3bp1w7Vr13Dq1Cm0bt0a9vb2hT6SQwxLqVRCKpXCwcGBbmoWky7nME5iYiKcnZ2RkpKi9UJSaBUqVEBqaipu3ryJVq1a8cu5C6mHDx/Cx8dHbZtTp07h448/RuXKlQsd9qws4uL3gKNz8FosRQ1bZ/zacWqZ75lO8bv8ofgtPIrfhlPa43dMTAx69erFT56p7RzHXaY3aNAAf/75J2rWrMmvi4qKws6dOwEA33zzTYFjlOuiY8eOuHr1Kvr06YNjx47ptM3hw4fRv39/ALkTiXKTn+Y1depU/Pzzz3BwcEBKSgq//JdffkFAQAAAID09XWvP8QEDBuDgwYNo2LAhHjx4oFPZuPjd5eBkLO00qtzcBKf4Xf5Q/BYexW/DKe3xm2OwT5m7KNc2WzApHn16ozepWFfwRnQhfVarLVytc29YVLd2Qh/XDwQuESGECK9GjRoAwF+46+Ls2bMA9HtsvCx6k5kEAIhNT8DByKsCl6bsovhNCCkPatasifv37+OXX36Bt7c3P+Zx3peXlxd++eUX3Lt3T60RHcjtxMYNzVDcRnTgvwk9nz9/rvM2DRo04N8/evRIazpuXd6b9arbF/TbhNve19dX57JxZAoZfn54EHJlwUMEkOKh+E0IKU8M1pA+adIkAMDatWtNYrxxU6Lv2OjL24wyYmlKP3OxGSbW7wsriQUm1OsLczHdkSWEkA8//BCMMezYoXlSqLwiIiKwbds2iEQidO3a1cilK92Yyr/bwk7jbWaygKUpuyh+E0LKC3Nzc0yaNAlPnjzBq1evcPr0aezfvx/79+/H6dOnERMTg6dPn2LSpEkwNzc3enkiIiIAFDx8Sl7e3t78BKWnT5/WmCY9PR3Xrl0DkNtzVlX79u1hbW1d4PZRUVF48uSJxu11RTfBjY/iNyGkPDFYQ3rbtm2xdOlS3LhxA4MGDUJycrKhsi73Fv67X+e0nV0al/lHznXRtFJdHOn4E5pUqiN0UQghpMSJxWKYmZkhNDSUX/bdd9/BzMwMf/31FwIDAwvc/u7du+jWrRvS0tJgaWmJUaPK9w1aVQqmxNrQo0IXo8yi+E0IKW9cXV3RrVs3DBw4EAMHDkS3bt0M0sscyB1LvbBObhcuXMDff/8NAOjUqZNe+X/55ZcAgAMHDqhNZMpZt24d0tLSIJFIMHjwYLV1tra2+PzzzwEAGzZsUBv2hbN06VIAuQ38ffv21atsHLoJXjIofhNCyguDtrgGBATAw8MD33zzDWrWrImuXbvCy8tLp5myZ8+ebciilBlyZQ4uvv5H5/Szmg0uPFE5YS42o6cjCCHlVt7zn5eXF2bNmoU5c+Zg/vz5OHXqFH8BC+T2Bjt+/DjOnj2Ly5cvA8gdt3XJkiWoVq1aSRa9VFMwJS7FheBuQjhaOHsJXZwyieI3IYQYRkxMDPr27YsxY8aga9eucHd358etjomJwd69e7FgwQIwxlCpUiX+KXNOYGAg5s6dCwCIjIyEm5ub2vqAgABs3boV8fHx+Pjjj/Hrr7+iefPmkMlk2LZtG2bNmgUAGDlyJLy88sfMefPm4ffff0dcXBz69OmDbdu2wdPTE+np6fjll1+wceNGAMBPP/2EihUrFrkeuJvg85oPK3IepHAUvwkh5YFBG9Lfvn2LI0eOICUlBUqlEkeP6t5jixrSNVv+8DB0DUXUG50IjRtPWalUQiaTCVwaQkhes2bNglwux6JFi3Dnzh3cvXuXv6CeMmUKn44xBpFIhNmzZ2P8+PFCFbfUEkOEoAeHsLtT2Z94lJQPFL8JKbtCQkIwevRoAICFhQUcHByQmZmJ9PR0Po27uzsOHz4MFxcXvfJ2dHTEiRMn0L17d4SGhqJFixawt7dHVlYW5HI5gNwhWVasWKFxe3d3dwQHB8Pf3x/Xrl2Dl5cXHB0dkZaWBoVCAQD46quv1H6jFAXdBCdlFcVvQkqewYZ2SUxMRIcOHbB3717+ETJ9XiQ/uTIHp17d0Tk99UYnhBBSmHnz5uHWrVv47LPPYG1tnS8em5ubo2fPnrh27RrmzJkjdHFLJSUYXmckIDiCxlwlhBCS37hx4xAbG2u0/IODg7Fv375C07m6uiI4OBhjx45F8+bN4ezsDKlUCqVSiVq1aqFPnz7YunUrHj9+jKZNmxapLM2bN8fjx48xadIkeHp6Qi6Xw9bWFu3bt8eWLVtw6tQpWFpaat2+V69eePDgAb799lu4ubkhMzMTFSpUQNeuXXHo0CHs2LGDv+lfHNxNcJp4lBBCSHEYrBvVokWLEB4eDgDw9/fH2LFj0ahRI1SoUMEgga88Co64AgVT6pSWeqMTQgjRVYsWLXDo0CHk5OQgNDQUb9++hUKhgJOTE3x9ffnJv4h2DMCJmFsY7PGh0EUhhBBSymzYsAHbt2/H8OHD8cMPP6Bu3brFzlMmk+HQoUNYuHAhnj59qtPNbgsLC/j7+8Pf379I+wwMDCx0XhUAqFq1KpYvX47ly5cXaT9169bF5s2bi7StrlRvglPsJoQQUlQGa3k9duwYRCIRhgwZgl27dhkq23ItWI/Zxak3OiGEEH2ZmZmhUaNGQhfDJIkA9K7ZWuhiEEIIKYWGDRuGX3/9FRs3bsTGjRvRqlUrDB06FJ999plew6fI5XJcu3YN+/btw+HDhyGVSsEYQ+3atdGlSxcjHkHZRDfBCSGEFJfBGtK5R9eGDx9uqCzLNbkyB1JZhk5pqTc6IYQQTeiJMOMQQ4RqNk4YUKeD0EUhhBBSCu3YsQPjxo3DzJkzce7cOdy+fRt///03vv/+e9SsWRMtW7ZE06ZNUaVKFVSsWBEVK1ZEZmYmkpKS8P79e4SHh+POnTt48OABP+4xYwxOTk6YOnUqxo8fX+BwKUQzuglOCCGkuAzW+urs7IzY2FjY29sbKstybfnDw8hhikLTiUC90QkhhGjWrVs3mJubFzsfkUiEFy9eGKBEZYMSDAGN+tNNbEIIIVq1aNECZ86cwd27d7FixQocOXIE2dnZiI6ORkxMDI4cOVLg9qrziNWrVw9jx47F119/DVtbW2MXvUyim+CEEEIMwWBXgH5+fjhw4AAePXqEZs2aGSrbckmfSUYdLWzpQp4QQohGhprojHq2/0ciEqODS0O0cPYSuiiEEEJMQIsWLbB3715IpVIcPXoUly5dwrVr1wq8QW1jY4PWrVvDz88PH3/8MVq0aFGCJS6b6CY4IYQQQzBYFJk8eTIOHz6MoKAgDBgwAFZWVobKutzRZ5LRQXU6G7k0hBBCTJWrq6tBeqST/0hEYnzn86nQxSCEEGJiHBwcMHToUAwdOhQA8O7dO7x69Qrv3r1DUlISrKysULlyZVSuXBl169aFRCIRuMRlB90EJ4QQYigGa0hv1qwZtm7dim+++QbdunXD1q1b4eVFgaooDr28rlM6EUCPphFCCNHq7Nmz8PHxEboYJk+1P/4I7x6oYl1BqKIQQggpI7hGc2J8dBOcEEKIoRisIZ2bZNTHxwfXr19H/fr10bhxY3h5ecHGxqbAbUUiEbZt22aoopg0uTIHWTkyndJ+XLM1PZpGCCGEGFlV60p4DSlq2DrD351uYBNCCCGmhG6CE0IIMRSDtcLu3LmTH0NVJBKBMYaQkBCEhIQUuB1jjBrSVQRHXEFaTmah6SQQ44eGn5VAiQghhJDybXT93lj0/BCmNPSnG9iEEEKICaGb4IQQQgzJYFeDtWrVosnIDEDXYV0cLG3oYr4cioqKQr169VCrVi2EhYUJXRxCCCkXGlVyx8nuCyjukiKj+E0IISXPQmJBN8FJsVD8JoTkZbCI8vLlS0NlVW7pM6zLQPdORi0LKTne3t6Ijo5WW2ZlZYVq1arBz88PEyZMoDGOCSFEYHQRTvKi+E0I0deHH36o9zYikQhWVlZwdHSEp6cnWrduje7du0MsFhuhhGXL7o5T4OJcVehikFKG4jchpDjoqrAU0XVYF1uJJU0yWgZ5eHjwEw6lpKTg+fPn+PXXX/Hbb79h7969+Pjjj2FmZgYvLy+4uroKXFpCCCGEABS/CSG6u3z5Mj8Mat6nuRljAKDT8qpVq+KXX37BF198YeQSmza6CU4KQvGbEFIUFFlKEV2HdbE2t6QfBWXQ1KlTMXToUP7/b968wfDhw3Hx4kWMGjUKT58+RfXq1Qudd4AQQgghJYfiNyFEVx06dIBIJEJcXBzCw8MB5DaQ16lTh2/Qe/fuHSIiIvjGdi8vL1StWhVSqRTh4eHIzMxEfHw8hgwZgpiYGEydOlXIQyLEZFH8JoQUBT0PVkrIlTmQKeQ6pe3vRr3Ry4OqVati27ZtsLS0RGJiIi5cuCB0kQghJiIyMhIRERHw8vISuiiElDsUvwkh2ly+fBkzZszAu3fvUKlSJaxatQoJCQl49uwZbty4gRs3buDZs2dISEjAypUrUbFiRbx79w7Tp0/HP//8g5SUFPz222+oUaMGGGOYOXMmQkNDhT4sQsoEit+EEF0YrCE9Ojq6WK/yLjjiCqTyjELT2ZlZ0bAu5YiLiws8PDwAAM+fP0dUVBSsra3h7e2dL21UVBS+++471K9fH46OjqhcuTLq16+PAQMGIDg4WC2tQqHA8ePHMWrUKDRr1gxVq1ZFpUqV0KRJE8yYMQMJCQklcnyEEOOoXbs2ateuDTMzenqJECFQ/CaEaPLixQv0798fIpEIN2/exPfff4+KFSvmS1exYkWMHz8eN2/ehEgkwoABAxAeHg4zMzP4+/vj6tWrqFChApRKJdavXy/AkRBSNlH8JoQUxmBX2O7u7kXeViQSIScnx1BFMUknYm7rlM7KzIKGddEg83kmlKnKfMsZY5DJZcgxz4FIJILYXgxrD2sBSlh03LiIBYmKikL79u2RkJAAGxsbeHl5QSKRICYmBsePH0dUVBQGDBjAp4+Li8OAAQMgFotRpUoV1K1bFxkZGYiKisKKFStw5MgRXLlyBVWr0uQ8hBBCjIfiN8VvQsqToKAgpKamYtmyZfD09Cw0vaenJ6ZOnYoff/wRQUFB2Lx5MwDAzc0No0aNwtKlS3Hp0iVjF5uQfCh+U/wmpLwyWIusLicbol2TSnXxKr3wu5A0rEt+mc8zEdJQ93HLGj9sbDLBPD4+Hi9evAAA1K1bV2u6lStXIiEhAUOGDMGKFStgZ2fHrwsLC8P16+rj79vb22PLli3o1asXKlWqxC9PTk7G3LlzsXHjRsyaNYv/sU4IIYQYGsVvit+ElDdnz56FSCSCn5+fztt07NgRAHD+/Hm15R9++CGWLl2K2NhYg5aRkMJQ/Kb4TUh5ZrCG9B07dhSaJj09HWFhYTh8+DBev36Ntm3b4ttvvzVUEUyWXJmD+wnPCk3nYlWBhnXRQNOdcEOmF8rbt28xYsQIZGdno2LFivjoo4+QlJSkMS0X7MePH68WxAHA29s736Nojo6OGDJkSL58KlSogBUrVuDEiRM4fPgw1q9fT0NDEEIIMQqK3xS/CSlvXr9+XeRt4+Pj1f5fpUoVAEB2dnaxykSIvih+U/wmpDwz2F/osGHDdE4bFBSE8ePHY/PmzWjbti2WLVtmqGKYpOCIK3idqfkErapF5Xo0rEsZtmzZMv6GVEpKCp4/fw6ZTAZzc3OsW7cO9vb2WgN5jRo1AAC///47GjRoAJFIpNM+L1++jD///BPPnz9HamoqlMrcHzlSqRQZGRl4/vw56tWrZ4CjI4SUd/fv38fx48dx7949hIeH4927d5BKpXBwcEC9evXQq1cvjBkzRq2Xjr7evHmDZcuW4cSJE4iOjoa1tTV8fX0xbNgwjBgxQudzIyH6oPhNCNGVo6Mj3r17h2vXruGDDz7QaZurV6/y26pKT08HADg5ORm2kISUExS/CSFFIUirrLm5OTZs2ICnT5/il19+wUcffYTu3bsLUZRSQdfx0f9Nem7kkhAhPX/+HM+f537GFhYWqFq1Ktq3b48JEyagcePGBW47atQo7NmzB4sXL8bevXvRrVs3tG3bFh07doSrq2u+9DKZDEOGDMHx48cLzFfbDwdCCNHX9u3bsW7dOv7/VlZWsLa2RlJSEm7cuIEbN25g5cqVOHbsGNq0aaN3/vfu3UP37t2RmJgIALCzs0NqaiquX7+O69ev4+DBgzh27BgsLS0NdkyEABS/CSG6a9euHX7//XcsWbIE/fr1K3DoCCD3/LJkyRKIRCK0bdtWbd3jx48BgMZUJqSIKH4TQopCLOTOx4wZA8YY1qxZI2QxBNekUsE/oDi9a7Y2ckmIkDZv3ozMzExkZmYiJSUF4eHh2L59e6FBHAAaN26Mc+fOoUuXLnj9+jW2bt2K4cOHw8PDA3369MHTp0/V0gcFBeH48eNwcXHBtm3b8PTpUyQnJ/P75xqxyvskwIQQw2nVqhV+/vln3Lx5E+/fv0dmZiakUilSU1Oxc+dOVK5cGQkJCejbty9SUlL0yjslJQW9e/dGYmIi6tWrhzt37iA1NRXp6elYu3YtzM3NcfbsWUyaNMlIR0fKM4rfhBBdTZw4ESKRCO/fv0fr1q2xbt06jTEvOTkZa9euRZs2bfD+/XuIRCL88MMPamlOnDihsYGdEKIbit+EkKIQdJwQbqbyu3fvClkMQek6Pnp1GycaH50U6IMPPsDx48eRlpaGmzdv4sqVK/jtt99w/vx5fPzxx7h37x4qVKgAADhw4ACA3B8PXbt2zZfXq1evSrLohJBy4Msvv9S43M7ODsOGDUO1atXQvXt3vH37FidOnMDgwYN1zjsoKAjx8fGwtrbGyZMn4e7uDiC3d9G4ceMglUoxY8YMbN68GRMnToSXl5dBjokQQ6D4TUj54efnh3nz5mHWrFlISkrC+PHjMXHiRLi7u/Njnr99+xaRkZFQKpVgjAEA5s+fj3bt2vH5vHjxAn/++ScYY+jZs6cgx0JIeUfxm5DySdAe6dzdd317npUlwRFXEKfD+OhNnTxpfHSiEzs7O3Tt2hULFixASEgI6tSpg9evX+PMmTN8mqioKABA69b5n3JITEws1kRIhBBSFKrnI30vJn799VcAwKBBg/hGdFXff/897OzsoFAosHfv3uIVlBAjofhNSPkwc+ZM7N27F5UrVwZjDAqFAs+fP8fNmzdx8+ZNPH/+HAqFAowxVKlSBfv27cP06dPV8qhbty5ycnKgVCrRq1cvgY6EEAJQ/CakvBG0IX3Xrl0AgGrVqglZDEGdiLkNpkM6Gh+dFIWNjQ18fX0BAHFxcfxya2trALk9XvJatWoVFApFyRSQEEL+37Vr1/j3hY0ZqyosLAzR0dEAoLVXnp2dHfz8/AAAZ8+eLUYpCSkZFL8JKdu++OILREdHY//+/fjmm2/QunVreHl5wcvLC61bt8aIESOwb98+REVFYdCgQUIXlxCiI4rfhJR9gjSkP3v2DKNHj8auXbsgEonK9V303jV1m62dxkcnBRk/fjwOHjyIjIwMteXXr1/H5cuXAQBNmzbll3NjKf74449IS0sDADDGsHfvXqxcuRJWVlYlU3BCSLmWnZ2Nly9fYu3atRg6dCgA8GNL6urRo0f8+wYNGmhNx60LDQ0tYmkJMTyK34SUXxYWFhg4cCA2b96MGzdu4MmTJ3jy5Alu3LiBLVu2YNCgQTRBNiGlFMVvQsovg40VUqdOnULTKJVKJCcnIzU1lV9WpUoVzJw501DFMDn93NphS9gpKJhSaxqJSIx+bjSJDNHu9u3b2LJlC8zMzODh4QE7Ozu8ffuW76X5xRdfoGPHjnz6n376CRcvXsSJEydQt25deHh4IC4uDnFxcfjf//6HmJgYtd6hhBBiSFZWVsjOzs63vF27dti3b59eDQeqj8JWr15dazpunVQqRVpaGuzs7PQoMSHGQfGbEEIIMT0UvwkpvwzWkP7y5Uu9t2ndujV27NhRrod2+f3lXwU2ogOAginx+8sbGOzxYQmVyrSI7fV7sELf9KZg2bJlOH78OG7cuIFXr14hIiICLi4u6Nq1K0aNGpXvqY9mzZrh3LlzmDt3Lm7fvo2wsDB4eHggICAAY8aMQffu3QU6EkJIeeDi4oKsrCykpaUhPT0dANC5c2csW7YMtWrV0isv1ZvzNjY2WtOprktNTdXYkJ6dna3WwC+VSgEAcrkccrlcr3KZMrlcDsYYlEollErNv1EYY2CMQSQS8ZPh6Utsp2f8thMXeV/GxNVFYWk0vV+6dClOnDiBv/76C7GxsXz87tKlCx+/VdM3bdoUZ8+exbx58/j4XbduXQQEBGD06NHo0aOHzmUqrLzaPntiHNznRXVffNwknXK5HBKJpMC0peXcnpOTg/fv3wMAKlasCDMzmhuLlF50/U3X34SUZyJmoCuSr7/+utA0YrEY9vb2cHd3R8eOHdGkSRND7LrUk0qlcHR0REJCApycnNTWfXFpEV6lJxSaRw1bZ+zvPMNYRTSKrKwsREZGwt3dXeujSkqlEpmZmRCJRBCLix5gM59nQpma/6KDMQaZXAYLc4vcfdiLYe1hXeT9EN0olUrIZDLY29sXegFDDEupVEIqlcLBwaFYf1NEt3MYJzExEc7OzkhJSYGDg0MJlbBsePv2LXbv3o2FCxciOTkZP/30E+bNm6fz9osWLeKfbJPL5VobH7Zs2YKRI0cCyO3FrukmfmBgIObOnZtv+b59+wpspC9rzMzM4OLigpo1a8LCwsKo+8p+kQ1FWuHjgkrsJLCsS0McEEIKJ5PJEBMTg/j4eOTk5BSYNiMjA//73/8Eid9PnjzB+vXrcf78eTx79oy/mSISieDp6YmuXbti9OjR8PHxKdFylQUFXX+XZXT9XX7R9bdw6PrbcEzl+ttgt7p37Nih9zYRERG4fv06AODLL780VFFMSu+aH2Dj0z8LTCMCjZFeGG3BmTEGs2wzWFpaQiQSlXCpCCGEFKZKlSqYPHky/Pz80KZNG8yfPx+tWrVC7969ddre3t6ef5+RkaH1h5TqGJaq26iaPn06fvjhB/7/UqkUNWvWROfOncvdhXhMTAzs7Oy0/ohljCEzMxNisbhY8dXSR3PjOGMMMpkMFhYWFL9LEFfvdnZ2dDFYwhhjSE1Nhb29PX3niykrKwvW1tbo0KGDThfiQpg+fTqCgoL43vOqGGMICwtDeHg4NmzYgClTpmDRokWClJMQbej6mxBSXgn6zNi1a9fw9ddfQywWl9uG9Lr2rgWuF0EEVxsnDKjToYRKRAghhJS8Vq1aoX379rh69So2b96sc0O6q+t/cTQ2NlZrQ3psbCwAwMHBQev46JaWlhrHZzc3N4e5ublO5SkLFAoF31NNW2OqUqnkL5CNeaEsEonoQrwEqfaIpYb0ksUN50J1X3zcDT5dzt1CnNu///57rF+/nv97q1+/Pj744AO4uLiAMYY3b97g77//RmhoKBQKBZYuXYr09HSsWrWqxMtKCCGEEHWlYvC10jjeZUmQK3Ow8vERiABoqwEGhkkN+sFcXCo+KkIIIcRouAlBnz9/rvM2DRo04N8/evQI9evX15ju0aNHAECPyBNCCBHMX3/9hXXr1kEkEsHHxwebN29G27ZtNaa9efMmRo8ejYcPH2Lt2rUYOHCg1rSEEEIIKRnU3UFAwRFX8DojUWsjOue5NK5EykMIIYQIKSIiAoD2oVc08fb25icoPX36tMY06enpuHbtGgCgW7duxSwlIYQQUjSbNm0CALi7u+Ovv/4qsGG8TZs2uHr1KurUqQMA2LhxY4mUkRBCCCHaUUO6gE7E3C60ET033S2jl4UQQggxFoVCUejTZxcuXMDff/8NAOjUqZNe+XPDwx04cAAvX77Mt37dunVIS0uDRCLB4MGD9cqbEEIIMZRr165BJBLhxx9/hKOjY6HpHR0dMW3aNDDG+BvChBBCCBEONaQLqHfNDwpNQxONEkIIMXUxMTFo2rQpNm3ahIiICLVG9ZiYGCxZsgSffvopGGOoVKkSJk2apLZ9YGAgP1a2pobygIAAuLi4ICMjAx9//DHu3bsHAJDJZNiwYQNmzZoFABg5ciS8vLyMd6CEEEJIAeLj4wEATZs21XmbZs2aAQDevHljlDIRQgghRHc08LaABtTpiJ3PziFLIdO4XgTA1caZJholhBBi8kJCQjB69GgAgIWFBRwcHJCZmYn09HQ+jbu7Ow4fPgwXFxe98nZ0dMSJEyfQvXt3hIaGokWLFrC3t0dWVhbkcjmA3CFdVqxYYbgDIoQQQvRkZWUFmUymFvsKk5aWBgAaJ8MmhBBCSMmiHukCCkmM0NqIDuROQBrQqD9NNEoIIcSkubq6Ijg4GGPHjkXz5s3h7OwMqVQKpVKJWrVqoU+fPti6dSseP36sVy89Vc2bN8fjx48xadIkeHp6Qi6Xw9bWFu3bt8eWLVtw6tQpaoQghBAiKHd3dwDAsWPHdN7m+PHjAMCPlU4IIYQQ4VALrUDkyhwEPTwIMURQahkp3UpigcaV6AcTIYQQ02ZhYQF/f3/4+/sXafvAwEAEBgYWmq5q1apYvnw5li9fXqT9EEIIIcbUq1cv/Pvvv1i7di169uyJjz76qMD0Fy5cwJo1ayASidCrV68SKiUhhBBCtKEe6QIJjriC1xmJWhvRASBLIUNwxNUSLBUhhBBC8pIrc4QuAiGEkDJg4sSJcHBwgFwuR8+ePTFu3Djcu3cPSqWST6NUKnHv3j2MHTsWPXv2hFwuh4ODAyZOnChcwQkhhBACoIg90ufNm2eQnf/7778GyccUnYi5XUATumq6Wxjs8aHRy0MIIYQQzYZe+RlLO41CM2dPoYtCCCHEhDk7OyM4OBiffPIJZDIZNm7ciI0bN8LCwgKVKlWCSCRCYmIiZLLc4T8ZY7CwsMDBgwfh5OQkcOkJIYQQUqSG9MDAQIhEIkOXpVzpXfMDbHz6Z4FpRAB612xdMgUihBBCiEYyhQw/PzyIXztOpXlLCCGEFEu3bt1w69YtjBw5Enfv3gUAZGdnIy4uLl/ali1bYvPmzWjcuHFJF5MQQgghGhT5apAxXfpTE20G1OmInc/OaZ1sVAwRqtk4YUCdDiVcMkIIIYTkFZuegIORV/G/uvSUGCGEkOJp0qQJ/v77b9y5cwfnz5/Ho0ePkJSUBACoVKkSGjRogC5duqBly5YCl5QQQgghqorUkH7p0iVDl6PcCUmM0NqIDgBKMAQ06k893wghhJBSgAHYFnYaXVyboYp1BaGLQwghpAxo2bIlNZYTQgghJqRIrbQdO3Y0dDnKFbkyB0EPD0IMkdbJRq0kFmhcqU4Jl4wQQggh2iiYEmtDj2Je82FCF4UQQgghhBBCSAkTC12A0uL+/fuYO3cuPvnkE9SrVw9OTk4wNzeHk5MT2rVrh4ULF/KP2xVXcMQVvM5I1NqIDgBZChmCI64aZH+ElGUikahMzNnQqVMniEQiXL58WeiiaLRz506IRCJ89dVXQheFEMEomBKX4kJwNyFc6KIQYvIofpcMit/CiI6ONsqLEEKERvG7ZFD8Lr2oIf3/bd++HYGBgTh+/DjCwsKQkZEBa2trJCUl4caNG/jpp5/g7e2NmzdvFntfJ2JuF9CErpruVrH3RUo/b29vWFtbY/fu3QbJLyQkBAsWLMCxY8cMkl95kpycjMDAQKxcuVLoohTo7NmzEIlEsLa2hlQqLTT927dvYW5uDpFIhDt37pRACQkpu8QQIejBIciVOUIXhQiM4nfpQfGblDbu7u4Gf9WpQ08rE2IIFL9LD4rfxBRRQ/r/a9WqFX7++WfcvHkT79+/R2ZmJqRSKVJTU7Fz505UrlwZCQkJ6Nu3L1JSUoq1r941P0Bh9+9EAHrXbF2s/ZDy6cGDB1i4cCGOHz8udFFMTnJyMubOnVvqA3mXLl3g6uqKrKwsHD58uND0Bw4cQE5ODry9vWkcTkKKSQmG1xkJ9NQYMTiK30VH8ZuUNowxo7wIIaUPxe+io/hNTBHNZPn/vvzyS43L7ezsMGzYMFSrVg3du3fH27dvceLECQwePLjI+xpQpyOOR9/C64xEjT3TxRChmo0TBtTpUOR9EELKLrFYjP/9738ICgrCnj178PXXXxeYfs+ePQCAoUOHlkTxCCnzGHKfGhvs8aHQRSGEmBCK3+XHjh07hC4CIYQQA6H4TVRRQ7qOWrf+r3f4q1evipWXudgMI7x7Yt4/ezSuV4IhoFF/mIvp49HHu3fvsGvXLpw6dQoJCQkwNzdH7dq1MXDgQPTt2xcWFhZCF5EQgxk6dCiCgoJw+fJlvH79Gq6urhrThYeH486dOxCJRMW6AUgI+Q89NWZYb968wc6dO3H69GkkJibCwsICderUwaBBg/DJJ5/AzIx+D5Gyg+J3+TBsWOmZlDoxMRHHjh3DhQsXcP/+fURFRSEnJweVK1dGixYtMGzYMPTr10/vfF++fAl3d3ed03/11Vf5bjB89dVX2LVrV6HbyuVyigWl0OvXr7Fjxw6cO3cOSUlJsLS0hIeHB7744gv06tWLPjNSplD8Jhwa2kVH165d49/XrVu32PldjX+ocbkIQOdqjdHC2avY+ygvMjMzMWHCBHh4eGDWrFm4ceMGwsPD8fjxY5w8eRLDhg1DvXr1sG/fPqGLqpcFCxbA2toaCxYsQEpKCgICAuDp6QlHR0f4+vpi8eLFyMlRH6PX29sbI0eOBJB7F9Ta2pp/devWLd8+zp07h/79+6N27dpwdHRE3bp1MXLkSERERORLGxUVBWtra3h7ewPInVegXbt2qFy5MqytrdXKYGtri+joaJw5cwadOnWCo6MjHBwc0LVrV7W/pbzkcjnWrFmDVq1awcHBAba2tmjcuDEWLlyIjIwMveovIiICS5cuRadOnVCzZk1YWlqicuXK6NGjB/7888986b/66iv+YiAqKoqfREXbZCpPnz7F8OHD4ebmBktLSzg5OeHjjz/GxYsXtZYpISEBY8eORfXq1WFlZQVvb2/Mnz8fcrlcr2MDgEaNGqFRo0ZQKpUFfre5u+F+fn5wc3MDANy6dQtTp05FixYtUKVKFVhaWqJmzZoYOnQoHj9+rFc5CpsE5fLlyxCJROjUqZPG9UlJSZg5cyYaNGgAW1tb2Nvbo3Xr1tiyZQuUSmW+9Dk5OVi1ahVatWoFe3t7WFpawtXVFW3btsWcOXOQnJysV/kJ0ZcYIrjaONNTYwaQlpaG0aNHw9PTE4GBgbh16xaePXuGx48f4/jx4xg8eDB8fHx0eoS2NKH4TfG7IBS/KX6XNBcXFwwfPhx79+7FkydPoFQqYW5ujtjYWBw9ehSfffYZevXqpfffqkQiQdWqVQt8OTo68ukLGt7AysqqwHzKwsSGZUlKSgqGDx8OLy8vLFiwALdv38azZ8/w6NEj/PHHHxg4cCAaNmyIEydOCF1UvVD8pvhdEIrfFL95jGiVlZXFIiMj2Zo1a1jFihUZAObh4cGysrL0yiclJYUBYAkJCYwxxu68DWPtj0/S+jr36p4xDqfEZWZmstDQUJaZmak1jUKhYGlpaSw9PZ1lZmbq/UpISGBt2rRhyH3Snn/Z29szCwuLfMvnzZtXpP0Y81WrVi0GgG3evFlt+cyZMxkA9t1337F69eoxMzMz1rhxY1a7dm3+eL7++mu1bfr168c8PDwYAFalShXWpk0b/jV69Gi1tOPGjePzqVKlCmvSpAlzcHBgAJiDgwO7ePGiWvqnT58yAKxWrVrs22+/ZQBYjRo1WLNmzViFChXyHc/s2bOZSCRilSpVYi1atGBOTk4MABOLxSw4ODjfdyEjI4N9+OGHfJnq16/PGjVqxMRiMQPAmjRpwv8NqeLS5zVixAgGgNnZ2TEvLy/WokULVq1aNT79kiVL1NIvXLiQtWjRggFglpaWrF27dmovVb/99hv//bK3t2dNmjRhLi4uDAATiURs9erV+coTFxfH6tSpwwAwMzMz1qRJE+bp6ckAsN69e7MOHTowAOzSpUta/17y+vnnnxkA1rhxY36ZQqFg79+/ZwqFgjHG+H1u2bKFT1O3bl0GgDk5ObEGDRqwxo0bM0dHRwaAWVtbayzDjh07GAA2bNgwnZZzLl26xACwjh075lv36NEjVr16dQaAWVhYMB8fH1a3bl0mEokYANa/f3+mVCrVtvn888/5z7Bu3bqsZcuWrGbNmkwikTAA7J9//tGl6nSiyzmMk5CQwACwlJQUg+2fCI+L3x0OfK8Wp++8CxO6aEZVEvE7Pj6eNWvWLF+cdnBwYObm5vmW//zzz4LHa4rfFL8pfuei+G1aALBWrVqx9evXsxcvXvDLIyMj+b83AGzIkCEG3/d3333Hfz/fv3+fb/2wYcMK/B4WVd7r7/KiJOL3q1evWMOGDTXGbzMzM7VlIpGIrV27VvB4TfGb4jfF71wUvw2DGtI1sLS0zBcYALB27dqxqKioQrfPyspiKSkp/CsmJoYBYHFxcSw9K4MNPr+IdTk2hX14LCDf66NjAWzw+cUsPSuDyWQyk35JpVL2+PFjlp6ezhQKhcZXTk4OS01NZenp6SwjI0OvV3p6OuvTpw//+VhbW7MJEyawhw8fsoyMDJaWlsaOHTvGunbtqvY57ty5U+99GfPFBb5NmzapLZ8xYwYDwMzNzVn79u3Z8+fP+XUHDx5UO3Gpbrdp0yb+x7C2fa5evZoBYG5ubuz06dP88tTUVDZnzhwGgFWvXp0lJSXx6548ecIAMIlEwmxtbVlwcDC/LjExMd/xmJmZsYkTJ7KsrCymUChYdnY2mzJlCv9D4dWrV2rfhR9++IEBYK6uruzOnTv88rCwMFavXj0GgPn7++f7DnGfa97lJ06cYDdu3GA5OTlqyy9fvsyqVavGJBIJCw8PV1v34sULBoDVrl1b63f2n3/+YZaWlszKyopt3LiRyeVyft0ff/zBHBwcmEQiYffv31fbrm/fvgwAa9asGXv58iW//Ny5c8ze3p5vOLpw4YLWfed9vXr1iv8ePHjwgP+bev/+PcvJyWHXrl1jAJiVlRVLSkrit9uxYwd79uyZWl7Z2dls8+bNzMzMjNWpU0ftuBQKBdu2bRsDwL788kudlnOvCxcu8IFcdblUKuV/UHz//ff8jw+FQsEePnzIfH19GQC2Zs0afvnff//NALCaNWuyR48eqeX3/v17tmnTJrW6Le4rPT2dPX78mEml0kLPd3FxcWX+Qrw8ytuQ3vHEZDbr7k6hi2V0xr4Qz8jIUIvNtra2bPLkyezp06csMzOTpaens99//5117txZLX7/9ttvgl9863MhzsXvFy9e8OsOHTrEn7f//fdfte02b97Mx29t+1yzZg0fv8+cOcMvT0tLY4GBgXz8fv/+fb4LcS5+Hzx4kF+XlJSU73i4+C2TyRhjjMnlcjZ16lQ+fr9+/VrtuzB58mQ+ft+7919nkGfPnvHxe8CAAfm+Q9ouxE+ePMlu3bqV70Lu6tWrfPx+/vy52rrIyEg+fmsTEhLCx+/NmzfzF7yMMXbs2DE+fv/7779q2/Xr14+P39HR0fzyCxcuqMVvfS7EX79+zX8PHj16xBhTvxD/66+/+PidnJzMb7dr1y61hlDGcj+frVu38vFb9bgYM/yFeFpaGh+/x48frxb3Hj9+zMfvtWvX8svv3r3Lx+/Q0FC1/FJSUtiWLVvU6ra4TOVCvKRcvHixwPWjRo3i/x4N/TlwndG0NdJTQ7phGTt+p6WlsXbt2qk1nv/4448sPDycXx8cHKyWRiwWs2PHjgkesyl+U/zmUPym+F1cNGiVBi4uLsjKykJaWhrS09MBAJ07d8ayZctQq1atQrdfvHgx5s6dm2/5pUuXYGNjA3/4FJxBGnDu9Nkilb00MTMzg4uLC9LS0iCTyQye/927d/mZsR0cHHD06FE0adIEAJCdnQ0g93EaPz8/rFixAvPmzQMAzJ49G71794ZEIjF4mYqCMQYg95EZrtwAoFAoAOTW48aNG+Hk5MSv79KlC3r16oXjx4/j5MmT/CNDXD7c9qr5cWQyGRYuXAiJRIJdu3ahUaNGaukmTpyIf/75B8eOHcNvv/2GQYMG8dtx+U6fPh1du3bltxOLxfx77njq1auHOXPmIDMzE5mZmQCAGTNm4OzZswgJCcHq1asxffp0AIBUKsXGjRsBAMuWLYOHhwekUikAoEqVKli3bh0++ugjHDp0CCEhIRrHY+TSc9q1awcASE1NVVveuHFjTJ8+HePHj8evv/6KyZMn8+vS0tIAAEqlMl9+nNmzZyM7OxtLlizBwIED+W0AoGPHjpg5cyamTZuG5cuXY82aNQByH3M7evQoAGDt2rVwdHTk82/RogV+/PFHzJw5EwCQkZGhdd952draomPHjrh48SK2b9+OOXPm8OtSU1P5cSh79OgBkUjE59u3b1+Ndebv74/z588jODgYFy5cwAcffMCvy8rKApD7+J/qdtqWc7hHAnNyctTWb968GS9evEDv3r35v01ufY0aNbBx40Z06NABv/zyC4YMGQIAePDgAQCgT58+qF69er79DRgwQONxFZVMJkNmZiauXr2a7zHOvPR99JGYJolIjO98PhW6GCbvypUrOHfuHADAyckJZ86cga+vL79eLBaje/fu6NixI5YuXYqlS5cCAObMmYM+ffqYzCP+ZmZm2LFjh9oYmh9//DF69+6No0eP4syZM/zj2rpQjd/79+/nf/MAuUMsTJs2Df/++y/++OMPHDlyBP/73//UtlcoFJg1axZ69+7NL1N9NJxTr149BAUF8b+TzMzMsHTpUpw/fx7379/Hxo0b+d+5UqkUGzZsAACsW7cOzZo14/Px8PDAr7/+ilatWuHgwYNYtGiRTsMj9uzZU+NyPz8/zJ8/H9988w1+++03zJgxo9C8VM2dOxfZ2dlYtWoVvv32W7V1ffr0wcKFC/H9999j9erV2LZtGwDg+fPn+OOPPwAAv/76K2rWrMlv8+GHH2Lu3Ln44Ycf9CoHAFSrVg0fffQRzp49iz179mDx4sVq67nHwvv06aM2NMaXX36ZLy8zMzOMGDECly9fxp49e3Dr1i20bdtW7zLpavv27Xjx4gX69euHVatWqa3z8fHBvn370KRJEyxfvhzjxo0DADx79gwA0L9/f9SvX19tGwcHB3zzzTdGKy/JvY4tyIgRI7Bp0yYAuddXqt/z4jhy5Ajev38PAPQZlxGnT5/GX3/9BSC3zeTs2bPw9PTk10skEvTu3RtdunTB3LlzsWrVKiiVSgQGBqJLly4Uvyl+U/xWQfHbdJlsQ/rOnTsLnSm3IKdOnUKPHj00rnv58iX//u3bt9i9ezcWLlyIVq1a4aeffuIbfbSZPn262h+lVCpFzZo10blzZ0wJ3YnX6YmFls/V1gnb/SYXmq40y8rKQkxMDOzs7GBlZaUxDWMMmZmZEIvFegfWnTt38u8XLVqk1uinmr9MJsPUqVNx/fp1XLx4EdHR0bhy5YrWE31J447bzMwMlpaW/HIuAHbt2hV16tTJt13Lli1x/PhxxMTEqG3HTeoikUjUlnP+/vtvvHnzBs2bN9c6VmGfPn1w7Ngx3Lp1i58sSXWy1i+//FJj3qrHM2LECNjZ2UEsVp+KYdy4cRg5ciSuXLnCB57r168jIyMDtWrVwqBBg/J9Fzp16oQ2bdrg5s2buHnzJho3bpxvvw4ODvmWvXv3Dvv378ft27fx7t07vtE3JSUFABAWFqa2nZ2dHYDcRhxN+clkMpw7dw4SiQSjRo3SmGbAgAGYNm0abt68ya+/efMmGGPo0KGDxu/puHHjMHfuXMhkMtjY2GjMV5thw4bh4sWLOHLkCIKCggDkNqJbWVnxjfdfffVVvjyfPn2KAwcO4OHDh3j//j3fUBwdHQ0g98dH165d+fTc37C5ublaXtqWc2xsbADkfi9V1586dQoAtNZj27Zt4ebmhsjISEilUtSoUQNeXrlzR1y/fh05OTmoVKmSzvVUFFlZWbC2tkaHDh20nsM4iYmFn9eJ6Rvh3QNVrCsIXQyTxzXYAEBQUJBaI7oqkUiE2bNn48qVK7h16xaePn2Ka9euoUMH0xifvmvXrqhRo0a+5c2bN8fRo0cRGRmpV363b99GfHw8mjdvrnYRrqp37974448/cO3atXwX4gB0mvRqxIgRGpePHTsW33zzDc6cOcNfiKvG708/zX+TqWXLlnz8PnfunM7zDL179w779u3D7du38fbt23zxOyQkRKd8ODKZDCdPnoREItE6pugnn3yC77//HleuXOGXnT17lo/fmr6n33zzDX788ccidRgZOnQozp49i3379mHRokX8crlcjuDgYD5NXk+fPsX+/fvx8OFDJCUl5YvfISEhRr0QP3LkCADtDaONGjWCm5sbIiIi8OrVK9SoUYNvwLhw4QKSkpKMHr+JflR/43AdeQyBa9Dy9PREx44dDZYvEc7mzZv596tXr1ZrRFclEomwcOFCXL58GSEhIbh//z7u3r1b4Dj5pQnFb4rfBaH4TUy2Ib2kVKlSBZMnT4afnx/atGmD+fPno1WrVmp3A/OytLTU2Mhobm6OHrVaYtPTP8EK2KcIQI9arWBubl78AxCQQqGASCSCWCzO15jKUSqVfKOpPg3p2dnZ/ORjFStWxP/+978CtxeLxRgzZgw/EcX+/fvRq1cvnfdXEvJOqsG9r1OnjsZjq1KlCgAgPT1d43Z533O4ySyio6Px0UcfaSwLF+ji4uLyfT7Ozs6oXLlyocfj5eXFf/6quEAWHh7Or3v+/DmA3Lvo2p4U8PX1xc2bN/H8+XON36e8y86ePYsBAwbwx6LJ+/fv1bbT9p7z/PlzZGVlwcLCQus5gOuRHxsby+fB3cmtX7++xnwdHR1RvXp1REZGFvj3osnnn3+OcePGITo6Gn/99Rfat28PILfHSGJiIpydndGrVy+1PBcvXoyffvpJ42QinOTkZI31kfcz1bY87/q87x8+zJ1wOTAwEEuWLNFYhoSEBAC538NatWqhXbt2+OCDD3D79m3Url0bXbt2RYcOHdCxY0c0a9bM4L1cuBt85ubmhZ6PTf18TQpXw9YZ/u6m0YBbmqWmpvJPk7m4uOCzzz4rML1IJMKYMWNw69YtAMC+fftMpiFd001wQD1+6+PRo0cAcuP3hx9+qDENF/Nev36db52zszOcnZ0L3Q930zIvridSeHg4v4x7X69ePa3nYC5+q25XEF3id1JSkk55qZaTi9/afv+pxm/V7QDk64XFsbe35+O3vvr16wc7OztER0fj2rVrfPw+deoUH7/zdvrRJX7rWzf64uL37Nmz1RoQVHHxOzY2FjVq1ECbNm34+F2zZk2jx2+in8uXL/PvGzZsaJA8IyIicOnSJQDaG/dUXbhwAV5eXoiOjoaFhQVq166Njz76COPGjdPaWEtK1rt373D2bO5T87Vr1y70WlosFmP06NEYM2YMgNz4bSoN6RS/c1H81oziN8Vvk21I/+KLLwpszC6M6mMWumjVqhXat2+Pq1evYvPmzUXe94A6HXE8+hbiMpKg1NCcLoYI1WycMKCOaVwkCkW1d3G7du00PtqUl2rv2levXhmtbIZma2urcTnXKMkFDl1xw168e/cO7969KzAtNySLKq6HcWG0NbZXrVoVgPqQK9zwKNyPE1230yY5ORmDBg1CSkoKvvzyS4wdOxbe3t5wcHCAWCzG+fPn0bVrV71n6+Z+FMhkMv6xRm247yfw3/EVdAOiatWqRQrktra26NevH3bv3o09e/bwgXzv3r0AgEGDBqk18l69ehUzZsyARCLB4sWL8cknn6B27dqwsbGBSCTCTz/9hIULFxZpJnN9cHV57969QtNy30OxWIxTp05h7ty52LNnD44ePcr3uq9duzYCAwO19lQgpDgsJBaY0tAf5mKT/dlUasTHx/M9Hjt06KD2tJM2qvE7JibGaGUzNIrf2rfThuI3xW9SspKTk/knRP38/PQarqIg27dvB2MMZmZm/NOtBXn16hUkEgkcHBwglUrx6NEjPHr0CBs2bMDKlSv5xlhtsrOz1Yaq5M6Xcrnc6H8TpYlcLgdjDEqlUmuDHcudJw8ikUivOKR6/dy5c2eIxWKN23PLGGNqnbZevXqld9wzNq4uVP8P5MZLTWXlGg21bZf3PYc7b+oav1XrsKDy5FW5cmX+88+7HMiNw9w6LiZXrlxZ63eFi+1SqVRjGtVlqvF76NChGDNmTL743b17d8jlcrXttL3ncMND6Rq/8x6fs7Oz1uPj4ndBfy+aWFtbo2/fvtizZw92797NDynLxe+BAwdCIpHwearG70WLFqFPnz5q8XvWrFlYtGgRZDKZxvrI+5lqW553fd73+sTv9PR0fts///wT8+bNw969e/PF79mzZxs0fiuVSjDGIJfLCx2KWchzu8leEWrr9W1M1atXB/Bf79miMBebIaChPybd3qhxvRIMAY3608V6IVQfO9S1J6i5uTn/g6E8/aDKi7uwHzRoED+GtjFwdzPzevv2LYDcu8AcbkgVbp0mb968ybedNqdOncL79+/Rpk0b7Ny5M9+d0qI2xHDlrF69ul43Y7jtCvrhVNCxF2bo0KHYvXs3Dh06hFWrVkEqleLEiRP8OlVcgJ8yZQp+/PHHfHnpWzeqPyg10dZjw87ODsnJyXj27Bk8PDx03l/FihWxcuVKrFixAiEhIbh69Sr++OMPXLp0CV9//TXs7OzQv39/vY6BkMLs7jgFLs5VhS5GmaA634Aujeh50xU2X0FZRvGb4jdA8ZsYjlKpxNChQxEXFwdLS0t+bp/iUigU/BCcH3/8MVxcXLSmbdasGVq2bInevXujRo0akEgkyMjIwOnTpzF16lS8ePECY8eOReXKlQv8fhQ2R1l5Ycw5ylRvMkokEo1zcanKu3+ZTFboNiWlsDnKtM01pm0ussLmKOOGT/L391cbHkcbLg+uDhljBdYddzwJCQlq83dxuJu9dnZ2/E0mbjjYuLg4rfNLcfHS0tJSYxrVZdycCC1btsSqVav4GMOVh3tCO++8WbrMUQYArq6u/JP1BeHy4H47xsbGas03Pj4egH5zlHE+++wz7NmzB4cOHcKCBQuQnZ3Nx+9+/fqp5cedD7///nv+pqBCoeAb+yMiIgDkfu66zEWm+v3QVG7uN0veura1tUVKSgru3bun9akLVdy2EokEc+fORWBgIB49eoQbN27gzz//xLVr1zBixAhIJBKNwwMVhanMUUattXrgvuC6XAgUpJZdFYggAsvTI10sEqGjSyO0cNb8SA75j7OzM98ofv/+fSiVykKHw7h79y4fZLjeUWVRYY/XcI83hYaGGrUc4eHh6NatW77lT548AaD+6Bn3/smTJ3wPiby4wKntkTVV3DwHbdq00ZiXtrHZCqs7T09PmJubIy4uTq8xwrgyP336VOP6tLS0Yj0l8dFHH6F69eqIjY3FyZMnERcXh6ysLHh5eaFVq1Zqabm60Tb+mr7j1nENO9oaGbTdePTx8cGNGzfw6NEjvS7EOSKRCE2aNEGTJk0wfvx4TJ8+HUuWLMGWLVvoQpwYHN3cNhzVnr1cXC7s3Hvnzh2N25c1FL8pflP8pvhdkiZMmMA3/Kxfv17jHERFcfr0aX6IhcImohs/fny+ZTY2Nvjss8/QsWNHtGjRAi9fvkRAQAA+//xzrX/rBc1R5uTkVIyjMS3GnKNMdeLN+/fva+3UyM1RZmFhoXZeqlq1aol3hNSmsDnKtM01pm0uMq5jn7btGjRoACA3luhTB1xjsEgkKnA77njCw8PRtWvXfO0i3BjcXA9x4L9hnJ49ewZ7e3uN3wWu8dvX11fjnFaqy7ibyu3bt9c4+gM31EreebO4tjVtc5Q1bdoU5ubmiI+P12uOLK7OIyMjNeablpbGD6Oj7xxlQO6cclz8vn79ulr8zjvBM7efTp06adwP9/vK0tJSp7nIuHNacnKyxvzi4uIA5K9rX19f3LhxAy9fvtQ6Vn9h2rVrh3bt2mHKlCmYMWMGli5dir1792ocE74oTGWOMt0H4i3DFApFoY/KXLhwAX///TeA3D+A4lgTehQaQxYDvvMxzJ2css7W1pZ/VCwqKgrnzp0rdJstW7bw7/v06WO0sgmNO+FoeqwbyD35OTs748GDB7h69arRysFNMJTX+vXrAUDtIr19+/awsbFBTEwM/6iQqrt37+LmzZsQiURqj/hrww31w/WCU5WYmKi1bNx22urOxsYG3bt3h1KpxOrVqwstB4c71qtXr2psANm6dWuxeo2IxWJ+Upq9e/cWOMlJQXVz9uxZvS/EubvZ//77b767xkqlUmuvSW5c5NWrVxvkMc/WrVsD0DyuICGk9HB2dkabNm0A5F5QXr9+vdBtVOP3J598YrSyCY3iN8Vvit+kpAQEBGDt2rUAgBUrVmD48OEGy3vr1q0Acp8A6dmzZ5HzcXJywsyZMwHkXu/9888/WtNyDVCqLwD8PDfl6aU6R5mml+q8XNx7XV61atXib7b8888/uH//vta0XN6q5+xPPvlEr/0Z88UpqOz6bKsagzRt0759ezg7O+Phw4e4du2awcqaN822bds0fv4bN+aOhtCtWzd+WYcOHfj4ffz48Xzb3L9/n4/f3bt3V1vHUV3GPfnx9u3bfHm9f/8e27dv17gdd1OXu7mT92VnZ8fH77Vr1xb43VZ9de/eHUBu/H769Gm+9du3b+fjt655qr7MzMy0xu+8abm6effuXb5158+f5+O3ps9O03LuJnZISAjfoVQ1PdcDPu+xcfF77dq1hZ4ndHlxv+fj4uKKnVfec5Su5zuhUEM6ch+FbNq0KTZt2oSIiAi1H4UxMTFYsmQJPv30UzDGUKlSJUyaNKnI+7r7LhyX40I0jo+uBEN0etEfDy1vvv32W/79lClTCrwjdeHCBezbtw9A7vj4AwYMMHr5hOLu7g4gd+wrTY+7WFlZYdasWQByZ/8+evRovguhx48fY+bMmbhx40aRy/HkyRNMmzaNH0YnJycHM2bMwL1792Bvb4/Ro0fzaR0cHPjHnL777ju1H8ovXrzgx1YcMGCATjOG+/n5AQCCg4Nx/vx5fnlcXBw+//xzrY8JVa5cGfb29nj79i1/Zziv+fPnw9LSEgsWLMCSJUvyXbTHxcVh1apV/A8WAPDw8ODPIcOGDVPrvXb58mUEBgYWOxBwF91//vkn/vrrL4hEIo2zu3NjsC5ZskRtTNc7d+5g+PDhhd75zatx48ZwdXVFXFwc5syZw3+XsrKyMHHiRK09J0eNGoU6derg0qVLGDx4MH/nnJOWlobg4GC13kV79+7F/Pnz+V55nMTERL5hpFmzZnqVnxBS8kaOHMm//+GHHwqclOrEiRM4cuQIgNyxOvv27Wvs4gmG4jfFb4rfpCRMnToVv/zyCwDg559/xsSJEw2W99u3b/Hnn38CAL766qtCx7gtDNdQA/z3dDgRhkgkUovf48eP1ziMCOfQoUM4ffo0AKBmzZrFuqlS2lH8pvhN8bucYYRFRkYyAPzLwsKCOTs7M1tbW7Xl7u7u7P79+3rnn5KSwgCwuLfxbOCFBazD8R9Y++OT8r38jk9iAy8sZDKF3AhHWfIyMzNZaGgoy8zM1JpGoVCwtLQ0lp6ezjIzM/V6paamMh8fH/7zqVu3Ltu/fz9LTU3l00RHR7OZM2cyCwsLPt2MGTP03pcxX7Vq1WIA2ObNm9WWz5w5kwFgM2fO1Ljd5s2bGQA2ZMgQteXp6enMw8ODAWBOTk7sgw8+YH5+fmzcuHFq6QICAvg6qVSpEmvevDlr2rQpq1SpEr/86NGjfPqnT58yAKxWrVo6Hc/s2bOZSCRiTk5OrGXLlszZ2ZkBYGKxmO3fvz/fdyEjI4N17tyZ37ePjw9r3Lgxk0gkDABr3LgxS0hIyLcdlz6v/v378+s8PDxYkyZNmJmZGbO3t2crV65kAFjHjh3zbTd8+HAGgFlZWbEWLVqwjh075kt35MgRZmNjw6dr0qQJa9WqFatZsya/z2nTpqltExsby9zc3BgAZm5uzpo2bcq8vLwYAPbxxx+zDh06MADs0qVLWv9eCtO4cWN+/35+fhrTpKSksDp16vDnuoYNGzJvb2++zn/44QcGgM2ZM0dtux07djAAbNiwYfny3L17N7/fypUrsxYtWjAHBwdmZ2fHgoKCtNb1kydPmLu7O/+9qF+/Pvvggw+Yl5cX/7l/8MEHfPoVK1bw+6levTpr2bIla9CgAf/3Xb16dRYVFVXk+stLl3MYJyEhgQFgKSkpBts/ER4XvzWde8oyY8fv5ORkVrduXf7vuV69euzgwYMsLS2NT/Py5Us2bdo0ZmZmxqebP3++4DGb4jfFb4rfFL9Nmer5Y9myZQbPf9myZQwAE4lE7MWLF8XO79GjR3x5Dx48qPN2FL+NE78TEhJYjRo1+M+kUaNG7I8//lDL68WLF2zSpElMLBbz6ZYvXy54zKb4TfGb4jfFb0OhHunIHe8rODgYY8eORfPmzeHs7MzPTFyrVi306dMHW7duxePHj9G0adMi7+d49C28zkjU2BsdyP12vs5IQHCE8R7XLUvMzMxw+PBhfgKbFy9e4IsvvoCnpyc+/fRTdOvWDZ6enli4cCH/2E6fPn0wY8YMIYttdGKxGL///jv69esHiUSCu3fv4tq1a3jw4IFauvnz5+PixYsYOHAgbGxs8PDhQ0RFRaF69eoYNmwYfv/993zje+nj888/x4kTJ+Dr64unT58iKysLH374IS5duoRBgwblS29tbY0zZ85g1apVaNGiBaKiohAeHg4fHx8sWLAAN27c0GuMw71792LWrFlwc3NDVFQU4uPj0b9/f9y5c6fAMSBXrVqFCRMmwMXFBSEhIbhy5QquXLmilqZfv34IDQ3FhAkT4ObmhrCwMISGhsLGxgb9+vXDrl278k0E5urqir///hujR4+Gs7MzQkNDwRjDvHnz8Pvvv+s1RqE2qo+Cc4+a5eXg4IDr16/jyy+/hIODA8LCwiCTyfDDDz/g5s2bRZoDYsiQIQgODkbz5s2RmpqKiIgIfPTRR7h9+zaaN2+udbt69eohJCQES5YsQcuWLREbG4t///0XMpkMHTt2RFBQEA4cOMCn//zzz7F06VJ07doVEokEDx8+RFxcHBo0aIAFCxbg0aNHqFWrlt7lJ4SULEtLSxw5cgTOzs4Acod48ff3h5eXF/r27YsuXbrA09MTS5cu5XswDRgwQK2HTFlE8TsXxW+K38Q4AgICEBQUBABYtmwZpkyZYvB9cEN5dO7cWafJ7Apz69Yt/j3X65cIx9bWFkeOHOHHwH7w4AH69u2LevXqoW/fvvjoo4/g7e2NFStWQKlUAsh9MkG1J3RZRPE7F8Vvit/lhYgxAwxuRwoklUpzhxM5NgevRYXPBlzD1hn7O5t+Y29WVhYiIyPh7u6u9XEVpVLJjyWmOt6WPqKiojBgwIB8gSqvb7/9Fr/88ougYymVB97e3oiOjkZISAh8fX2L/Ugn0Q8347mDg0OR/6ZILl3OYZzExEQ4OzsjJSVF78lqSOnFxe+EhIRyN1lZScTvZ8+eYeDAgVof5QVyHyUfN24clixZQvHEyCh+C4vit+FQ/M4vICCAH84lKCgIkydPNvg+rl+/zg/vsG/fPnzxxRcFpmeFTDadlJSEli1bIiIiAjVq1EBUVJTOfxsUv40bv0NDQzFw4ECtkxIDuY3LP/zwA+bOnUvnNCOj+C0sit+GYyrxmz7lEtTFtanmSUZViAD0rtm6JIpTZtSuXRs3b97E8ePH0bt3b342bSB3kppx48bhwYMHWL16NTWiE0IIIaWEp6cn7ty5gyNHjqB79+5qF36VK1fG+PHj8fjxY/z88890UUgIIUU0bdo0vhF9+fLlejWiBwYG8hMa5h0jNy9uktFKlSrxk9oVZM+ePfjss89w+PBhvH373zxhmZmZ+OOPP9C6dWt+XPSgoCBqoCpFfHx88M8//yA4OBgfffSR2mdTtWpVTJ48GU+ePMH8+fPpcyOElDlmhSchhtKnVmtcSHmEuIwkjcO7iCFCNRsnDKjTQYDSmTaxWIwuXbqgS5cuyMnJQUpKCiwsLGBrawuZTAZLS0uhi0gIIYSQPCQSCXr27ImePXtS/CaEEAOLjo7GsmXLAOReLy1duhRLly7Vmj4gIAABAQF67yc1NRUHDx4EkDvkgC7nboVCgd9//x2///47gNxhQ6ysrJCcnAyFQgEgdyiw5cuXY+DAgXqXiRiXmZkZ+vTpgz59+vDx29LSEjY2NhS/CSFlGjWklyBzsRkCGvpj0u2NGtcrwRDQqD/MxfSxFIeZmRn/CB+NXEQIIYSYBorfhBBiWNw41dz7N2/eFJg+LS2tSPvZv38/MjIyAADffPONTtt07twZCxcuxM2bN/HkyRMkJibyj+h7eHjgww8/xKhRo2hsdBNA8ZsQUp5Qi20Ja1HZC52qNcbVuAdqvdIlIjE6uDREC2cvAUtHCCGEEEIIIaQscHNzK1bDZmBgIAIDAwtNN3LkSIwcOVKvvGvXro0ZM0x/XjBCCCHlCzWkC2BMvY9xOS5EbZlEJMZ3Pp8KVCJCDCcsLAxKpRIymUzoohBCCCFERxS/CSGEENND8ZuQkkUzPwjgUtyDfMtGePdAFesKJV8YQgghhBBCCCGEEEIIIQWihvQS9jYzGdvDT+db3tGlsQClIYQQQgghhBBCCCGEEFIYakgvYWtCj0LBlGrLxBBh09MTApWIEEIIIYQQQgghhBBCSEGoIb0EhSRG4HJcSL6GdCUYLsWF4G5CuEAlI4QQQgghhBBCCCGEEKINNaSXoE1PT0AMkcZ1YogQ9OAQ5MqcEi4VIYQQQgghhBBCCCGEkIJQQ3oJepP5HkowjeuUYHidkYDgiKslXCpCCCGEEEIIIYQQQgghBaGG9BKkuQldff2JmFslURRCCCGEEEIIIYQQQgghOqKG9BKkeVAX9fW9a7YuiaIQQgghhBBCCCFlFg2bSgghxNCoIb0EVbWuWOAY6a42zhhQp0MJl4oQQgghhBBCCClbhl75GfcTngldDEIIIWUINaSXoFH1ehc4RnpAo/4wF5uVcKkIIYQQQgghhJCyRaaQ4eeHB6lnOiGEEIOhhvQS1NipDjpVawyJSL3aJSIxOldrjBbOXgKVjBDTJRKJIBIVNnCS4ezcuRMikQhfffVVie2zrKK6JISQ8ovit+miuiSmJDY9AQcjrwpdDELKDIrfpovq0jCoIb2Efe/zqcaG9O98PhWoRERo3t7esLa2xu7duw2SX0hICBYsWIBjx44ZJL/yJDk5GYGBgVi5cqXQRSmWffv2QSKR8D9yRCIRxGIxKlasiDZt2iAoKAhZWVlCF5MQQkwaxe/Sg+I3IUQbBmBb2Gm8zUwWuiiklKD4XXpQ/CamiMYRKWFVrCtguFcPbHp6gh/kZYR3D1SxriBksQQll8vBGDPaXc3s7GyD5ieRSGBubm7QPA3pwYMHWLhwIYYMGYJPPvlE6OKYlOTkZMydOxe1a9fGxIkTNaZxdHSEt7c3qlWrVrKFKwJLS0u0aNECAKBQKBAdHY1bt27h1q1b2L9/Py5fvgx7e3vBymdKdUkIyY/it2FR/C46it8ly5TqkhAAUDAl1oYexbzmw4QuSqlA8duwKH4XHcXvkmVKdVmaUUO6ADwcXPn3NWyd4e9eficYlcvleP78ObKzs40SyBljyMnJgZmZmcHyt7KygqenZ6kO5sR4+vXrh379+gldDJ24uLjg+vXrasvOnDmDzz//HPfv38eSJUuwcOFCgUpnWnVJii8xMRHHjh3DhQsXcP/+fURFRSEnJweVK1dGixYtMGzYsGJ/Hw4dOoRdu3bh3r17SEhIgLm5OWrUqIEOHTpg3LhxaNKkiWEOhlD8JibHlGIOxW9CDEvBlLgUF4K7CeHlfjhVit/E1JhSzKH4XT5QQ3oJkytzsOLRETAAIgCTGnxWricYVSgUyM7OhpmZmdECo1wuN1jeOTk5yMrKgkKhoEBOTFL37t0xadIkLFiwAEeOHBE0kJPyxcXFBTk5/032ZWVlBXNzc8TGxiI2NhZHjx5Fz549cejQIdjY2OiVd3Z2Nvz9/XH8+HF+mZ2dHWQyGcLDwxEeHo7t27cjKCgIkyZNMtgxlWcUvwkpWRS/CSkeMUQIenAIuztNpetvit+ElBiK32UPjZFewoIjruJ1RgKA3PHanktfC1ugUoIL5KX9ZWZWcj+6FixYAGtrayxYsAApKSkICAiAp6cnHB0d4evri8WLF6s1SgG5472NHDkSALBnzx5YW1vzr27duuXbx7lz59C/f3/Url0bjo6OqFu3LkaOHImIiIh8aaOiomBtbQ1vb28AwPbt29GuXTtUrlwZ1tbWamWwtbVFdHQ0zpw5g06dOsHR0REODg7o2rUrrl27pvWY5XI51qxZg1atWsHBwQG2trZo3LgxFi5ciIyMDL3qLyIiAkuXLkWnTp1Qs2ZNWFpaonLlyujRowf+/PPPfOm/+uoruLu788eqOr6Zam+KwiboePz4MYYOHYoaNWrAwsICVatWxeeff45bt25pTP/VV19BJBJh586deP36NYYPH45q1arBysoKvr6+WLdunV7HrYuWLVsCAF6+fKlxfVJSEmbOnIkGDRrA1tYW9vb2aN26NbZs2QKlUqlxG5lMhkWLFsHb2xtWVlaoXr06Ro8ejXfv3iEwMBAikQiBgYFq25SFuiS6y8nJQatWrbB+/Xq8ePECmZmZSEtLQ2RkJEaMGAEAOHXqFEaNGqV33osWLeIb0ceOHYtXr14hNTUVmZmZuHv3Ltq3bw+lUonJkyfj7t27Bj2u8o7id34Uvyl+U/wu/XVJyh8lGF5nJCA4giYeBSh+a0Lxm+I3xe/SX5elQfm9FSuAhCwptoef5sdGB3InPuni2qxcj5FOCpaSkoJOnTrh+fPn8PX1hUQiQUREBObNm4eYmBisX7+eT9u8eXNYWFjg+fPnqFKlCurWrcuv8/X1Vcs3ICCAP7FVqVIFPj4+iIiIwO7du3H06FH88ccfaNOmjcYyjR8/Hlu2bEGNGjXg5eWlMfAfPnwY8+fPR8WKFeHl5YXIyEicP38eFy9exIEDB+Dv76+WPjMzE71798bFixcBAPXr14e5uTkePXqEBw8e4NChQzh//jycnJx0qrdFixZh27ZtsLOzg6urKxo1aoTY2FicOXMGZ86cwZIlSzBt2jQ+vZeXF1q0aIG7d++qjW2mj2PHjmHAgAHIzs5GhQoV0LhxY0RFReHIkSP4448/sHHjRnz77bcat42KikLz5s2RnJwMHx8fiMVihIaG4rvvvkNycjJmzpypd3m04X4Uaer1+/jxY3Tv3h2xsbGwsLCAh4cHsrOz8ffff+P27ds4e/YsgoOD1X7c5OTk4JNPPsGZM2cA/DeBz7Zt23DmzBn06dNH7zKaSl0S3V28eBGdO3fOt9zNzQ1bt26FmZkZNm3ahD179mDRokWoWbOmznn/+uuvAICOHTuq/WATi8Vo3rw5Tpw4gRo1aiAtLQ2HDx8u0t83Ifqi+E3xm+J36a1LUj4xACdibmGwx4dCF4WUYhS/KX5T/C69dVkaUI/0ErQj/AwUTP1uEjfxCSHabNq0Cc7OzggLC8OtW7fw9OlTHDp0CBKJBDt27EBYWBifdt++fZg6dSoAoFu3brh48SL/WrFiBZ9u69atWLduHdzc3HDmzBlERUXh5s2beP36NQIDAyGVSjF06FCNM0vHxsZi3759OHjwIJ49e4a//vpLYyBftGgRJkyYgPj4eNy5cwfx8fGYOnUqlEolvvnmG8TFxamlnzVrFi5evAhXV1fcu3cPoaGhCAkJQVhYGOrVq4d///0XY8eO1bneuDunUqkUYWFhuHPnDl6/fo2rV6+iWrVqmDlzJl68eMGnnzFjBg4ePAjgv7HNVF+Fef36NYYOHYrs7GxMmDABb9684Y974cKFUCqVGDduHB48eKBx+4ULF6J9+/aIi4vDvXv3EBsby/9IW7BgAZKTk3U+9sKcOnUKAPKNF52eno5PP/0UsbGxGD9+PN69e4fHjx/j+fPnePToEXx9fXHo0CG1H48AsHLlSpw5cwaVKlXCX3/9hadPn+Kff/5BREQEKlasiI0bN+pVPlOqS6I7TY3oqrhe6QD07jXOnU+0/QB3dHSEl1fumKhpaWl65U1IUVH8pvhN8bv01iUpn0QAetdsLXQxSClH8ZviN8Xv0luXpQE1pJegm29DNTakcxOfEKKJmZkZduzYAVfX/yap/fjjj9G7d28A4O9C6komk2HhwoWQSCTYv38/OnT4b7JbiUSCadOmoW/fvoiNjcWRI0fyba9QKDBr1ix+/wDUHi3j1KtXD0FBQfxYdmZmZli6dCmaNWsGqVSqdnKXSqXYsGEDAGDdunVo1qwZv87Dw4PvbXrw4EG14FuQnj174oMPPsg3yY2fnx/mz58PhUKB3377Tae8dLF+/XpIpVI0adIEK1euhIWFBYDcHrEzZsxAr169IJfLERQUpHF7Jycn7Ny5ExUqVOCXjRkzBs2aNUNWVhYuXbpUrPIpFApERkZi5syZ2L17N8RiMf+jj7N9+3a8ePEC/fr1w6pVq+Dg4MCv8/Hxwb59+yASibB8+XJ+uVKpxMqVKwEAq1evRtu2bfl1NWvWRHBwsNbH0bQp7XVJjMPKyop/r1Ao9Nq2Tp06AIB79+5pXJ+SkoLw8Nw4S73RSUmh+E3xm+J36apLUr6JIYKrjTMG1OlQeGJSrlH8pvhN8bt01WVpQw3pJUgMzbNWcxOfyJU5GteT8q1r166oUaNGvuXNmzcHAERGRuqV3+3btxEfH48mTZrkuyPK4YK0tvHUBg8eXOh+VHuXquLuaqv+ALl+/ToyMjJQq1YtfPrpp/m2admyJdq0aQPGGM6dO1fovjnv3r3DqlWr8L///Q9dunRB+/bt0b59ez7whISE6JxXYc6ePQsA+O677zSunzBhglq6vL744gvY2trmW86Np6ap10FhVMeaMzMzQ506dfghM/bv34/u3burped+uH3zzTca82vUqBHc3NwQERGBV69eAQBCQ0MRGxsLW1vbfI8LArk/xPz8/PQqd2msS2J8ly9f5t83bNhQr23HjBnD5zFu3DjExsYCABhjuH//Pnr37o20tDS0bt1ap/MXIYZA8ZviN0DxWxXFbyIkJRgCGvUv1xONEt1Q/Kb4DVD8VkXxWx1FkRKkVBsdXX05N/EJjddG8uJ6WuZVpUoVALmPA+nj0aNHAIDo6Gh8+KHm71tKSgqA3Ed88nJ2doazs3Oh++GGUcirfv36AMD3DlV9X69evXx3sDm+vr64efOm2nYFOXv2LAYMGMAfiyZJSUk65aULrlw+Pj4a13Nj5L158wZSqVTtbjMAtfH0VHGfc1GGo1Aday4zMxPPnj1DamoqnJ2d0bp1/sdaHz58CACYPXs2Fi1apDHPhITcyZJjY2NRo0YNPHv2DEDuZ8fduc6rUaNGao2khSmNdUmMKzk5GYsXLwaQ22uFm1RJV+PGjcOrV68QFBSE9evXY/369bCzs4NMJoNMJoOLiwt+/PFHzJ49u0iTVtGNblIUFL9zUfym+M2h+E2EIhGJ0cGlIVo4az6/EaKK4ncuit8UvzkUv9VRQ3opQROfEG003dkDch+zAXJ7XOpDKpUCyL1b/O7duwLTZmZm5lumaYIMTSpXrqxxedWqVQEAqamp/DLuxMqdaHXdTpvk5GQMGjQIKSkp+PLLLzF27Fh4e3vDwcEBYrEY58+fR9euXSGXy3U6Fl0Udgxc+YHcY8gbfAz9OQP/jTWnWsYffvgBW7ZsQa9evXD37l214TS4Hz3ahsdQxX03uB+S9vb2WtMWtE6T0liXxHiUSiWGDh2KuLg4WFpaYs2aNXrnIRaLsXjxYvj4+OC7775DWlqa2g+2rKwspKSkID09XeOjsJzs7GxkZ2fz/+fOl8Mv/4J5fsPR2Enzj8SyRi6XgzEGpVKp9dFQxphR/5a4vBljWi/wipOnIeWtC+69jY2Nxn1xx6NtO21l5M7RusbvvMerrTx5Va5cmf/88y4Hcs+73DouJleuXFnrd4U7l0ulUo1pVJepxu+hQ4dizJgx+eJ39+7dIZfL1bbT9l7TfvIeG3eucHZ21rit6u+ZlJQU2NnZ8fkAuY/Xa9qO+5wL+jtSpfrZuLi44OrVq2plnDx5MrZu3YpevXrh77//LnL8Tk9Ph1Kp5D87e3t7reVTPVZNdVxa61KpVIIxBrlcDolEUmBaQ/4WJKWXRCTGdz75e90Sogldf2vfThu6/s5F19/qyur1NzWklxI08QkpKdxJbtCgQdixY4fR9sPdOc3r7du3ANRP7tyFFLdOkzdv3uTbTptTp07h/fv3aNOmDXbu3JmvESYmJqbQPPRlZ2eHlJQUvH37VuMdWa78gP6BzVDs7OywYcMG3Lt3D/fv30dQUBB++ukntfXJycl49uwZPDw8dMqT+z4VdJdZlx9fectZ2uuSGM6ECRNw4sQJALnj8zVu3FjvPBISEuDv74/Lly+ja9eumDNnDho0aIDMzEzcvHkT06ZNw4YNG3DmzBlcvXoV1atX15jP4sWLMXfu3HzLh+Y0ReztMMQiTMNWZY+ZmRlcXFyQlpYGmUymMU12djYUCgVycnIM1tCtSU6OYZ4GkMvlyMnJUbtRUlzcRUHefLkx/hUKhcb9cceUd7225Rzuwsvf3x+bN28utHxcHtxnyBgr8Pi540lISNB4TuceZbezs+MbBbgnPOLi4vhleXGPIltaWmpMo7rsyJEjeP/+PVq2bIlVq1bx3y2uPFwvrJycHLXtuPVKpVJrObgJ3ORyuVoaW1tbSKVSREZGamyEUO0dyBjjt+UaA7KysjTuk6vr7OxsrWXSRtNxLFmyBHfu3EFISAgWLVqEgIAAtWNISUnBvXv3tPakVCWVSvmL25SUFK3lS0xM1HgMpb0uZTIZMjMzcfXq1ULPIRkZGYXmR0zfCO8eqGJdQehikHKKrr/p+puuv8sWakgvQWKIoKkPhRgiVLNxoolPiEEU1qDBPdoVGhpq1HKEh4ejW7du+ZY/efIEgPqjZ9z7J0+eaO19+Pjx43zbafPy5UsAQJs2bTTmpW1stuI0Bnl5eeHOnTsIDQ1FmzZt8q3nyl+1atV8d3BLkkQiwaJFi9CjRw8EBQXh+++/h6OjI4DcR7lu3LiBR48e6RzIuc/j6dOnkMvl/OQ2qrhH1nRlKnVJii8gIABr164FAKxYsQLDhw8vUj7Dhg3D5cuX0bFjR5w5c4b/W3Z0dES/fv3Qrl07+Pr6IiIiAj/++CN2796tMZ/p06fjhx9+4P8vlUpRs2ZNbBf9DSY2w3DvHvB3L/uxOisrCzExMbCzs1PrNaMqMzMTEokEZmZmGv/ui4sxhpycHJiZmRmsoT4nJweWlpawtLQ0SH5cuczMzNTy5HrASiQSjfviGp/zrufqUdt2DRo0AJB7vtXnGLjHfkUiUYHbcccTHh6Orl278g2tnOjoaADge5gB/81n8OzZM9jb22v8rLjGb19fX43nbNVl3AV9+/bt+dikinv02MzMTG077qJOLBZrjQvcd9nc3Fwtjbe3N+7cuYOoqCh06dIl33a3bt0CkBtzVMfM5T4vKysrjfvk6trS0lKnWKXaW0vbcSxevBi9evXC2rVrMXnyZL6OfH19cePGDbx8+VLr+Lt5cTctnz17Bmtra41/x1x95z2G0l6XWVlZsLa2RocOHbSewzjczQJSdtWwdS4XsZsIh66/6fq7IHT9XfbQZKMlqKAx0mniE2Io3AWDpsfCAKBdu3ZwdnbGgwcP1B4bNrRt27ZpXL5+/XoAUAvy7du3h42NDWJiYnD06NF829y9exc3b96ESCRC165dC903N3SD6p1TTmJiotaycdtpq7uCcBOHcI2Cea1evVotnZC6d++Opk2bIiUlRa28n332GYDcsur6+FX9+vVRvXp1pKWl4dChQ/nWR0REaJ00p6DyAaZRl6Topk6dil9++QUA8PPPP2PixIlFyufJkyc4efIkAGDy5Mkaf5BXqVIFX375JYDcHq/avt9cI43qCwBkIgWyRQpse34G73PSYW5uXuZfIpEIYrFY64ubSMlYuLwNuQ/VPA3xUs1X0zp9t1WNQZq2ad++PZydnfHw4UNcu3bNYGXNm2bbtm0aP/+NGzcCyI3f3LIOHTrw8fv48eP5trl//z4fv7t37662jqO6jHt8/e3bt/nyev/+PbZv365xO653VmZmZoHfW64OVJdxsWT9+vUat+FiUd7yc3VW0N+Ipv0Vll7T8XGvnj178vFbtbxc/F67dq3O+/P19eXj95EjR/Ktf/nyJR+/NeVZmuuS20bX8x0puywkFpjS0J+us4lR0fU3XX8Xhq6/yxZqSC9Bbar4QCJSr3KJSIzO1RrTxCfEYNzd3QHkjrOl6XFVKysrzJo1C0Du7N9Hjx7Nd9J+/PgxZs6ciRs3bhS5HE+ePMG0adP4x3VzcnIwY8YM3Lt3D/b29hg9ejSf1sHBAWPGjAGQO1P0P//8w6978eIFhg0bBgAYMGCA1oksVHGzVAcHB+P8+fP88ri4OHz++edaH/OtXLky7O3t8fbtW/7Ova7GjBkDBwcH/Pvvv5g0aRL/KL1SqcSyZcvw559/wtzcHJMnT9YrX2OZOnUqAGDlypX892TUqFGoU6cOLl26hMGDByMuLk5tm7S0NAQHB6v12BWLxXwD6Pjx4/neZkDu4/wDBgzQuyHM1OqS6G/KlCn4+eefAQDLli1TG6JAX6q9ewo6P3h6egLIfYy/oMdYC6JgSqwNzX+xQYghUPym+K0Lit+E6G53xylo5uwpdDFIGUfxm+K3Lih+lx3UkF6CvvbqrrEhnSY+IYbUtGlTeHh44OXLl/Dy8kKnTp3QrVs3tYaqkSNHIiAgAAkJCRg0aBBq1KiB9u3bo23btqhevTpatGiB5cuXF2t25RkzZmD58uWoVq0aWrVqhWrVqmHx4sUQi8XYvHkzXF1d1dLPnz8fnTt3RmxsLJo1awZfX180adIE3t7eCA0NRePGjbFu3Tqd9t28eXP0798fcrkcXbt2haenJ5o2bYpatWrh/v37WLJkicbtRCIR/P39AQDNmjVDy5Yt0alTJ3Tq1KnQfbq6umL37t2wsLDAypUr4eLiwh/3tGnT+J5YjRo10ukYjM3f3x/u7u5ISEjgx9q1s7PDn3/+CXd3d+zfvx81atSAj48PWrduDW9vb1SoUAEDBw7M9wNv4sSJ6NatGxISEtCmTRvUr18fzZo1g7u7OxITE/kfbYVN+MUxtbok+gkICEBQUBCA3Eb0KVOmFCs/1Z6tUVFRWtOp9pDhxoXUl4IpcSkuBHcTwou0PSEFofhN8VsXFL8J0R31RCclgeI3xW9dUPwuO6ghvQQ5WzlguFcPqN4boolPcuXk5EAul5f6l6EmPTMmsViM33//Hf369YNEIsHdu3dx7do1PHjwQC3d/PnzcfHiRQwcOBA2NjZ4+PAhoqKiUL16dQwbNgy///47OnfuXORyfP755zhx4gR8fX3x9OlTZGVl4cMPP8SlS5cwaNCgfOmtra1x5swZrFq1Ci1atEBUVBTCw8Ph4+ODBQsW4MaNG3ByctJ5/3v37sWsWbPg5uaGqKgoxMfHo3///rhz506BkxmuWrUKEyZMgIuLC0JCQnDlyhVcuXJFp31+8sknuHfvHgYPHgwrKyv8+++/YIyhX79+uH79OkaOHKlz+Y1NIpHwd5R/+eUX/q5zvXr1EBISgiVLlqBly5aIjY3Fv//+C5lMho4dOyIoKAgHDhxQy8vMzAzHjx/HggUL4OnpiYiICMTHx2PYsGG4ffs2P7apPhOTmFJdEt0FBATww7kEBQUVuxEdyP3RzdmwYYPGNOnp6fj1118BAI0aNdI6s7wuxBAh6MEhyJWlPx6UBIrfhkPxOxfF74JR/CaEGALFb8Oh+J2L4nfBKH6XHSKm60A8pMikUikcHR2RkJAAh4qOGHp5GWIzElDD1hm/dpxaZu+UZ2VlITIyEu7u7lon+snOzsbDhw+RnZ1tlLFWjTFZmZWVFTw9PWlMRS28vb0RHR2NkJAQ+Pr66nwXlBiGUqmEVCqFg4ODWk9dIfXp0wcnTpzA77//jr59+wpdHJ3pcg7jJCYmwtnZGSkpKTQBixbTpk3DsmXLAADLly/HpEmTdN42MDAQc+fOBQBERkbCzc1Nbf0nn3yC48ePAwCGDBmCwMBA1KlTBzk5Obhz5w4mTJiAu3fvAgB27drFj5deGC5+dzjwPZS2/8VqEYBR9XpjsMeHOh+DKaH4XT5R/BYWxW/DofhNVK+/9WkINHUUv8snit/CovhtOKYSv8tmC24pZi42w5RG/vjxzjaa+ASAubk5PDw8tM4UbQjZ2dn8HTlDkEgkFMQJ0dGrV69w7tw5SCQStG7dWujiEIFER0fzjehisRhLly7F0qVLtaYPCAjQa9z07du3o0ePHrh37x727NmDPXv2wMbGBjKZTK0nU0BAgM6N6AVhAE7E3CqzDem6oPhNSNlG8ZuQsoniNyFlG8Vv4yvfrbgCae7siZPdF5T7RnSOubk5RCKRUe7ecQ9cWFpaGu2HAiEEWLBgAQYOHMhP6AgAYWFh+N///ofs7Gx89tlncHFxEbCEREhKpVLtvep45ZroOz6ks7Mzbt26hV27duHgwYP4999/kZSUBDMzM9SqVQtt27bFqFGj0L59+yKVPy8RgN416YcpxW9CTB/Fb0LKH4rfhJg+it/CoZZcAciVOdSITggpU7Zu3YpZs2bB2dkZbm5uSElJwbNnzwAAderUwerVqwUuIRGSm5sbijOSXGBgIAIDAwtMY2ZmhhEjRmDEiBFF3o8uxBChmo0TBtTpYNT9EEJISaD4TQghhJgeit/CKR0D+JQj9xKeodeZn3A/4ZnQRSGEEIOZNWsWunfvDktLSzx69AixsbHw9fXFzJkzcffuXVSvXl3oIhJiEEowBDTqTzfECSFlAsVvQgghxPRQ/BYOXQWWILkyBz8/PIgshQw/PzxYpicaJeVXWFgYlEolPws1KR9KoicwIUKTiMTo4NIQLZy9hC4KIQZH8bt8ovhNCCGmjeJ3+UTxWzjUI70EHY++jdcZCQCA2PQEHIy8KnCJCCGEEKIriUiM73w+FboYhBBCCCGEEEIEQA3pJSg44jK4EWIZgG1hp/E2M1nAEhFCCCFEVyO8e6CKdQWhi0EIIYQQQgghRADUkF6CFEyZ7/9rQ48KVBpCCCGE6KqGrTP83WmCUUIIIYQQQggpr6ghvQRpaki/FBeCuwnhApWIEEIIIYWxkFhgSkN/mteEEEIIIYQQQsoxakgXmBgiBD04BLkyR+iiGA1jrPBEhBBSytC5i3B2d5yCZs6eQhejxNHfACHEFNG5i5R39DdACDFFpnLuooZ0gSnB8DojAcERZW/iUYlEAgCQy+UCl4QQQvTHnbu4cxkpv8pbT3SK34QQU0bxm5RXFL8JIabMVOI3NaSXAgzAiZhbQhfD4MzNzWFpaYmUlBSTubNECCFA7t3wlJQUWFpawtzcXOjiEFKiKH4TQkwVxW9SnlH8JoSYKlOK3+Wri1UpJQLQu2ZroYthFM7OzoiNjcWrV6/g6OgIc3NziEQifr1SqUR2djZEIhHEYsPf12GMQSaTAYDafolxKZVKyOVyZGVllfq7iWWNUqmETCZDVlaWUf6myjrGGORyOVJSUpCWlobq1asLXSRCBEHxu3yi+C0cit/FQ/GbkFwUv8snit/CofhdPKYYv6khXWBiiFDNxgkD6nQQuihG4eDgAABISEhAbGxsvvVcoBWJREYLtHK5vNTf0SprGGPIycmBlZUVBZMSxhhDZmYmrK2t6cdrMVhaWqJ69er8OYyQ8obid/lE8Vs4FL8Ng+I3Ke8ofpdPFL+FQ/HbMEwpflNDusCUYAho1L9Mj7/q4OAABwcHyOVyKBQKtXXZ2dkIDw832uMbCoUC8fHxqF27Nt2ZLUHZ2dmIj49H69atYWtrK3RxyhW5XI6rV6+iQ4cO9AO2iCQSCdUdIaD4XR5R/BYOxe/io/hNSC6K3+UPxW/hUPwuPlOL32W39bYUkojEUOb5fweXhmjh7CVYmUqSubm51j8OYz5alpOTA7FYTHdmS5BIJEJOTg4sLS1hZWUldHHKFYlEwvdGMKVgRAgpvSh+lx8Uv4VD8ZsQYmgUv8sPit/Cofhd/tCZrRBLlizhH3sq7mMaEpE43/+/8/m0WHkSQgghhBBCCCGaJCYmYseOHRgyZAh8fHxga2sLS0tL1KhRA3379sXvv/9e5LwDAwPVrpW1vZ4/f15gPi9evMCoUaPg7u4OKysrVKlSBd27d8fhw4eLXDZCCCHEGKhHegHCwsIwd+5cg+U3oE4n7I67DG7+7BHePVDFuoLB8ieEEEIIIYQQQjguLi7Iycnh/8/1moyNjUVsbCyOHj2Knj174tChQ7CxsSnSPszNzVGpUiWt683MtDc7nDx5Ev7+/sjIyACQOyxJYmIizp49i7Nnz+Lrr7/Gtm3baOxhQgghpQL1SNdCqVRixIgRyMrKQps2bQySZ59aH8DVxhkAUMPWGf7uZXOCUUIIIYQQQgghwsvJyUGrVq2wfv16vHjxApmZmUhLS0NkZCRGjBgBADh16hRGjRpV5H20bdsW8fHxWl9ubm4at4uMjMSAAQOQkZGBdu3aISwsDCkpKUhJScHs2bMBADt27MDPP/9c5LIRQgghhkQN6VqsWbMGf/31FwYPHoxu3boZJE9zsRmmNPKHlcQCUxr6l+kJRgkhhBBCCCGECOvixYu4ffs2xowZgzp16vDL3dzcsHXrVr4Bfc+ePYiJiSnRss2ePRvp6elwcXHBiRMn4OWVO3eYnZ0d5s6di5EjRwIAFi5ciPfv35do2QghhBBNqCFdg8jISMycORNOTk5YsWKFQfNu7uyJk90XoJmzp0HzJYQQQgghhBBCVHXu3LnA9VyvdAC4e/eusYvDS09P58dAHzNmDCpUqJAvzfTp0wEAUqkUf/zxR4mVjRBCCNGGGtI1+Pbbb5Geno7ly5ejcuXKBs+feqITQgghhBBCCBGalZUV/16hUJTYfq9fv47MzEwAQM+ePTWmcXNzQ/369QEAZ8+eLbGyEUIIIdpQQ3oeW7ZswYULF9ClSxd8+eWXQheHEEIIIYQQQggxisuXL/PvGzZsWKQ8Hj9+jAYNGsDa2hp2dnbw9vbGt99+i3/++UfrNo8ePeLf+/r6ak3XoEEDfh+EEEKI0KhrtIrY2FhMmTIF1tbW2LRpU5Hzyc7ORnZ2Nv//lJQUAEBSUlKxy1jWZGdnIzU1FRkZGQXO5l5USqUSGRkZSExMhFhM941KikwmQ0ZGBpKSktT+FojxyeVy/jtvbm4udHHKDe78zhgTuCTEkLjPMzU1lf6e8sjKykJaWhqysrKMGr/fv39P8bsEcfFbKpVCqVQKXZxyhYvfUqmUzjclKDU1FUD5jd/JyclYvHgxAMDPzw/e3t5FyichIQFJSUmoUKECpFIpwsPDER4ejm3btmHGjBlYsGBBvm1ev34NAKhYsSJsbGy05l29enW19JrQ9bfu6Pq7bKLrb+HQ9bcwhLz+poZ0FaNGjUJKSgqWLl2qNhGLvhYvXoy5c+fmW85NnkIIIaRsSkxMhKOjo9DFIAaSmJgIAHB3dxe4JIQQQoypPMZvpVKJoUOHIi4uDpaWllizZo3eeXh6emLZsmX49NNP4e7uDnNzc8hkMly+fBkzZszAvXv3sHDhQlSsWBGTJ09W25a7iVFQI7rqei69JnT9TQgh5ZMQ8dtkG9J37tyJr7/+usjbnzp1Cj169OD/v2fPHvz5559o0qQJfvjhh2KVbfr06Wp5JCcno3bt2oiOji53P9CEJpVKUbNmTcTExMDBwUHo4pQbVO/CoboXRkpKCmrVqoVKlSoJXRRiQNznSfG75NG5TBhU78KhuhdGeY7fEyZMwIkTJwAA69evR+PGjfXOY/DgwfmWWVhYoFu3bujQoQM6dOiAO3fuIDAwEN98843RYildf5cedC4TBtW7cKjuhSFk/DbZhnRDevv2LSZOnAiJRIItW7YU+xEnS0tLWFpa5lvu6OhIf1gCcXBwoLoXANW7cKjuhUGPsJYt3OdJ8Vs4dC4TBtW7cKjuhVHe4ndAQADWrl0LAFixYgWGDx9u8H1YWVlh0aJF6Nq1K9LS0nDhwgV89tln/Hp7e3sAQEZGRoH5cOu59JrQ9XfpQ+cyYVC9C4fqXhhCxG+TbUj/4osv0Lt37yJvr3pnetq0aUhMTMSYMWNQr149pKWlqaWVyWT8e26dhYUFLCwsirx/QgghhBBCCCGkJE2dOhW//PILAODnn3/GxIkTjbavNm3a8O8jIiLU1rm6ugIA3r9/j4yMDK1DvMTGxqqlJ4QQQoRksg3p2u46F0VkZCQAYMOGDdiwYUOBabk74RMmTMDKlSsNsn9CCCGEEEIIIcSYpkyZgqCgIADAsmXLEBAQIFhZGjRowL9//PgxWrZsqTHdo0ePAAC+vr4lUi5CCCGkIOXrGTaBWFpaYs6cOQZr+Ce6o7oXBtW7cKjuhUH1XjbR5yocqnthUL0Lh+peGOWp3gMCAtQa0adMmWL0fd66dYt/n3fi7vbt28Pa2hoAcPr0aY3bR0VF4cmTJwCAbt266bzf8vS5ljZU98KgehcO1b0whKx3EWOMlfheTUxgYCA/CzhVFyGEEEIIIYQQUxEQEMAP5xIUFITJkycXO0/GGEQikdb12dnZ6NixI27fvg1bW1u8evUKFSpUUEszdOhQ7NmzB9WqVcOTJ0/yTQw6duxYbNiwAfb29oiKikLFihWLXW5CCCGkOKhHOiGEEEIIIYQQUgZNmzaNb0Rfvny5Xo3ogYGBEIlEEIlEePnypdq6q1evokuXLtizZw9evXrFL5fL5bhw4QL8/Pxw+/ZtAMDs2bPzNaIDwLx582Bra4u4uDj06dMHz549AwCkp6dj3rx52LhxIwDgp59+okZ0QgghpYLJjpFOCCGEEEIIIYQQzaKjo7Fs2TIAgFgsxtKlS7F06VKt6QMCAnQeN50xhgsXLuDChQsAAGtra9ja2iIlJQVyuZzf548//oipU6dqzMPd3R3BwcHw9/fHtWvX4OXlBUdHR6SlpUGhUAAAvvrqqxIZhoYQQgjRBTWkE0IIIYQQQgghZYxSqVR7/+bNmwLTp6Wl6Zx3w4YNERQUhJs3b+Lhw4dISEhAcnIybGxs4OPjAz8/P4wcORINGzYsMJ9evXrhwYMHWLp0Kc6dO4fXr1+jQoUKaNasGUaNGoXPP/9c5zIRQgghxkZjpBNCCCGEEEIIIYQQQgghBaAx0vWQmpqKwMBANGzYEHZ2dnB0dETLli3xyy+/QCaTFSvvN2/eYPLkyfD29oa1tTUqVaoEPz8/bN26tdxPcGqMek9OTsbRo0cxe/Zs9O7dG9WqVePH/9u5c6dhD8CEGaPuY2NjsX79evj7+8PDwwPW1tawtraGu7s7vvjiC1y8eNHAR2F6jFHvV65cwcyZM9G9e3d4enqiYsWKMDc3R5UqVdC5c2esXr0amZmZBj4S02PM83xeo0eP5s87bm5uBs2bqKP4LQyK38Kh+C0Mit/CofhdNlH8FgbFb+FQ/BYGxW/hmGT8ZkQnL1++ZG5ubgwAA8BsbGyYpaUl//+mTZuypKSkIuV99+5d5uTkxOdlZ2fHzMzM+P9369aNZWVlGfiITIOx6n3Hjh18HnlfO3bsMPyBmCBj1H10dDQTiURq9W1jY8Osra3Vlg0fPpzl5OQY6chKN2N95z/++GO1Ora1tWW2trZqy9zd3VlYWJgRjso0GPM8n9elS5fU/hZq165tkHxJfhS/hUHxWzgUv4VB8Vs4FL/LJorfwqD4LRyK38Kg+C0cU43f1JCug5ycHNawYUMGgFWrVo2dO3eOMcaYQqFgBw4cYPb29gwA69mzp955JycnMxcXFwaA1atXj925c4cxxlh2djZbu3YtMzc3ZwDYmDFjDHpMpsCY9b5jxw7m4uLCevbsyWbOnMkOHz5MgVyFseo+MjKSAWAfffQR27VrF4uNjeXzffz4Mfv000/5z+Gnn34y+HGVdsb8zq9YsYKtXr2a3b9/n0mlUn55QkICW716Nf9jysfHhykUCoMdk6kwZt3nlZ6ezurWrcvMzc1ZixYt6ELciCh+C4Pit3AofguD4rdwKH6XTRS/hUHxWzgUv4VB8Vs4phy/qSFdB1u3buVPLjdu3Mi3ft++ffz68+fP65X3Tz/9xAAwa2trFhERkW/9okWLGAAmkUjK3Z0qY9a7XC7Pt4wC+X+MVffJycns3r17WtcrlUrWo0cPvmdIZmZmkcpvqoz5nS/Mpk2b+LyvX79u0LxNQUnW/cSJExkANnPmTDZs2DC6EDciit/CoPgtHIrfwqD4LRyK32UTxW9hUPwWDsVvYVD8Fo4px29qSNeBn58fA8A6d+6scb1SqWTu7u4MAPvyyy/1yrtWrVoMAPv66681rk9NTWV2dnYMAJs9e7beZTdlxqx3TSiQ/6ek615VcHAw/1ncv3/foHmXdkLWe0hICF/vBw4cMGjepqCk6v7mzZtMLBYzLy8vlpmZSRfiRkbxWxgUv4VD8VsYFL+FQ/G7bKL4LQyK38Kh+C0Mit/CMeX4TZONFiIjIwN//fUXAKBnz54a04hEIvTo0QMAcPbsWZ3zDgsLQ3R0dIF529nZwc/PT++8TZ0x650UTOi6t7Ky4t8rFAqD5l2aCV3v165d49/XrVvXoHmXdiVV99nZ2Rg+fDgYY9i0aZPad50YHsVvYQh9LivPhK57it8Uv0saxe+yieK3MIQ+l5VnQtc9xW+K3yXN1OM3NaQX4smTJ1AqlQCABg0aaE3HrYuPj0dSUpJOeT969Cjf9gXlHRoaqlO+ZYEx650UTOi6v3z5MgDAwsICXl5eBsu3tBOi3jMzM/Hs2TMsWrQIkydPBgB06NABLVq0KFa+pqak6n7evHl48uQJRowYgU6dOhWprER3FL+FIXQMKc+ErnuK3xS/SxrF77KJ4rcwhI4h5ZnQdU/xm+J3STP1+G1msJzKqNevX/Pvq1evrjWd6rrXr1+jUqVKBs9bKpUiLS0NdnZ2heZt6oxZ76RgQtZ9ZGQkNm7cCAAYOHAgHBwcip2nqSipeo+Pj0e1atU0ruvTpw927typV35lQUnU/T///INly5ahatWqWLZsWdEKSvRC8VsYFL+FQ/FbGBS/hUPxu2yi+C0Mit/CofgtDIrfwjH1+E090guRmprKv7exsdGaTnWd6jZC5W3qqG6EI1TdZ2Zmwt/fHxkZGXBycsLixYuLnacpKal6l0gkqFq1KqpWrar2aJO/vz+WLVtWLn8MG7vuc3JyMHz4cOTk5GD16tWoWLFi0QpK9ELxWxhUN8Kh+C0Mit/CofhdNlH8FgbVjXAofguD4rdwTD1+U0M6IURwOTk5+N///od79+7B3Nwc+/btK/DOJCm6ypUrIz4+HvHx8cjIyEBMTAxmzpyJ48ePo1GjRti8ebPQRSxzlixZgn///Re9e/fGgAEDhC4OIYQYDMXvkkPxu+RR/CaElFUUv0sOxe+SZ+z4TQ3phbC3t+ffZ2RkaE2nuk51G6HyNnVUN8Ip6bpXKBQYMmQI/vjjD5iZmWHfvn3o1q1bkfMzVUJ850UiEWrUqIEFCxZg7969kMvlGDNmDEJCQoqVr6kxZt2HhoZi/vz5sLOzw/r164teSKI3it/CoLoRDsVvYVD8Fg7F77KJ4rcwqG6EQ/FbGBS/hWPq8Zsa0gvh6urKv4+NjdWaTnWd6jaGzNvBwaFcjM8GGLfeScFKsu65IP7bb79BIpFgz5496N+/f5HyMnVCf+c/++wz1K5dG0qlEtu2bTNYvqbAmHU/btw4yGQyzJw5ExUrVkRaWpraKycnBwDAGOOXyeXyIh4JUUXxWxhCn8vKM4rfwhD6O0/xOxfF77KD4rcwhD6XlWcUv4Uh9Hee4ncuU4zf1JBeiPr160Mszq0m1Vm+8+LWubi46DzGkerstLrk7ePjo1O+ZYEx650UrKTqXqFQYPDgwThw4AAfxAcOHFi0QpcBpeE7zwWn58+fGzTf0s6YdR8ZGQkAmD59Ouzt7fO99u7dCwCIjo7ml61bt644h0P+H8VvYZSGc1l5RfFbGKXhO0/xm+J3WULxWxil4VxWXlH8FkZp+M5T/DbN+E0N6YWwsbFBu3btAACnT5/WmIYxhjNnzgCAXo/EeHt7o1atWgXmnZ6ejmvXrumdt6kzZr2TgpVE3XNBXPVO+KBBg4pe6DJA6O88Y4wPOuXtMU2h654YB8VvYdDfk3AofgtD6O88xW8635Q1FL+FQX9PwqH4LQyhv/MUv034fMNIobZu3coAMJFIxG7dupVv/W+//cYAMADs/PnzeuX9008/MQDMxsaGRUZG5lu/dOlSBoBJJBIWFhZW1EMwScasd024vHbs2FHsvEydMes+JyeHDRgwgAFgZmZm7MCBA4YqtskzVr3L5fJC02zbto3Pe/369XqVuywo6fMNZ9iwYQwAq127tsHyJP+h+C0Mit/CofgtDIrfwqH4XTZR/BYGxW/hUPwWBsVv4Zhy/KaGdB3I5XLWsGFDBoBVr16d/xAVCgULDg5mDg4ODADr2bNnvm3nzJnDf/iaAnVycjJzcXFhAJiPjw+7e/cuY4yx7Oxstn79emZhYcEAsDFjxhj1GEsjY9Y7Y4y9e/dO7cWlX7Nmjdry9PR0Yx5mqWSsus/JyWFffPEFH8SDg4NL4nBMhrHq/dKlS8zPz4/9+uuvLCYmRm1deHg4mzZtGjMzM2MAWN26dVlGRobRjrG0Mvb5Rhu6EDcuit/CoPgtHIrfwqD4LRyK32UTxW9hUPwWDsVvYVD8Fo4px29qSNdRZGQkc3Nz4z8sGxsbZmVlxf+/adOmLCkpKd92unzAd+/eZU5OTnw6e3t7Zm5uzv+/W7duLCsry8hHWDoZs9659YW95syZY9yDLKWMUfdXrlzh15mbm7OqVasW+CqPd8uNUe+XLl1S+05bWVkxZ2dnZm1trba8cePGegeissSY5xtt6ELc+Ch+C4Pit3AofguD4rdwKH6XTRS/hUHxWzgUv4VB8Vs4phq/aYx0Hbm5ueHBgweYPXs2GjRoAJFIBHNzczRv3hxBQUG4desWKlasWKS8mzdvjsePH2PSpEnw9PSEXC6Hra0t2rdvjy1btuDUqVOwtLQ08BGZBmPWOymYMepeqVTy7+VyOd68eVPgKzMz09CHVeoZo96bN2+OX3/9FcOHD0fjxo3h6OiI5ORkiMVi1K1bF/7+/jhw4ADu3bsHNzc34xyYCaDzTdlE8VsY9PckHIrfwqD4LRw635RNFL+FQX9PwqH4LQyK38Ix1fONiDHGhC4EIYQQQgghhBBCCCGEEFJaUY90QgghhBBCCCGEEEIIIaQA1JBOCCGEEEIIIYQQQgghhBSAGtIJIYQQQgghhBBCCCGEkAJQQzohhBBCCCGEEEIIIYQQUgBqSCeEEEIIIYQQQgghhBBCCkAN6YQQQgghhBBCCCGEEEJIAaghnRBCCCGEEEIIIYT8X3v3H1NV/cdx/AXIr/mDMk1RELMxl0oJmdn8VZSWc1NT8xdeWeRWurbsl9Pm2polmvZHP3FtZinJKE2dlQxzgqYxUaK8pjE2VNSVvxUTkB/n+wfjfkG5h3u5B869+nxsbId7P58Pn/Nx83X3PvdzDgDABIV0AAAAAAAAAABMUEgHAAAAAAAAAMAEhXQAAAAAAAAAAExQSAcAAAAAAAAAwASFdAC2qqmp0cCBAxUUFKTs7Gy7p9MuFi5cqKCgIKWmpto9FQAALEF+AwAQeMhvwDcU0gEf5OXlKSgoyPXTtWtX3bhxo9V+lZWVioqKatY3Ly+v/Sfshz799FOVlJTooYce0gsvvHDb+3fCGi9dulRhYWHauHGjCgsLbZkDAOD/7oRssRv5DQDoaHdCttiN/AZ8QyEdsND169e1bdu2Vttt375d165da/8J+bnr168rPT1dkvTuu+8qOLj1/5ICcY1jY2OVmpoqwzC0bNkyu6cDALhFIGaLnchvAIA/CMRssRP5DfiOQjpgkYiICEnSxo0bW23b2Kaxz90qIyNDFy5cUGxsrGbMmNFq+0Be4zfffFOSlJuby1VxAPAjgZwtdiG/AQB2C+RssQv5DfiOQjpgkUmTJkmSdu3apX/++cdtu3Pnzik3N1eSNHny5A6Zmz+qq6vTZ599JkmaPXu2R1fDA3mNBw4cqKSkJEnSxx9/bPNsAACNAjlb7EB+AwD8QSBnix3Ib8AaFNIBi4wfP169e/dWXV2dsrKy3LbLyspSbW2tevXqpXHjxnXgDP3Lrl27dOrUKUnS3LlzPeoT6GuckpIiSdqyZYuuXr1q82wAAFLgZ0tHI7/JbwDwB4GeLR2N/Ca/YQ0K6YBFQkJCNHv2bEnmW582bNggSZozZ45CQkJMx3Q6nXr//ff17LPPKiYmRuHh4erSpYvi4+OVmpqqgoKCVud19uxZLVmyRElJSYqKilJYWJh69+6thIQEzZ49W19//bXbe5n50rc13333nSQpPj5eCQkJHvVpjzXuSNOmTZMkVVVVafv27TbPBgAgkd/eIr/JbwDwB+S3d8hv8hsWMQC02Z49ewxJhiRj/fr1RlFRket3p9N5W/ujR4+63i8qKjLWr1/v+n3Pnj1uxzb7WbJkidv57d271+jWrVurY+zYscPSvp7o37+/IclwOBym7dpzje0QHR1tSDJefPFFu6cCAHct8pv89hb5DQD2I7/Jb2+R37Aa30gHLJSYmKghQ4ZIavmKbeNrgwcPVmJioulYtbW16ty5s2bMmKG1a9cqLy9PRUVFysnJ0UcffaS4uDhJ0sqVK7V+/frb+ldXV2vWrFm6du2aunbtqsWLF2vnzp06fPiwCgoKlJ2drUWLFik2NtbSvp44ffq0Tpw4IUl67LHHvOpr5RrbofF89+3bZ/NMAACNyG/PkN/kNwD4E/LbM+Q3+Q0L2V3JBwLZrVdrDcMwVq1aZUgyYmJijLq6Olfb+vp6IzY21pBkrFy50jAMw/Rq7fnz543Lly+7/dvV1dXGuHHjDElGXFycUVtb2+z93bt3e3TVuqamxrh69aplfT2RnZ3tGn/fvn2mbdtzje3w3nvvuebz77//2j0dALgrkd/kt7fIbwCwH/lNfnuL/IbV+EY6YLGUlBQFBwfr9OnTys/Pd72el5en8vJyBQcHux56YaZHjx6655573L4fFham1atXS5JOnjyp4uLiZu83far2mDFj3I7TqVMndevWzbK+njh9+rTr+P777/e6v1VrbIem53vmzBkbZwIAaIr8bh353YD8BgD/QX63jvxuQH7DChTSAYv17dtXTz31lKTmW58aj5988knFxMR4PW51dbVOnTqlv/76S06nU06nU4ZhuN7/448/mrWPjo52Hbe09cyML309cf78edfxvffe63X/9lrjjtC9e3fXcdN1AADYi/xuHfndgPwGAP9BfreO/G5AfsMKFNKBdjBv3jxJ0ubNm1VZWanKykpt2bJFkuRwODwe57///lN6eroeeeQRde7cWXFxcRo8eLASEhKUkJDQ7B5kFy5caNZ31KhRGjBggCRp0aJFGj58uNLT03XgwAHdvHnT9O/60tc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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the Phillips to Pisa interpolation\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", + "plt.suptitle('Interpolating from Phillips to Pisa for a 1 Myr Cluster', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].scatter(philpisa_m[phil1], philpisa_l[phil1], label='Phillips', color='mediumseagreen', marker='d', s=65)\n", + "axs[0].scatter(philpisa_m[pisa], philpisa_l[pisa], label='Pisa', color='m', marker='s', s=55)\n", + "axs[0].scatter(philpisa_m[philpisa_interp], philpisa_l[philpisa_interp], facecolors='none', edgecolors='black', lw=2, label='Interpolated Values', s=90)\n", + "axs[0].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[0].set_title('Mass vs. Luminosity', fontsize=24)\n", + "axs[0].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[0].set_ylabel('Luminosity', fontsize=20)\n", + "axs[0].set_xlim(0,0.4)\n", + "axs[0].set_ylim(-4, 0.5)\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "axs[0].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[1].scatter(philpisa_m[phil1], philpisa_t[phil1], label='Phillips', color='mediumseagreen', marker='d', s=65)\n", + "axs[1].scatter(philpisa_m[pisa], philpisa_t[pisa], label='Pisa', color='m', marker='s', s=55)\n", + "axs[1].scatter(philpisa_m[philpisa_interp], philpisa_t[philpisa_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=90)\n", + "axs[1].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[1].set_title('Mass vs. Effective Temperature', fontsize=24)\n", + "axs[1].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[1].set_ylabel('Teff (K)', fontsize=20)\n", + "axs[1].set_xlim(0,0.4)\n", + "axs[1].set_ylim(2.7, 3.7)\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "axs[1].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[2].scatter(philpisa_m[phil1], philpisa_g[phil1], label='Phillips', color='mediumseagreen', marker='d', s=65)\n", + "axs[2].scatter(philpisa_m[pisa], philpisa_g[pisa], label='Pisa', color='m', marker='s', s=55)\n", + "axs[2].scatter(philpisa_m[philpisa_interp], philpisa_g[philpisa_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=90)\n", + "axs[2].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[2].set_title('Mass vs. Surface Gravity', fontsize=24)\n", + "axs[2].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[2].set_ylabel('log(gravity)', fontsize=20)\n", + "axs[2].set_xlim(0,0.4)\n", + "axs[2].set_ylim(2.5, 3.7)\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "axs[2].legend(loc='lower right', fontsize=16)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('philtopisa.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "26eb9f68-be32-4910-b93f-2103f9b39790", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the Phillips to PARSEC interpolation\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", + "plt.suptitle('Interpolating from Phillips to PARSEC for a 1 Gyr Cluster', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].scatter(philpars_m[phil2], philpars_l[phil2], label='Phillips', color='mediumseagreen', marker='d', s=70)\n", + "axs[0].scatter(philpars_m[parsec], philpars_l[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", + "axs[0].scatter(philpars_m[philpars_interp], philpars_l[philpars_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=80)\n", + "axs[0].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[0].set_title('Mass vs. Luminosity', fontsize=24)\n", + "axs[0].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[0].set_ylabel('Luminosity', fontsize=20)\n", + "axs[0].set_xlim(0,0.4)\n", + "axs[0].set_ylim(-7, -1)\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "axs[0].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[1].scatter(philpars_m[phil2], philpars_t[phil2], label='Phillips', color='mediumseagreen', marker='d', s=70)\n", + "axs[1].scatter(philpars_m[parsec], philpars_t[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", + "axs[1].scatter(philpars_m[philpars_interp], philpars_t[philpars_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=80)\n", + "axs[1].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[1].set_title('Mass vs. Effective Temperature', fontsize=24)\n", + "axs[1].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[1].set_ylabel('Teff (K)', fontsize=20)\n", + "axs[1].set_xlim(0,0.4)\n", + "axs[1].set_ylim(2.4, 3.7)\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "axs[1].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[2].scatter(philpars_m[phil2], philpars_g[phil2], label='Phillips', color='mediumseagreen', marker='d', s=70)\n", + "axs[2].scatter(philpars_m[parsec], philpars_g[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", + "axs[2].scatter(philpars_m[philpars_interp], philpars_g[philpars_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=80)\n", + "axs[2].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[2].set_title('Mass vs. Surface Gravity', fontsize=24)\n", + "axs[2].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[2].set_ylabel('log(gravity)', fontsize=20)\n", + "axs[2].set_xlim(0,0.4)\n", + "axs[2].set_ylim(3.5, 5.5)\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "axs[2].legend(loc='lower right', fontsize=16)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('philtoparsec.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 5.ipynb b/docs/paper_examples/Begbie+26/Figure 5.ipynb new file mode 100644 index 00000000..a3ad8da2 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 5.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 5.\n", + "\n", + "Topic: Comparing HR Diagrams with brown dwarfs at different ages to show new `MergedPhillipsBaraffePisaEkstromParsec` evolutionary model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone loaded from existing file: .//iso_6.00_0.00_00010_p0.00.fits\n", + "Isochrone loaded from existing file: .//iso_8.00_0.00_00010_p0.00.fits\n", + "Isochrone loaded from existing file: .//iso_10.00_0.00_00010_p0.00.fits\n" + ] + } + ], + "source": [ + "# Defining three different isochrones (logage = 6, 8, 10)\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "filt_list = ['wfc3,ir,f153m']\n", + "\n", + "young_iso = synthetic.IsochronePhot(6, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), \n", + " filters=filt_list)\n", + "med_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), \n", + " filters=filt_list)\n", + "old_iso = synthetic.IsochronePhot(10, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), \n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Remove low mass stars, keep compact objects\n", + "Remove low mass stars, keep compact objects\n", + "Remove low mass stars, keep compact objects\n" + ] + } + ], + "source": [ + "# Defining our IMF with brown dwarfs\n", + "k_imf = imf.Salpeter_Kirkpatrick_2024()\n", + "\n", + "# Defining clusters for all isochrones\n", + "cluster_mass = 10**6\n", + "young_cluster = synthetic.ResolvedCluster(young_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "med_cluster = synthetic.ResolvedCluster(med_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "old_cluster = synthetic.ResolvedCluster(old_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "young_out = young_cluster.star_systems\n", + "med_out = med_cluster.star_systems\n", + "old_out = old_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1c3f73bf-52ac-446a-897c-cdf5a91dde17", + "metadata": {}, + "outputs": [], + "source": [ + "young_teff = young_out['Teff']\n", + "young_lum = young_out['L']\n", + "med_teff = med_out['Teff']\n", + "med_lum = med_out['L']\n", + "old_teff = old_out['Teff']\n", + "old_lum = old_out['L']\n", + "\n", + "L_sun = 3.828 * 10**(26)\n", + "\n", + "bd_idx_young = young_out['phase'] == 90.0\n", + "bd_idx_med = med_out['phase'] == 90.0\n", + "bd_idx_old = old_out['phase'] == 90.0\n", + "\n", + "log_lum_young = np.log10(young_lum / L_sun)\n", + "log_teff_young = np.log10(young_teff)\n", + "\n", + "log_lum_med = np.log10(med_lum / L_sun)\n", + "log_teff_med = np.log10(med_teff)\n", + "\n", + "log_lum_old = np.log10(old_lum / L_sun)\n", + "log_teff_old = np.log10(old_teff)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cbbf0bea-0d3a-47fa-9bfb-c6ac0167bc0c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting CMDs\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,6))\n", + "\n", + "plt.suptitle('Cluster Evolution over Time (with Brown Dwarfs)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].scatter(log_teff_young[~bd_idx_young], log_lum_young[~bd_idx_young], s=40, color='black', alpha=0.6)\n", + "axs[0].scatter(log_teff_young[bd_idx_young], log_lum_young[bd_idx_young], marker='d', s=40, color='mediumseagreen', label='Brown Dwarfs')\n", + "axs[0].invert_xaxis()\n", + "axs[0].set_title('1 Myr Cluster', fontsize=25)\n", + "axs[0].set_xlabel('log(Teff) [K]', fontsize=20)\n", + "axs[0].set_ylabel('log(L/L$_\\odot$)', fontsize=20)\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].legend(loc='lower left', fontsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].scatter(log_teff_med[~bd_idx_med], log_lum_med[~bd_idx_med], s=40, color='black', alpha=0.6)\n", + "axs[1].scatter(log_teff_med[bd_idx_med], log_lum_med[bd_idx_med], marker='d', s=40, color='mediumseagreen', label='Brown Dwarfs')\n", + "axs[1].invert_xaxis()\n", + "axs[1].set_title('100 Myr Cluster', fontsize=25)\n", + "axs[1].set_xlabel('log(Teff) [K]', fontsize=20)\n", + "axs[1].set_ylabel('log(L/L$_\\odot$)', fontsize=20)\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].legend(loc='lower left', fontsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].scatter(log_teff_old[~bd_idx_old], log_lum_old[~bd_idx_old], s=40, color='black', alpha=0.6)\n", + "axs[2].scatter(log_teff_old[bd_idx_old], log_lum_old[bd_idx_old], marker='d', s=40, color='mediumseagreen', label='Brown Dwarfs')\n", + "axs[2].invert_xaxis()\n", + "axs[2].set_title('10 Gyr Cluster', fontsize=25)\n", + "axs[2].set_xlabel('log(Teff) [K]', fontsize=20)\n", + "axs[2].set_ylabel('log(L/L$_\\odot$)', fontsize=20)\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].legend(loc='lower left', fontsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('cmds.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 6.ipynb b/docs/paper_examples/Begbie+26/Figure 6.ipynb new file mode 100644 index 00000000..86df8fc0 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 6.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 6.\n", + "\n", + "Topic: Showing the simulated Pleiades cluster (via best-fit isochrone) against actual Pleiades data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# simulate Pleiades-like cluster (mass=800 M_sun, distance=440 ly -- http://www.pleiade.org/pleiades_03.html)\n", + " # age= 7.99, z = 0.015, per paper (Alfonso et al., 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [], + "source": [ + "# Define isochrone parameters\n", + "logAge = 8.10 # Age in log(years)\n", + "AKs = 0.0 # extinction in mags (from https://academic.oup.com/mnras/article/343/4/1263/1067893)\n", + "dist = 134.905 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Specify Gaia filters\n", + "gaia_filts = ['gaia,dr2_rev,G', 'gaia,dr2_rev,Gbp', 'gaia,dr2_rev,Grp']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=gaia_filts,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", + "metadata": {}, + "outputs": [], + "source": [ + "m = my_iso.points['mass']\n", + "\n", + "phillips = (m < 0.07) & (m >= 0.01)\n", + "phillips_baraffe = (m >= 0.07) & (m < 0.075)\n", + "\n", + "baraffe = (m >= 0.075) & (m < 0.4)\n", + "baraffe_pisa = (m >= 0.4) & (m < 0.5)\n", + "\n", + "pisa = (m >= 0.5) & (m < 7.0)\n", + "ms = m >= 7.0 # Ekstrom at this age\n", + "\n", + "color = my_iso.points['m_gaiaDR2_Gbp'] - my_iso.points['m_gaiaDR2_Grp']\n", + "mag = my_iso.points['m_gaiaDR2_G']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Remove low mass stars below grid and compact objects\n", + "Found 9 stars out of mass range\n" + ] + } + ], + "source": [ + "# plotting simulated Pleiades cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)\n", + "\n", + "mass = 800.\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "mask = (my_iso.points['mass'] >= 0.01)\n", + "\n", + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: UnitsWarning: 'log(cm.s**-2)' did not parse as fits unit: 'log' is not a recognized function If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n", + "WARNING: UnitsWarning: ''dex'' did not parse as fits unit: At col 0, Unit ''dex'' not supported by the FITS standard. If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n" + ] + }, + { + "data": { + "text/plain": [ + "2000" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pulling in real data\n", + "gaia_file = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits'\n", + "gaia_table = Table.read(gaia_file, format='fits')\n", + "len(gaia_table['designation'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1d18cfcf-df2b-4785-a2f3-9b8b9a74cd45", + "metadata": {}, + "outputs": [], + "source": [ + "# 3 degree viewing\n", + "# output limited to 2000 sources\n", + "with fits.open(gaia_file) as h:\n", + " real_g = h[1].data['phot_g_mean_mag']\n", + " real_bp_rp = h[1].data['bp_rp']\n", + " par = h[1].data['parallax']\n", + " pmra = h[1].data['pmra']\n", + " pmdec = h[1].data['pmdec']\n", + "\n", + "mask = (par > 6.5) & (par < 8.0) & \\\n", + " (pmra > 15) & (pmra < 25) & \\\n", + " (pmdec > -50) & (pmdec < -40)\n", + "\n", + "real_g = real_g[mask]\n", + "real_bp_rp = real_bp_rp[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9dbf8948-07b3-4302-bafa-09e760e45591", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 Msun -> actual mass = 1.0000\n", + "0.5 Msun -> actual mass = 0.5000\n", + "0.08 Msun -> actual mass = 0.0816\n" + ] + } + ], + "source": [ + "marker_masses = [1.0, 0.5, 0.08]\n", + "\n", + "marker_points = {}\n", + "\n", + "for m in marker_masses:\n", + " idx = np.argmin(np.abs(iso['mass'] - m))\n", + "\n", + " marker_points[m] = {\n", + " 'color': iso['m_gaiaDR2_Gbp'][idx] - iso['m_gaiaDR2_Grp'][idx],\n", + " 'mag': iso['m_gaiaDR2_G'][idx]\n", + " }\n", + "\n", + " print(f\"{m} Msun -> actual mass = {iso['mass'][idx]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all together\n", + "fig, axs = plt.subplots(1, 3, figsize=(20,8))\n", + "\n", + "plt.suptitle('Pleiades Simulated Isochrone vs. Real/Simulated Data (Gaia Filters)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", + "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", + "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='red', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[0].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[0].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[0].invert_yaxis()\n", + "axs[0].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", + "axs[0].set_ylabel('G', fontsize=20)\n", + "axs[0].set_title('100 Myr Expected Pleiades Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", + "axs[0].legend(markerscale=4, fontsize=16, loc='upper right')\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].plot(clust['m_gaiaDR2_Gbp'] - clust['m_gaiaDR2_Grp'], clust['m_gaiaDR2_G'],\n", + " 'k.', ms=10, alpha=0.1, label='Simulated Pleiades Data')\n", + "axs[1].plot(iso['m_gaiaDR2_Gbp'] - iso['m_gaiaDR2_Grp'], iso['m_gaiaDR2_G'],\n", + " color='mediumseagreen', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[1].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[1].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[1].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", + "axs[1].set_ylabel('G', fontsize=20)\n", + "axs[1].invert_yaxis()\n", + "axs[1].set_title('Simulated Pleiades Cluster', fontsize=24)\n", + "axs[1].legend(fontsize=18, loc='upper right')\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].plot(real_bp_rp, real_g,\n", + " 'm.', ms=10, alpha=0.3, label='Real Gaia Pleiades Data')\n", + "axs[2].plot(iso['m_gaiaDR2_Gbp'] - iso['m_gaiaDR2_Grp'], iso['m_gaiaDR2_G'],\n", + " color='mediumseagreen', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[2].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[2].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[2].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", + "axs[2].set_ylabel('G', fontsize=20)\n", + "axs[2].invert_yaxis()\n", + "axs[2].set_title('Real Pleiades Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", + "axs[2].legend(fontsize=18, loc='upper right')\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('pleiades_gaia.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 7.ipynb b/docs/paper_examples/Begbie+26/Figure 7.ipynb new file mode 100644 index 00000000..35a2c8f3 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 7.ipynb @@ -0,0 +1,347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 7.\n", + "\n", + "Topic: Showing the simulated Pleiades cluster (via best-fit isochrone) against actual Pleiades data from UKIDSS." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# simulate Pleiades-like cluster (mass=800 M_sun, distance=440 ly -- http://www.pleiade.org/pleiades_03.html)\n", + " # age= 7.99, z = 0.015, per paper (Alfonso et al., 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [], + "source": [ + "# Define isochrone parameters\n", + "logAge = 8.10 # Age in log(years)\n", + "AKs = 0.0 # extinction in mags (from https://academic.oup.com/mnras/article/343/4/1263/1067893)\n", + "dist = 134.905 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Specify Gaia filters\n", + "ukidss_filts = ['ukirt,Z', 'ukirt,Y', 'ukirt,J', 'ukirt,H', 'ukirt,K']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=ukidss_filts,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", + "metadata": {}, + "outputs": [], + "source": [ + "m = my_iso.points['mass']\n", + "\n", + "phillips = (m < 0.07) & (m >= 0.01)\n", + "phillips_baraffe = (m >= 0.07) & (m < 0.075)\n", + "\n", + "baraffe = (m >= 0.075) & (m < 0.4)\n", + "baraffe_pisa = (m >= 0.4) & (m < 0.5)\n", + "\n", + "pisa = (m >= 0.5) & (m < 7.0)\n", + "ms = m >= 7.0 # Ekstrom at this age\n", + "\n", + "color = my_iso.points['m_ukirt_Z'] - my_iso.points['m_ukirt_J']\n", + "mag = my_iso.points['m_ukirt_Z']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Remove low mass stars below grid and compact objects\n", + "Found 16 stars out of mass range\n" + ] + } + ], + "source": [ + "# plotting simulated Pleiades cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)\n", + "\n", + "mass = 800.\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "mask = (my_iso.points['mass'] >= 0.01)\n", + "\n", + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: VerifyWarning: Invalid keyword for column 3: ASCII table null option (TNULLn) is longer than the column's character width and will be truncated (got '-32768'). [astropy.io.fits.column]\n" + ] + } + ], + "source": [ + "# read in ukidss data\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit'\n", + "tab = Table.read(other, format='fits', hdu=2)\n", + "\n", + "focus = np.where((tab['Zmag'] - tab['Jmag'] < 10) & (tab['Zmag'] > 0))\n", + "minus = tab['Zmag'] - tab['Jmag']\n", + "Z_J = minus[focus]\n", + "\n", + "z_err = tab['e_Zmag']\n", + "j_err = tab['e_Jmag']\n", + "zj_err = np.sqrt(z_err**2 + j_err**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "64ea3fad-4e9a-4d57-ae55-dbc33931a09c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 Msun -> actual mass = 1.0000\n", + "0.5 Msun -> actual mass = 0.5000\n", + "0.08 Msun -> actual mass = 0.0816\n" + ] + } + ], + "source": [ + "marker_masses = [1.0, 0.5, 0.08]\n", + "\n", + "marker_points = {}\n", + "\n", + "for m in marker_masses:\n", + " idx = np.argmin(np.abs(iso['mass'] - m))\n", + "\n", + " marker_points[m] = {\n", + " 'color': color[idx],\n", + " 'mag': mag[idx]\n", + " }\n", + "\n", + " print(f\"{m} Msun -> actual mass = {iso['mass'][idx]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all together\n", + "fig, axs = plt.subplots(1, 3, figsize=(20,8))\n", + "\n", + "plt.suptitle('Pleiades Simulated Isochrone vs. Real/Simulated Data (UKIDSS Filters)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", + "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", + "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='red', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[0].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[0].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[0].invert_yaxis()\n", + "axs[0].set_xlabel('Z - J', fontsize=20)\n", + "axs[0].set_ylabel('Z', fontsize=20)\n", + "axs[0].set_title('100 Myr Expected Pleiades Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", + "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].plot(clust['m_ukirt_Z'] - clust['m_ukirt_J'], clust['m_ukirt_Z'],\n", + " 'k.', ms=10, alpha=0.1, label='Simulated Pleiades Data')\n", + "axs[1].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[1].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[1].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[1].set_xlabel('Z - J', fontsize=20)\n", + "axs[1].set_ylabel('Z', fontsize=20)\n", + "axs[1].invert_yaxis()\n", + "axs[1].set_title('Simulated Pleiades Cluster', fontsize=24)\n", + "axs[1].legend(fontsize=18, loc='upper right')\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "\n", + "\n", + "axs[2].errorbar(Z_J, tab['Zmag'][focus], xerr=zj_err[focus], yerr=z_err[focus], fmt='mo', ms=8, alpha=0.3, label='Real UKIDSS Data/Errors')\n", + "axs[2].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=3, zorder=50, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[2].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[2].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[2].set_xlabel('Z - J', fontsize=20)\n", + "axs[2].set_ylabel('Z', fontsize=20)\n", + "axs[2].invert_yaxis()\n", + "axs[2].set_title('Real Pleiades Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", + "axs[2].legend(fontsize=18, loc='upper right')\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('pleiades_ukidss.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 8.ipynb b/docs/paper_examples/Begbie+26/Figure 8.ipynb new file mode 100644 index 00000000..a267e83a --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 8.ipynb @@ -0,0 +1,384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 8.\n", + "\n", + "Topic: Showing the simulated Upper Sco cluster (via best-fit isochrone) against actual data from UKIDSS." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "aa61aecf-9b3a-458d-8188-62668d8dde28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts/spisea/__init__.py\n" + ] + } + ], + "source": [ + "import sys\n", + "\n", + "sys.path.insert(\n", + " 0,\n", + " \"/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts\"\n", + ")\n", + "\n", + "import spisea\n", + "print(spisea.__file__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# simulate Upper Sco-like cluster (mass=1000-2000 M_sun, distance=145 pc, extinction=2)\n", + " # age= 5 myr, z = 0.015, per paper (Alfonso et al., 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [], + "source": [ + "# Define isochrone parameters\n", + "logAge = np.log10(5e6) # 5 Myr\n", + "dist = 145 # pc\n", + "metallicity = 0\n", + "AKs = 0.05\n", + "\n", + "red_law = reddening.RedLawCardelli(3.1)\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "#red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Specify UKIDSS filters\n", + "ukidss_filts = ['ukirt,Z', 'ukirt,Y', 'ukirt,J', 'ukirt,H', 'ukirt,K']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=ukidss_filts,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", + "metadata": {}, + "outputs": [], + "source": [ + "m = my_iso.points['mass']\n", + "\n", + "phillips = (m < 0.07) & (m >= 0.01)\n", + "phillips_baraffe = (m >= 0.07) & (m < 0.075)\n", + "\n", + "baraffe = (m >= 0.075) & (m < 0.4)\n", + "baraffe_pisa = (m >= 0.4) & (m < 0.5)\n", + "\n", + "pisa = (m >= 0.5) & (m < 7.0)\n", + "ms = m >= 7.0 # Ekstrom at this age\n", + "\n", + "color = my_iso.points['m_ukirt_Z'] - my_iso.points['m_ukirt_J']\n", + "mag = my_iso.points['m_ukirt_Z']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Remove low mass stars below grid and compact objects\n" + ] + } + ], + "source": [ + "# plotting simulated Upper Sco cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)\n", + "\n", + "mass = 1500.\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "mask = (my_iso.points['mass'] >= 0.009) & (my_iso.points['mass'] < 7.0)\n", + "\n", + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: AstropyDeprecationWarning: Specified hdu=2 not found, reading in first available table (hdu=1) instead. This will result in an error in future versions! [astropy.io.fits.connect]\n" + ] + } + ], + "source": [ + "# read in ukidss data\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit'\n", + "tab = Table.read(other, format='fits', hdu=2)\n", + "tab\n", + "\n", + "focus = np.where((tab['Zmag'] - tab['Jmag'] < 10) & (tab['Zmag'] > 0))\n", + "minus = tab['Zmag'] - tab['Jmag']\n", + "Z_J = minus[focus]\n", + "\n", + "z_err = tab['e_Zmag']\n", + "j_err = tab['e_Jmag']\n", + "zj_err = np.sqrt(z_err**2 + j_err**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a2582947-4bae-4ee5-bfb6-9b43a49b4ab5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 Msun -> actual mass = 0.9969\n", + "0.5 Msun -> actual mass = 0.5000\n", + "0.08 Msun -> actual mass = 0.0800\n" + ] + } + ], + "source": [ + "marker_masses = [1.0, 0.5, 0.08]\n", + "\n", + "marker_points = {}\n", + "\n", + "for m in marker_masses:\n", + " idx = np.argmin(np.abs(iso['mass'] - m))\n", + "\n", + " marker_points[m] = {\n", + " 'color': color[idx],\n", + " 'mag': mag[idx]\n", + " }\n", + "\n", + " print(f\"{m} Msun -> actual mass = {iso['mass'][idx]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8d82b7de-58fe-43ee-85e9-7fb3f4c8ee09", + "metadata": {}, + "outputs": [], + "source": [ + "clust_mask = (clust['mass'] < 7)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all together\n", + "fig, axs = plt.subplots(1, 3, figsize=(20,8))\n", + "\n", + "plt.suptitle('Upper Sco Simulated Isochrone vs. Real/Simulated Data (UKIDSS Filters)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", + "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", + "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='red', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[0].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[0].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "axs[0].invert_yaxis()\n", + "axs[0].set_xlabel('Z - J', fontsize=20)\n", + "axs[0].set_ylabel('Z', fontsize=20)\n", + "axs[0].set_title('5 Myr Expected Upper Sco Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", + "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].plot(clust['m_ukirt_Z'][clust_mask] - clust['m_ukirt_J'][clust_mask], clust['m_ukirt_Z'][clust_mask],\n", + " 'k.', ms=10, alpha=0.1, label='Simulated Upper Sco Data')\n", + "axs[1].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=4, label='Theoretical Upper Sco Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[1].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[1].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[1].set_xlabel('Z - J', fontsize=20)\n", + "axs[1].set_ylabel('Z', fontsize=20)\n", + "axs[1].invert_yaxis()\n", + "axs[1].set_title('Simulated Upper Sco Cluster', fontsize=24)\n", + "axs[1].legend(fontsize=18, loc='upper right')\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].errorbar(Z_J, tab['Zmag'][focus], xerr=zj_err[focus], yerr=z_err[focus], fmt='mo', ms=8, alpha=0.3, label='Real UKIDSS Data/Errors')\n", + "axs[2].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=4, zorder=50, label='Theoretical Upper Sco Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[2].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[2].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[2].set_xlabel('Z - J', fontsize=20)\n", + "axs[2].set_ylabel('Z', fontsize=20)\n", + "axs[2].invert_yaxis()\n", + "axs[2].set_title('Real Upper Sco Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", + "axs[2].legend(fontsize=18, loc='upper right')\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('uppersco_ukidss.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 9.ipynb b/docs/paper_examples/Begbie+26/Figure 9.ipynb new file mode 100644 index 00000000..bccb1c46 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 9.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 9.\n", + "\n", + "Topic: Showing the simulated M44 (via best-fit isochrone) against actual 2mass data (shows continuity between papers)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 250 for T= 206 logg=3.41\n", + "Changing to logg=5.00 for T= 2017 logg=5.29\n", + "Changing to logg=5.00 for T= 2046 logg=5.29\n", + "Changing to logg=5.00 for T= 2075 logg=5.30\n", + "Changing to logg=5.00 for T= 2104 logg=5.30\n", + "Changing to logg=5.00 for T= 2131 logg=5.30\n", + "Changing to logg=5.00 for T= 2158 logg=5.31\n", + "Changing to logg=5.00 for T= 2185 logg=5.31\n", + "Changing to logg=5.00 for T= 2214 logg=5.31\n", + "Changing to logg=5.00 for T= 2242 logg=5.31\n", + "Changing to logg=5.00 for T= 2271 logg=5.32\n", + "Changing to logg=5.00 for T= 2300 logg=5.32\n", + "Changing to logg=5.00 for T= 2330 logg=5.32\n", + "Changing to logg=5.00 for T= 2330 logg=5.32\n", + "Isochrone generation took 12.527270 s.\n", + "Making photometry for isochrone: log(t) = 8.85 AKs = 0.01 dist = 187\n", + " Starting at: 2026-04-17 10:30:49.238432 Usually takes ~5 minutes\n", + "Starting filter: 2mass,J Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_2mass_J = 36.27\n", + "M = 0.400 Msun T = 3344 K m_2mass_J = 13.76\n", + "M = 2.392 Msun T = 5047 K m_2mass_J = 5.61\n", + "Starting filter: 2mass,Ks Elapsed time: 0.48 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_2mass_Ks = 38.77\n", + "M = 0.400 Msun T = 3344 K m_2mass_Ks = 12.89\n", + "M = 2.392 Msun T = 5047 K m_2mass_Ks = 5.04\n", + "Starting filter: ps1,i Elapsed time: 0.91 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_ps1_i = 36.52\n", + "M = 0.400 Msun T = 3344 K m_ps1_i = 15.52\n", + "M = 2.392 Msun T = 5047 K m_ps1_i = 6.39\n", + "Starting filter: ps1,z Elapsed time: 2.37 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_ps1_z = 35.85\n", + "M = 0.400 Msun T = 3344 K m_ps1_z = 14.78\n", + "M = 2.392 Msun T = 5047 K m_ps1_z = 6.13\n", + "Starting filter: ps1,y Elapsed time: 3.83 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_ps1_y = 39.73\n", + "M = 0.400 Msun T = 3344 K m_ps1_y = 14.48\n", + "M = 2.392 Msun T = 5047 K m_ps1_y = 6.06\n", + " Time taken: 5.32 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = 8.845 # Age in log(years)\n", + "AKs = 0.01 # extinction in mags\n", + "dist = 187 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Also specify filters for synthetic photometry (optional). Here we use \n", + "# the HST WFC3-IR F127M, F139M, and F153M filters\n", + "filt_list = ['2mass,J', '2mass,Ks', 'ps1,i', 'ps1,z', 'ps1,y']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c9645685-9f14-4251-9094-528e0d4d0b71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NumRAdegDEdegpmRAe_pmRApmDEe_pmDEgmage_gmagrmage_rmagimage_imagzmage_zmagymage_ymagJmage_JmagHmage_HmagKmage_KmagFlagName
degdegmas / yrmas / yrmas / yrmas / yrmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmag
int64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64int64str11
1127.2026218.976639-35.65.6-15.05.620.390.0319.040.0117.170.016.40.015.930.0114.730.0214.080.0713.750.010--
2127.2079419.988964-37.54.2-17.14.216.880.015.150.614.420.614.050.013.610.012.340.0211.730.0211.460.020--
3127.2608419.636719-38.24.2-5.84.218.710.0117.390.016.420.099.099.099.099.014.630.0314.050.0413.80.040--
4127.3061419.479976-36.81.3-15.41.311.60.611.20.611.030.610.960.610.930.610.020.029.730.029.650.020--
5127.3285120.717992-41.94.1-17.34.117.650.016.470.015.270.014.720.014.470.013.280.0212.660.0212.40.020--
6127.4277420.952028-33.34.1-15.54.199.099.099.099.099.099.099.099.099.099.013.340.0212.730.0312.50.020--
7127.4348520.673132-38.71.3-12.61.312.440.015.720.012.160.011.430.011.350.010.350.029.910.039.810.021--
8127.4617618.399097-28.24.2-14.94.221.560.0520.140.0318.240.0117.350.016.910.0115.570.0715.120.0914.590.080--
9127.4876818.566668-39.24.1-16.54.116.180.018.880.0314.020.013.570.013.340.012.150.0211.480.0211.30.021--
10127.4944519.483014-38.55.6-12.55.621.560.121.490.618.090.0117.270.016.870.0115.490.0514.990.0714.610.080--
11127.5248818.279527-39.25.6-14.65.620.840.0618.810.617.650.0116.810.016.450.0115.190.0414.520.0614.30.060--
12127.5586618.422182-39.24.1-21.54.118.450.0117.220.015.820.015.190.014.890.013.720.0213.090.0212.810.030--
13127.6341521.170965-41.44.1-12.14.199.099.099.099.099.099.099.099.099.099.014.510.0213.80.0313.660.040--
14127.6484818.222578-27.74.1-14.44.199.099.099.099.099.099.099.099.099.099.014.150.0313.570.0213.310.030--
15127.6526618.377222-39.15.5-20.75.520.030.0218.720.0116.930.016.060.015.640.014.320.0313.720.0213.340.030--
...........................................................................
1025132.4998118.364986-35.71.3-12.21.311.520.012.730.012.320.010.80.010.770.099.099.099.099.099.099.00BD+18 2049
1026132.4999218.365058-35.71.3-12.21.311.40.611.050.610.930.610.90.610.890.610.020.029.730.039.640.020--
1027132.522620.703691-29.44.1-11.74.199.099.099.099.099.099.099.099.099.099.015.290.0514.610.0714.360.071--
1028132.5426720.148646-40.03.6-15.93.615.60.014.370.013.670.013.440.013.230.012.110.0211.480.0311.250.020--
1029132.5451521.101212-34.94.0-9.64.099.099.099.099.099.099.099.099.099.099.013.90.0313.240.0413.050.031--
1030132.5457119.551449-30.13.6-19.83.699.099.099.099.099.099.099.099.099.099.014.580.0313.940.0413.690.040--
1031132.5568919.690012-30.45.0-14.95.021.540.0620.20.0418.30.0117.390.016.970.0115.750.0614.950.0714.510.070--
1032132.5587620.567427-36.36.2-12.46.222.330.1720.860.0519.040.0218.280.0117.940.0216.450.1116.060.1715.80.210--
1033132.5771919.428511-34.63.7-14.33.713.40.612.720.612.40.612.210.612.140.011.090.0210.660.0210.530.020--
1034132.5947220.150953-32.43.7-6.03.799.099.099.099.099.099.099.099.099.099.014.690.0314.040.0413.770.040--
1035132.6469920.710466-32.74.2-5.94.299.099.099.099.099.099.099.099.099.099.014.370.0313.660.0313.40.040--
1036132.6504219.951848-36.85.0-16.15.020.780.0421.330.619.570.616.840.016.470.0115.080.0414.530.0514.330.060--
1037132.7075919.810125-38.43.7-18.33.718.550.0117.230.015.740.015.080.014.780.013.530.0212.910.0212.650.020--
1038132.7368819.616067-38.83.7-14.33.718.20.0116.950.015.680.015.020.014.780.013.570.0212.940.0212.690.030--
1039132.8466719.855061-34.05.0-21.95.020.770.0519.410.0217.610.616.970.016.650.0115.230.0414.710.0514.450.061--
1040132.8575819.315661-37.23.7-20.03.718.090.0116.830.015.430.014.790.014.490.013.30.0212.680.0212.40.020--
" + ], + "text/plain": [ + "\n", + " Num RAdeg DEdeg pmRA e_pmRA pmDE e_pmDE gmag e_gmag rmag e_rmag ... ymag e_ymag Jmag e_Jmag Hmag e_Hmag Kmag e_Kmag Flag Name \n", + " deg deg mas / yr mas / yr mas / yr mas / yr mag mag mag mag ... mag mag mag mag mag mag mag mag \n", + "int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 ... float64 float64 float64 float64 float64 float64 float64 float64 int64 str11 \n", + "----- --------- --------- -------- -------- -------- -------- ------- ------- ------- ------- ... ------- ------- ------- ------- ------- ------- ------- ------- ----- ----------\n", + " 1 127.20262 18.976639 -35.6 5.6 -15.0 5.6 20.39 0.03 19.04 0.01 ... 15.93 0.01 14.73 0.02 14.08 0.07 13.75 0.01 0 --\n", + " 2 127.20794 19.988964 -37.5 4.2 -17.1 4.2 16.88 0.0 15.15 0.6 ... 13.61 0.0 12.34 0.02 11.73 0.02 11.46 0.02 0 --\n", + " 3 127.26084 19.636719 -38.2 4.2 -5.8 4.2 18.71 0.01 17.39 0.0 ... 99.0 99.0 14.63 0.03 14.05 0.04 13.8 0.04 0 --\n", + " 4 127.30614 19.479976 -36.8 1.3 -15.4 1.3 11.6 0.6 11.2 0.6 ... 10.93 0.6 10.02 0.02 9.73 0.02 9.65 0.02 0 --\n", + " 5 127.32851 20.717992 -41.9 4.1 -17.3 4.1 17.65 0.0 16.47 0.0 ... 14.47 0.0 13.28 0.02 12.66 0.02 12.4 0.02 0 --\n", + " 6 127.42774 20.952028 -33.3 4.1 -15.5 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 13.34 0.02 12.73 0.03 12.5 0.02 0 --\n", + " 7 127.43485 20.673132 -38.7 1.3 -12.6 1.3 12.44 0.0 15.72 0.0 ... 11.35 0.0 10.35 0.02 9.91 0.03 9.81 0.02 1 --\n", + " 8 127.46176 18.399097 -28.2 4.2 -14.9 4.2 21.56 0.05 20.14 0.03 ... 16.91 0.01 15.57 0.07 15.12 0.09 14.59 0.08 0 --\n", + " 9 127.48768 18.566668 -39.2 4.1 -16.5 4.1 16.18 0.0 18.88 0.03 ... 13.34 0.0 12.15 0.02 11.48 0.02 11.3 0.02 1 --\n", + " 10 127.49445 19.483014 -38.5 5.6 -12.5 5.6 21.56 0.1 21.49 0.6 ... 16.87 0.01 15.49 0.05 14.99 0.07 14.61 0.08 0 --\n", + " 11 127.52488 18.279527 -39.2 5.6 -14.6 5.6 20.84 0.06 18.81 0.6 ... 16.45 0.01 15.19 0.04 14.52 0.06 14.3 0.06 0 --\n", + " 12 127.55866 18.422182 -39.2 4.1 -21.5 4.1 18.45 0.01 17.22 0.0 ... 14.89 0.0 13.72 0.02 13.09 0.02 12.81 0.03 0 --\n", + " 13 127.63415 21.170965 -41.4 4.1 -12.1 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.51 0.02 13.8 0.03 13.66 0.04 0 --\n", + " 14 127.64848 18.222578 -27.7 4.1 -14.4 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.15 0.03 13.57 0.02 13.31 0.03 0 --\n", + " 15 127.65266 18.377222 -39.1 5.5 -20.7 5.5 20.03 0.02 18.72 0.01 ... 15.64 0.0 14.32 0.03 13.72 0.02 13.34 0.03 0 --\n", + " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " 1025 132.49981 18.364986 -35.7 1.3 -12.2 1.3 11.52 0.0 12.73 0.0 ... 10.77 0.0 99.0 99.0 99.0 99.0 99.0 99.0 0 BD+18 2049\n", + " 1026 132.49992 18.365058 -35.7 1.3 -12.2 1.3 11.4 0.6 11.05 0.6 ... 10.89 0.6 10.02 0.02 9.73 0.03 9.64 0.02 0 --\n", + " 1027 132.5226 20.703691 -29.4 4.1 -11.7 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 15.29 0.05 14.61 0.07 14.36 0.07 1 --\n", + " 1028 132.54267 20.148646 -40.0 3.6 -15.9 3.6 15.6 0.0 14.37 0.0 ... 13.23 0.0 12.11 0.02 11.48 0.03 11.25 0.02 0 --\n", + " 1029 132.54515 21.101212 -34.9 4.0 -9.6 4.0 99.0 99.0 99.0 99.0 ... 99.0 99.0 13.9 0.03 13.24 0.04 13.05 0.03 1 --\n", + " 1030 132.54571 19.551449 -30.1 3.6 -19.8 3.6 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.58 0.03 13.94 0.04 13.69 0.04 0 --\n", + " 1031 132.55689 19.690012 -30.4 5.0 -14.9 5.0 21.54 0.06 20.2 0.04 ... 16.97 0.01 15.75 0.06 14.95 0.07 14.51 0.07 0 --\n", + " 1032 132.55876 20.567427 -36.3 6.2 -12.4 6.2 22.33 0.17 20.86 0.05 ... 17.94 0.02 16.45 0.11 16.06 0.17 15.8 0.21 0 --\n", + " 1033 132.57719 19.428511 -34.6 3.7 -14.3 3.7 13.4 0.6 12.72 0.6 ... 12.14 0.0 11.09 0.02 10.66 0.02 10.53 0.02 0 --\n", + " 1034 132.59472 20.150953 -32.4 3.7 -6.0 3.7 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.69 0.03 14.04 0.04 13.77 0.04 0 --\n", + " 1035 132.64699 20.710466 -32.7 4.2 -5.9 4.2 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.37 0.03 13.66 0.03 13.4 0.04 0 --\n", + " 1036 132.65042 19.951848 -36.8 5.0 -16.1 5.0 20.78 0.04 21.33 0.6 ... 16.47 0.01 15.08 0.04 14.53 0.05 14.33 0.06 0 --\n", + " 1037 132.70759 19.810125 -38.4 3.7 -18.3 3.7 18.55 0.01 17.23 0.0 ... 14.78 0.0 13.53 0.02 12.91 0.02 12.65 0.02 0 --\n", + " 1038 132.73688 19.616067 -38.8 3.7 -14.3 3.7 18.2 0.01 16.95 0.0 ... 14.78 0.0 13.57 0.02 12.94 0.02 12.69 0.03 0 --\n", + " 1039 132.84667 19.855061 -34.0 5.0 -21.9 5.0 20.77 0.05 19.41 0.02 ... 16.65 0.01 15.23 0.04 14.71 0.05 14.45 0.06 1 --\n", + " 1040 132.85758 19.315661 -37.2 3.7 -20.0 3.7 18.09 0.01 16.83 0.0 ... 14.49 0.0 13.3 0.02 12.68 0.02 12.4 0.02 0 --" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt'\n", + "tab = Table.read(other, format='ascii')\n", + "tab" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "399ff5ef-805e-4d97-845f-9269feaf0d03", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a color-magnitude diagram from the isochrone\n", + "m = iso.points['mass']\n", + "J_iso = iso.points['m_2mass_J']\n", + "K_iso = iso.points['m_2mass_Ks']\n", + "\n", + "phillips = (m >= 0.01) & (m < 0.0755)\n", + "phillips_parsec = (m >= 0.075) & (m < 0.2)\n", + "\n", + "parsec = (m >= 0.2)\n", + "\n", + "#focus = np.where((tab['Jmag'] - tab['K2mag'] < 10))\n", + "#minus = tab['Jmag'] - tab['K2mag']\n", + "#J_K = minus[focus]\n", + "\n", + "py.figure(1, figsize=(8,8))\n", + "py.clf()\n", + "color = J_iso - K_iso\n", + "mag = J_iso\n", + "py.plot(tab['Jmag'] - tab['Kmag'], tab['Jmag'],\n", + " 'm.', ms=5, alpha=0.6, label='Observed')\n", + "py.plot(color[phillips], mag[phillips], '-', lw=2, color='purple', label='Phillips')\n", + "py.plot(color[phillips_parsec], mag[phillips_parsec], '-', lw=2, color='orange', label='Phillips/Parsec transition (interpolated)')\n", + "py.plot(color[parsec], mag[parsec], '-', lw=2, color='blue', label='Parsec')\n", + "\n", + "py.title('M44 Observed Cluster vs. Simulated \\n Isochrone (by Evolutionary Model)', fontsize=25)\n", + "py.xlabel('$J - K$', fontsize=20)\n", + "py.ylabel('$J$', fontsize=20)\n", + "py.ylim(0, 22)\n", + "py.gca().invert_yaxis()\n", + "py.grid()\n", + "py.legend(loc='upper right', fontsize=14)\n", + "py.tick_params(axis='both', labelsize=16)\n", + "#py.savefig('M44_comparison.png')\n", + "py.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/README b/docs/paper_examples/Begbie+26/README new file mode 100644 index 00000000..b15fecba --- /dev/null +++ b/docs/paper_examples/Begbie+26/README @@ -0,0 +1 @@ +This directory contains jupyter notebooks to re-create the figures included in Begbie et al. (2026). The notebooks are labeled by figure number. \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits b/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits new file mode 100644 index 00000000..f340b69d --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits @@ -0,0 +1,465 @@ +SIMPLE = T / Standard FITS format BITPIX = 8 / Character data NAXIS = 0 / No image, just extensions EXTEND = T / There are standard extensions COMMENT Dummy header; see following table extension END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 176 / width of table in bytes NAXIS2 = 2000 / number of rows in table PCOUNT = 0 / heap size (no gap) GCOUNT = 1 / one data group TFIELDS = 32 / number of columns TTYPE1 = 'designation' / label for column 1 TFORM1 = '26A ' / format for column 1 TCOMM1 = 'Unique source designation (unique across all Data Releases)' TUCD1 = 'meta.id;meta.main' / VO Unified Content Descriptor for column 1 TTYPE2 = 'source_id' / label for column 2 TFORM2 = 'K ' / format for column 2 TCOMM2 = 'Unique source identifier (unique within a particular Data Release)' TUCD2 = 'meta.id ' / VO Unified Content Descriptor for column 2 TTYPE3 = 'ra ' / label for column 3 TFORM3 = 'D ' / format for column 3 TUNIT3 = 'deg ' / units for column 3 TCOMM3 = 'Right ascension' TUCD3 = 'pos.eq.ra;meta.main' / VO Unified Content Descriptor for column 3 TUTYP3 = 'stc:AstroCoords.Position3D.Value3.C1' / VO Utype for column 3 TTYPE4 = 'dec ' / label for column 4 TFORM4 = 'D ' / format for column 4 TUNIT4 = 'deg ' / units for column 4 TCOMM4 = 'Declination' TUCD4 = 'pos.eq.dec;meta.main' / VO Unified Content Descriptor for column 4 TUTYP4 = 'stc:AstroCoords.Position3D.Value3.C2' / VO Utype for column 4 TTYPE5 = 'parallax' / label for column 5 TFORM5 = 'D ' / format for column 5 TUNIT5 = 'mas ' / units for column 5 TCOMM5 = 'Parallax' TUCD5 = 'pos.parallax.trig' / VO Unified Content Descriptor for column 5 TTYPE6 = 'pmra ' / label for column 6 TFORM6 = 'D ' / format for column 6 TUNIT6 = 'mas.yr**-1' / units for column 6 TCOMM6 = 'Proper motion in right ascension direction' TUCD6 = 'pos.pm;pos.eq.ra' / VO Unified Content Descriptor for column 6 TUTYP6 = 'stc:AstroCoords.Velocity3D.Value3.C1' / VO Utype for column 6 TTYPE7 = 'pmdec ' / label for column 7 TFORM7 = 'D ' / format for column 7 TUNIT7 = 'mas.yr**-1' / units for column 7 TCOMM7 = 'Proper motion in declination direction' TUCD7 = 'pos.pm;pos.eq.dec' / VO Unified Content Descriptor for column 7 TUTYP7 = 'stc:AstroCoords.Velocity3D.Value3.C2' / VO Utype for column 7 TTYPE8 = 'ruwe ' / label for column 8 TFORM8 = 'E ' / format for column 8 TCOMM8 = 'Renormalised unit weight error' TUCD8 = 'stat.error' / VO Unified Content Descriptor for column 8 TTYPE9 = 'phot_g_mean_mag' / label for column 9 TFORM9 = 'E ' / format for column 9 TUNIT9 = 'mag ' / units for column 9 TCOMM9 = 'G-band mean magnitude' TUCD9 = 'phot.mag;em.opt' / VO Unified Content Descriptor for column 9 TTYPE10 = 'phot_bp_mean_mag' / label for column 10 TFORM10 = 'E ' / format for column 10 TUNIT10 = 'mag ' / units for column 10 TCOMM10 = 'Integrated BP mean magnitude' TUCD10 = 'phot.mag;em.opt.B' / VO Unified Content Descriptor for column 10 TTYPE11 = 'phot_rp_mean_mag' / label for column 11 TFORM11 = 'E ' / format for column 11 TUNIT11 = 'mag ' / units for column 11 TCOMM11 = 'Integrated RP mean magnitude' TUCD11 = 'phot.mag;em.opt.R' / VO Unified Content Descriptor for column 11 TTYPE12 = 'bp_rp ' / label for column 12 TFORM12 = 'E ' / format for column 12 TUNIT12 = 'mag ' / units for column 12 TCOMM12 = 'BP - RP colour' TUCD12 = 'phot.color;em.opt.B;em.opt.R' / VO Unified Content Descriptor for colTTYPE13 = 'phot_variable_flag' / label for column 13 TFORM13 = '13A ' / format for column 13 TCOMM13 = 'Photometric variability flag' TUCD13 = 'meta.code;src.var' / VO Unified Content Descriptor for column 13 TTYPE14 = 'non_single_star' / label for column 14 TFORM14 = 'I ' / format for column 14 TCOMM14 = 'Flag indicating the availability of additional information in the va'TUCD14 = 'meta.code.status' / VO Unified Content Descriptor for column 14 TTYPE15 = 'has_xp_continuous' / label for column 15 TFORM15 = 'L ' / format for column 15 TCOMM15 = 'Flag indicating the availability of mean BP/RP spectrum in continuou'TUCD15 = 'meta.code.status' / VO Unified Content Descriptor for column 15 TTYPE16 = 'has_xp_sampled' / label for column 16 TFORM16 = 'L ' / format for column 16 TCOMM16 = 'Flag indicating the availability of mean BP/RP spectrum in sampled f'TUCD16 = 'meta.code.status' / VO Unified Content Descriptor for column 16 TTYPE17 = 'has_rvs ' / label for column 17 TFORM17 = 'L ' / format for column 17 TCOMM17 = 'Flag indicating the availability of mean RVS spectrum for this sourc'TUCD17 = 'meta.code.status' / VO Unified Content Descriptor for column 17 TTYPE18 = 'has_epoch_photometry' / label for column 18 TFORM18 = 'L ' / format for column 18 TCOMM18 = 'Flag indicating the availability of epoch photometry for this source'TUCD18 = 'meta.code.status' / VO Unified Content Descriptor for column 18 TTYPE19 = 'has_epoch_rv' / label for column 19 TFORM19 = 'L ' / format for column 19 TCOMM19 = 'Flag indicating the availability of epoch radial velocity for this s'TUCD19 = 'meta.code.status' / VO Unified Content Descriptor for column 19 TTYPE20 = 'has_mcmc_gspphot' / label for column 20 TFORM20 = 'L ' / format for column 20 TCOMM20 = 'Flag indicating the availability of GSP-Phot MCMC samples for this s'TUCD20 = 'meta.code.status' / VO Unified Content Descriptor for column 20 TTYPE21 = 'has_mcmc_msc' / label for column 21 TFORM21 = 'L ' / format for column 21 TCOMM21 = 'Flag indicating the availability of MSC MCMC samples for this source'TUCD21 = 'meta.code.status' / VO Unified Content Descriptor for column 21 TTYPE22 = 'teff_gspphot' / label for column 22 TFORM22 = 'E ' / format for column 22 TUNIT22 = 'K ' / units for column 22 TCOMM22 = 'Effective temperature from GSP-Phot Aeneas best library using BP/RP 'TUCD22 = 'phys.temperature.effective' / VO Unified Content Descriptor for columTTYPE23 = 'logg_gspphot' / label for column 23 TFORM23 = 'E ' / format for column 23 TUNIT23 = 'log(cm.s**-2)' / units for column 23 TCOMM23 = 'Surface gravity from GSP-Phot Aeneas best library using BP/RP spectr'TUCD23 = 'phys.gravity' / VO Unified Content Descriptor for column 23 TTYPE24 = 'mh_gspphot' / label for column 24 TFORM24 = 'E ' / format for column 24 TUNIT24 = '''dex'' ' / units for column 24 TCOMM24 = 'Iron abundance from GSP-Phot Aeneas best library using BP/RP spectra'TUCD24 = 'phys.abund.Z' / VO Unified Content Descriptor for column 24 TTYPE25 = 'distance_gspphot' / label for column 25 TFORM25 = 'E ' / format for column 25 TUNIT25 = 'pc ' / units for column 25 TCOMM25 = 'Distance from GSP-Phot Aeneas best library using BP/RP spectra' TUCD25 = 'pos.distance;pos.eq' / VO Unified Content Descriptor for column 25 TTYPE26 = 'azero_gspphot' / label for column 26 TFORM26 = 'E ' / format for column 26 TUNIT26 = 'mag ' / units for column 26 TCOMM26 = 'Monochromatic extinction $A_0$ at 547.7nm from GSP-Phot Aeneas best 'TUCD26 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 26TTYPE27 = 'ag_gspphot' / label for column 27 TFORM27 = 'E ' / format for column 27 TUNIT27 = 'mag ' / units for column 27 TCOMM27 = 'Extinction in G band from GSP-Phot Aeneas best library using BP/RP s'TUCD27 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 27TTYPE28 = 'ebpminrp_gspphot' / label for column 28 TFORM28 = 'E ' / format for column 28 TUNIT28 = 'mag ' / units for column 28 TCOMM28 = 'Reddening $E(G_{\rm BP} - G_{\rm RP})$ from GSP-Phot Aeneas best lib'TUCD28 = 'phot.color.excess' / VO Unified Content Descriptor for 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b/docs/paper_examples/Begbie+26/cluster_data/9b2caaac-f8ae-11f0-9655-bc97e148b76b-O-result.fits @@ -0,0 +1,503 @@ +SIMPLE = T / Standard FITS format BITPIX = 8 / Character data NAXIS = 0 / No image, just extensions EXTEND = T / There are standard extensions COMMENT Dummy header; see following table extension END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 172 / width of table in bytes NAXIS2 = 2000 / number of rows in table PCOUNT = 0 / heap size (no gap) GCOUNT = 1 / one data group TFIELDS = 32 / number of columns TTYPE1 = 'designation' / label for column 1 TFORM1 = '27A ' / format for column 1 TCOMM1 = 'Unique source designation (unique across all Data Releases)' TUCD1 = 'meta.id;meta.main' / VO Unified Content Descriptor for column 1 TTYPE2 = 'source_id' / label for column 2 TFORM2 = 'K ' / format for column 2 TCOMM2 = 'Unique source identifier (unique within a particular Data Release)' TUCD2 = 'meta.id ' / VO Unified Content Descriptor for column 2 TTYPE3 = 'ra ' / label for column 3 TFORM3 = 'D ' / format for column 3 TUNIT3 = 'deg ' / units for column 3 TCOMM3 = 'Right ascension' TUCD3 = 'pos.eq.ra;meta.main' / VO Unified Content Descriptor for column 3 TUTYP3 = 'stc:AstroCoords.Position3D.Value3.C1' / VO Utype for column 3 TTYPE4 = 'dec ' / label for column 4 TFORM4 = 'D ' / format for column 4 TUNIT4 = 'deg ' / units for column 4 TCOMM4 = 'Declination' TUCD4 = 'pos.eq.dec;meta.main' / VO Unified Content Descriptor for column 4 TUTYP4 = 'stc:AstroCoords.Position3D.Value3.C2' / VO Utype for column 4 TTYPE5 = 'parallax' / label for column 5 TFORM5 = 'D ' / format for column 5 TUNIT5 = 'mas ' / units for column 5 TCOMM5 = 'Parallax' TUCD5 = 'pos.parallax.trig' / VO Unified Content Descriptor for column 5 TTYPE6 = 'parallax_over_error' / label for column 6 TFORM6 = 'E ' / format for column 6 TCOMM6 = 'Parallax divided by its standard error' TUCD6 = 'stat.snr;pos.parallax.trig' / VO Unified Content Descriptor for columTTYPE7 = 'pmra ' / label for column 7 TFORM7 = 'D ' / format for column 7 TUNIT7 = 'mas.yr**-1' / units for column 7 TCOMM7 = 'Proper motion in right ascension direction' TUCD7 = 'pos.pm;pos.eq.ra' / VO Unified Content Descriptor for column 7 TUTYP7 = 'stc:AstroCoords.Velocity3D.Value3.C1' / VO Utype for column 7 TTYPE8 = 'pmdec ' / label for column 8 TFORM8 = 'D ' / format for column 8 TUNIT8 = 'mas.yr**-1' / units for column 8 TCOMM8 = 'Proper motion in declination direction' TUCD8 = 'pos.pm;pos.eq.dec' / VO Unified Content Descriptor for column 8 TUTYP8 = 'stc:AstroCoords.Velocity3D.Value3.C2' / VO Utype for column 8 TTYPE9 = 'ruwe ' / label for column 9 TFORM9 = 'E ' / format for column 9 TCOMM9 = 'Renormalised unit weight error' TUCD9 = 'stat.error' / VO Unified Content Descriptor for column 9 TTYPE10 = 'phot_g_mean_mag' / label for column 10 TFORM10 = 'E ' / format for column 10 TUNIT10 = 'mag ' / units for column 10 TCOMM10 = 'G-band mean magnitude' TUCD10 = 'phot.mag;em.opt' / VO Unified Content Descriptor for column 10 TTYPE11 = 'bp_rp ' / label for column 11 TFORM11 = 'E ' / format for column 11 TUNIT11 = 'mag ' / units for column 11 TCOMM11 = 'BP - RP colour' TUCD11 = 'phot.color;em.opt.B;em.opt.R' / VO Unified Content Descriptor for colTTYPE12 = 'radial_velocity' / label for column 12 TFORM12 = 'E ' / format for column 12 TUNIT12 = 'km.s**-1' / units for column 12 TCOMM12 = 'Radial velocity ' TUCD12 = 'spect.dopplerVeloc.opt;em.opt.I' / VO Unified Content Descriptor for TUTYP12 = 'stc:AstroCoords.Velocity3D.Value3.C3' / VO Utype for column 12 TTYPE13 = 'phot_variable_flag' / label for column 13 TFORM13 = '13A ' / format for column 13 TCOMM13 = 'Photometric variability flag' TUCD13 = 'meta.code;src.var' / VO Unified Content Descriptor for column 13 TTYPE14 = 'non_single_star' / label for column 14 TFORM14 = 'I ' / format for column 14 TCOMM14 = 'Flag indicating the availability of additional information in the va'TUCD14 = 'meta.code.status' / VO Unified Content Descriptor for column 14 TTYPE15 = 'has_xp_continuous' / label for column 15 TFORM15 = 'L ' / format for column 15 TCOMM15 = 'Flag indicating the availability of mean BP/RP spectrum in continuou'TUCD15 = 'meta.code.status' / VO Unified Content Descriptor for column 15 TTYPE16 = 'has_xp_sampled' / label for column 16 TFORM16 = 'L ' / format for column 16 TCOMM16 = 'Flag indicating the availability of mean BP/RP spectrum in sampled f'TUCD16 = 'meta.code.status' / VO Unified Content Descriptor for column 16 TTYPE17 = 'has_rvs ' / label for column 17 TFORM17 = 'L ' / format for column 17 TCOMM17 = 'Flag indicating the availability of mean RVS spectrum for this sourc'TUCD17 = 'meta.code.status' / VO Unified Content Descriptor for column 17 TTYPE18 = 'has_epoch_photometry' / label for column 18 TFORM18 = 'L ' / format for column 18 TCOMM18 = 'Flag indicating the availability of epoch photometry for this source'TUCD18 = 'meta.code.status' / VO Unified Content Descriptor for column 18 TTYPE19 = 'has_epoch_rv' / label for column 19 TFORM19 = 'L ' / format for column 19 TCOMM19 = 'Flag indicating the availability of epoch radial velocity for this s'TUCD19 = 'meta.code.status' / VO Unified Content Descriptor for column 19 TTYPE20 = 'has_mcmc_gspphot' / label for column 20 TFORM20 = 'L ' / format for column 20 TCOMM20 = 'Flag indicating the availability of GSP-Phot MCMC samples for this s'TUCD20 = 'meta.code.status' / VO Unified Content Descriptor for column 20 TTYPE21 = 'has_mcmc_msc' / label for column 21 TFORM21 = 'L ' / format for column 21 TCOMM21 = 'Flag indicating the availability of MSC MCMC samples for this source'TUCD21 = 'meta.code.status' / VO Unified Content Descriptor for column 21 TTYPE22 = 'teff_gspphot' / label for column 22 TFORM22 = 'E ' / format for column 22 TUNIT22 = 'K ' / units for column 22 TCOMM22 = 'Effective temperature from GSP-Phot Aeneas best library using BP/RP 'TUCD22 = 'phys.temperature.effective' / VO Unified Content Descriptor for columTTYPE23 = 'logg_gspphot' / label for column 23 TFORM23 = 'E ' / format for column 23 TUNIT23 = 'log(cm.s**-2)' / units for column 23 TCOMM23 = 'Surface gravity from GSP-Phot Aeneas best library using BP/RP spectr'TUCD23 = 'phys.gravity' / VO Unified Content Descriptor for column 23 TTYPE24 = 'mh_gspphot' / label for column 24 TFORM24 = 'E ' / format for column 24 TUNIT24 = '''dex'' ' / units for column 24 TCOMM24 = 'Iron abundance from GSP-Phot Aeneas best library using BP/RP spectra'TUCD24 = 'phys.abund.Z' / VO Unified Content Descriptor for column 24 TTYPE25 = 'distance_gspphot' / label for column 25 TFORM25 = 'E ' / format for column 25 TUNIT25 = 'pc ' / units for column 25 TCOMM25 = 'Distance from GSP-Phot Aeneas best library using BP/RP spectra' TUCD25 = 'pos.distance;pos.eq' / VO Unified Content Descriptor for column 25 TTYPE26 = 'azero_gspphot' / label for column 26 TFORM26 = 'E ' / format for column 26 TUNIT26 = 'mag ' / units for column 26 TCOMM26 = 'Monochromatic extinction $A_0$ at 547.7nm from GSP-Phot Aeneas best 'TUCD26 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 26TTYPE27 = 'ag_gspphot' / label for column 27 TFORM27 = 'E ' / format for column 27 TUNIT27 = 'mag ' / units for column 27 TCOMM27 = 'Extinction in G band from GSP-Phot Aeneas best library using BP/RP s'TUCD27 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 27TTYPE28 = 'ebpminrp_gspphot' / label for column 28 TFORM28 = 'E ' / format for column 28 TUNIT28 = 'mag ' / units for column 28 TCOMM28 = 'Reddening $E(G_{\rm BP} - G_{\rm RP})$ from GSP-Phot Aeneas best lib'TUCD28 = 'phot.color.excess' / VO Unified Content Descriptor for column 28 TTYPE29 = 'target_id' / label for column 29 TFORM29 = '3A ' / format for column 29 TTYPE30 = 'target_ra' / label for column 30 TFORM30 = 'D ' / format for column 30 TTYPE31 = 'target_dec' / label for column 31 TFORM31 = 'D ' / format for column 31 TTYPE32 = 'target_separation 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DR3 661302681574958208 -kIIä€@`I'áJ&ô@3Ë9d\[¥?ö©Já`f@6 $À “»j”´í?öª±%Ò¹Ž?~:‚AŸê®?ý«àÀNOT_AVAILABLEFFFFFFFÀÀÀÀÀÀÀm44@`?ÉbúÌ@3›òXÍv–?Õ<[FšGaia DR3 661318830650337152 -yù0§€@`BWºï™@3î­ìA”?ÿmI]»-B«±ƒ¿Èçþbš À##ì•3ÿ?†€Abî´?|BpÁ'ãÜVARIABLETTFTFTTE¤ðÈ@’r°½ÙcCô9‰=É…ð=¤?æ=.}Vm44@`?ÉbúÌ@3›òXÍv–?Õ<:ð¤É \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt b/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt new file mode 100644 index 00000000..e0919030 --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt @@ -0,0 +1,1086 @@ +Title: Characterization of the Praesepe Star Cluster by Photometry and Proper + Motions with 2MASS, PPPMXL and Pan-STARRS +Authors: Wang P.F., Chen W.P., Lin C.C., Pandey A.K., Huang C.K., Panwar N., + Lee C.H., Tsai M.F., Tang C.-H.., Goldman B., Burgett W.S., + Chambers K.C., Draper P.W., Flewelling H., Grav T., Heasley J.N., + Hodapp K.W., Huber M.E., Jedicke R., Kaiser N., Kudritzki R., + Luppino G.A., Lupton R.H., Magnier E.A., Metcalfe N., Monet D.G., + Morgan J.S., Onaka P.M., Price P.A., Stubbs C.W., Sweeney W., + Tonry J.L., Wainscoat R.J., Waters C. +Table: Member Candidates of Praesepe +================================================================================ +Byte-by-byte Description of file: apj491325t1_mrt.txt +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 1- 4 I4 --- Num Identification number + 6- 14 F9.5 deg RAdeg Right Ascension in decimal degrees (J2000) + 16- 24 F9.6 deg DEdeg Declination in decimal degrees (J2000) + 26- 30 F5.1 mas/yr pmRA Proper motion along RA times cos(DE) + 32- 35 F4.1 mas/yr e_pmRA Uncertainty in pmRA + 37- 41 F5.1 mas/yr pmDE Proper motion along DE + 43- 46 F4.1 mas/yr e_pmDE Uncertainty in pmDE + 48- 52 F5.2 mag gmag Pan-STARRS g band magnitude (1) + 54- 58 F5.2 mag e_gmag Uncertainty in gmag (1) + 60- 64 F5.2 mag rmag Pan-STARRS r band magnitude (1) + 66- 70 F5.2 mag e_rmag Uncertainty in rmag (1) + 72- 76 F5.2 mag imag Pan-STARRS i band magnitude (1) + 78- 82 F5.2 mag e_imag Uncertainty in imag (1) + 84- 88 F5.2 mag zmag Pan-STARRS z band magnitude (1) + 90- 94 F5.2 mag e_zmag Uncertainty in zmag (1) + 96-100 F5.2 mag ymag Pan-STARRS y band magnitude (1) + 102-106 F5.2 mag e_ymag Uncertainty in ymag (1) + 108-112 F5.2 mag Jmag 2MASS J band magnitude (1) + 114-118 F5.2 mag e_Jmag Uncertainty in Jmag (1) + 120-124 F5.2 mag Hmag 2MASS H band magnitude (1) + 126-130 F5.2 mag e_Hmag Uncertainty in Hmag (1) + 132-136 F5.2 mag Kmag 2MASS K_S_ band magnitude (1) + 138-142 F5.2 mag e_Kmag Uncertainty in Kmag (1) + 144 I1 --- Flag Binary flag (2) + 146-156 A11 --- Name Common source identifier +-------------------------------------------------------------------------------- +Note (1): A 99.00 indicates a null value. +Note (2): + 1 = possible binary system. + 0 = Not a binary system. +-------------------------------------------------------------------------------- + 1 127.20262 18.976639 -35.6 5.6 -15.0 5.6 20.39 0.03 19.04 0.01 17.17 0.00 16.40 0.00 15.93 0.01 14.73 0.02 14.08 0.07 13.75 0.01 0 + 2 127.20794 19.988964 -37.5 4.2 -17.1 4.2 16.88 0.00 15.15 0.60 14.42 0.60 14.05 0.00 13.61 0.00 12.34 0.02 11.73 0.02 11.46 0.02 0 + 3 127.26084 19.636719 -38.2 4.2 -5.8 4.2 18.71 0.01 17.39 0.00 16.42 0.00 99.00 99.00 99.00 99.00 14.63 0.03 14.05 0.04 13.80 0.04 0 + 4 127.30614 19.479976 -36.8 1.3 -15.4 1.3 11.60 0.60 11.20 0.60 11.03 0.60 10.96 0.60 10.93 0.60 10.02 0.02 9.73 0.02 9.65 0.02 0 + 5 127.32851 20.717992 -41.9 4.1 -17.3 4.1 17.65 0.00 16.47 0.00 15.27 0.00 14.72 0.00 14.47 0.00 13.28 0.02 12.66 0.02 12.40 0.02 0 + 6 127.42774 20.952028 -33.3 4.1 -15.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.34 0.02 12.73 0.03 12.50 0.02 0 + 7 127.43485 20.673132 -38.7 1.3 -12.6 1.3 12.44 0.00 15.72 0.00 12.16 0.00 11.43 0.00 11.35 0.00 10.35 0.02 9.91 0.03 9.81 0.02 1 + 8 127.46176 18.399097 -28.2 4.2 -14.9 4.2 21.56 0.05 20.14 0.03 18.24 0.01 17.35 0.00 16.91 0.01 15.57 0.07 15.12 0.09 14.59 0.08 0 + 9 127.48768 18.566668 -39.2 4.1 -16.5 4.1 16.18 0.00 18.88 0.03 14.02 0.00 13.57 0.00 13.34 0.00 12.15 0.02 11.48 0.02 11.30 0.02 1 + 10 127.49445 19.483014 -38.5 5.6 -12.5 5.6 21.56 0.10 21.49 0.60 18.09 0.01 17.27 0.00 16.87 0.01 15.49 0.05 14.99 0.07 14.61 0.08 0 + 11 127.52488 18.279527 -39.2 5.6 -14.6 5.6 20.84 0.06 18.81 0.60 17.65 0.01 16.81 0.00 16.45 0.01 15.19 0.04 14.52 0.06 14.30 0.06 0 + 12 127.55866 18.422182 -39.2 4.1 -21.5 4.1 18.45 0.01 17.22 0.00 15.82 0.00 15.19 0.00 14.89 0.00 13.72 0.02 13.09 0.02 12.81 0.03 0 + 13 127.63415 21.170965 -41.4 4.1 -12.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.51 0.02 13.80 0.03 13.66 0.04 0 + 14 127.64848 18.222578 -27.7 4.1 -14.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.15 0.03 13.57 0.02 13.31 0.03 0 + 15 127.65266 18.377222 -39.1 5.5 -20.7 5.5 20.03 0.02 18.72 0.01 16.93 0.00 16.06 0.00 15.64 0.00 14.32 0.03 13.72 0.02 13.34 0.03 0 + 16 127.67308 20.407421 -39.4 4.1 -13.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.72 0.03 14.08 0.04 13.88 0.05 0 + 17 127.71251 19.352421 -38.9 4.1 -13.9 4.1 15.38 0.00 14.17 0.00 13.69 0.60 13.28 0.00 13.12 0.00 12.02 0.02 11.36 0.02 11.19 0.02 0 + 18 127.71419 18.897652 -40.3 4.1 -17.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.26 0.03 13.62 0.03 13.39 0.03 0 + 19 127.73101 19.555473 -32.2 1.6 -17.6 1.6 11.19 0.60 10.83 0.60 10.69 0.60 10.63 0.60 10.61 0.60 9.73 0.02 9.46 0.02 9.37 0.02 0 + 20 127.73994 20.662451 -34.4 4.1 -11.7 4.1 16.03 0.00 14.82 0.00 14.15 0.00 13.83 0.00 13.67 0.00 12.60 0.02 11.90 0.02 11.72 0.02 0 + 21 127.74543 18.694725 -36.0 4.1 -17.4 4.1 16.06 0.00 14.83 0.00 14.04 0.00 13.71 0.00 13.53 0.00 12.46 0.02 11.76 0.02 11.59 0.02 0 + 22 127.75076 19.404428 -43.1 5.5 -18.9 5.5 20.50 0.02 19.08 0.01 17.35 0.00 16.60 0.00 16.22 0.00 14.94 0.05 14.26 0.05 13.99 0.05 0 + 23 127.77505 18.129977 -42.7 4.1 -12.0 4.1 20.27 0.04 18.31 0.60 17.29 0.00 16.47 0.00 16.10 0.01 14.84 0.04 14.17 0.04 13.90 0.05 0 + 24 127.77755 21.229530 -31.6 5.6 -10.6 5.6 18.97 0.01 99.00 99.00 99.00 99.00 15.54 0.00 15.15 0.01 13.94 0.03 13.33 0.02 13.02 0.03 1 + 25 127.80400 18.153683 -35.4 1.1 -11.9 0.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.02 0.03 8.77 0.01 8.74 0.02 1 BD+18 1959 + 26 127.80453 20.900096 -38.0 4.1 -11.7 4.1 18.52 0.01 17.29 0.00 15.87 0.00 15.20 0.00 14.90 0.00 13.69 0.02 13.05 0.03 12.80 0.03 0 + 27 127.80822 21.137447 -32.3 4.1 -19.6 4.1 20.24 0.02 18.93 0.01 17.29 0.00 16.53 0.00 16.18 0.01 14.91 0.04 14.37 0.05 14.09 0.05 0 + 28 127.86211 18.682350 -38.0 5.5 -17.3 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.25 0.03 13.59 0.03 13.32 0.03 0 + 29 127.87202 18.398043 -35.1 4.1 -5.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.63 0.02 13.05 0.03 12.75 0.02 0 + 30 127.87431 20.410384 -38.4 4.1 -16.0 4.1 15.33 0.00 14.13 0.00 13.39 0.60 12.99 0.00 12.78 0.00 11.67 0.02 10.95 0.02 10.77 0.02 1 + 31 127.88665 21.024420 -39.8 4.1 -14.3 4.1 15.27 0.00 14.00 0.00 13.39 0.60 13.15 0.00 13.00 0.00 11.89 0.02 11.25 0.01 11.12 0.02 0 + 32 127.91843 21.273458 -34.1 4.1 -21.4 4.1 19.67 0.01 18.41 0.01 16.83 0.00 16.07 0.00 15.74 0.00 14.42 0.03 13.80 0.03 13.59 0.03 0 + 33 127.91845 19.798370 -35.9 4.1 -14.1 4.1 15.69 0.00 18.45 0.02 13.74 0.60 13.31 0.00 13.11 0.00 11.92 0.02 11.22 0.02 11.03 0.02 0 + 34 127.92032 18.495262 -38.6 4.1 -18.8 4.1 18.67 0.01 17.47 0.01 16.08 0.00 15.44 0.00 15.15 0.00 13.97 0.02 13.37 0.04 13.11 0.03 1 + 35 127.92895 18.485112 -40.8 4.1 -19.4 4.1 19.34 0.01 18.11 0.01 16.55 0.00 15.83 0.00 15.50 0.00 14.25 0.03 13.67 0.03 13.37 0.03 0 + 36 127.94647 18.163225 -28.7 4.1 -17.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.55 0.03 13.94 0.04 13.70 0.04 1 + 37 127.96308 21.489645 -34.1 4.1 -16.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.83 0.04 14.12 0.03 13.89 0.05 0 + 38 128.00303 21.422300 -29.2 4.1 -16.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.50 0.03 13.84 0.03 13.62 0.04 0 + 39 128.00775 19.979740 -37.5 4.1 -17.7 4.1 19.18 0.01 17.91 0.01 16.36 0.00 15.65 0.00 15.33 0.00 14.10 0.03 13.43 0.02 13.21 0.03 0 + 40 128.01131 20.772398 -38.6 4.1 -11.9 4.1 19.36 0.02 17.07 0.60 16.31 0.60 15.71 0.00 15.37 0.00 14.15 0.03 13.48 0.02 13.25 0.03 0 + 41 128.02228 19.300140 -32.8 4.1 -8.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.61 0.02 11.89 0.02 11.79 0.01 0 + 42 128.03043 19.606693 -36.6 4.2 -9.9 4.2 21.11 0.05 19.63 0.02 17.82 0.01 17.02 0.00 16.63 0.01 15.25 0.04 14.70 0.06 14.44 0.07 0 + 43 128.03306 18.740739 -29.2 4.1 -15.6 4.1 17.96 0.01 16.75 0.00 15.49 0.00 14.95 0.00 14.68 0.00 13.51 0.02 12.83 0.02 12.57 0.02 1 + 44 128.03559 21.264164 -28.0 4.1 -14.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.10 0.03 14.43 0.05 14.20 0.05 0 + 45 128.04440 21.341547 -35.8 4.1 -9.8 4.1 14.85 0.00 13.70 0.60 13.29 0.60 12.96 0.00 12.84 0.00 11.77 0.02 11.10 0.02 10.96 0.01 1 + 46 128.06274 18.228382 -32.6 1.5 -17.3 1.6 11.06 0.60 10.81 0.60 10.76 0.60 10.78 0.60 10.80 0.60 9.98 0.02 9.73 0.02 9.66 0.02 0 + 47 128.07433 19.547048 -38.8 4.1 -15.6 4.1 18.85 0.01 17.47 0.00 16.12 0.00 15.44 0.00 15.14 0.00 13.88 0.02 13.27 0.04 13.02 0.03 1 + 48 128.07862 19.052386 -38.2 4.1 -16.3 4.1 18.38 0.01 16.54 0.60 15.85 0.00 15.21 0.00 14.93 0.00 13.67 0.02 13.06 0.03 12.83 0.03 1 + 49 128.09771 20.995792 -38.8 4.1 -11.9 4.1 13.24 0.60 12.58 0.60 12.30 0.60 12.14 0.60 12.06 0.00 11.04 0.02 10.59 0.01 10.47 0.02 0 + 50 128.13509 20.844718 -39.6 4.1 -16.0 4.1 18.31 0.01 20.99 0.18 15.78 0.00 15.10 0.00 14.83 0.00 13.60 0.02 12.95 0.02 12.72 0.02 1 + 51 128.13911 20.080077 -43.2 4.1 -14.1 4.1 13.58 0.60 12.85 0.60 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19.11 0.01 18.03 0.01 16.55 0.00 15.87 0.00 15.53 0.00 14.32 0.02 13.65 0.03 13.43 0.03 0 + 89 128.41559 18.596889 -42.2 5.6 -13.0 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.97 0.04 14.35 0.05 14.11 0.05 0 + 90 128.43304 18.797438 -35.5 4.1 -15.2 4.1 17.87 0.00 16.63 0.00 15.41 0.00 14.90 0.00 14.61 0.00 13.44 0.02 12.79 0.03 12.64 0.02 0 + 91 128.43847 19.654393 -34.8 4.1 -10.8 4.1 18.81 0.01 17.53 0.00 15.94 0.00 15.21 0.00 14.87 0.00 13.61 0.02 12.98 0.02 12.67 0.03 0 + 92 128.44561 21.440963 -37.4 4.1 -9.6 4.1 15.27 0.00 18.22 0.01 13.54 0.60 13.24 0.00 13.07 0.00 11.97 0.02 11.31 0.02 11.15 0.02 0 + 93 128.46143 19.782934 -33.2 4.1 -15.2 4.1 16.30 0.00 19.04 0.04 14.22 0.00 13.86 0.00 13.68 0.00 12.57 0.02 11.93 0.02 11.73 0.02 0 + 94 128.46757 19.436574 -37.9 5.6 -18.5 5.6 20.63 0.04 19.30 0.02 17.62 0.01 16.83 0.00 16.42 0.01 15.13 0.04 14.53 0.05 14.45 0.07 1 + 95 128.49677 19.362609 -40.9 4.1 -13.8 4.1 15.59 0.00 14.36 0.00 13.60 0.60 13.18 0.00 12.98 0.00 11.86 0.02 11.19 0.02 11.00 0.02 0 + 96 128.50196 18.926677 -35.4 4.1 -15.5 4.1 18.16 0.01 16.95 0.00 15.58 0.00 14.93 0.00 14.62 0.00 13.43 0.02 12.76 0.02 12.51 0.02 1 + 97 128.50529 17.811222 -27.6 1.1 -12.5 1.1 9.23 0.60 9.07 0.60 9.01 0.60 8.94 0.60 8.94 0.60 8.18 0.02 7.99 0.01 7.92 0.01 1 BD+18 1976 + 98 128.50637 21.010821 -38.2 5.6 -16.1 5.6 20.12 0.03 18.84 0.02 17.14 0.00 16.35 0.00 16.00 0.00 14.77 0.03 14.09 0.03 13.89 0.04 0 + 99 128.50942 17.585442 -30.1 4.2 -8.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.56 0.03 13.86 0.04 13.68 0.05 0 + 100 128.51015 19.322755 -37.4 4.1 -13.6 4.1 16.92 0.00 15.53 0.60 14.78 0.60 14.09 0.00 13.84 0.00 12.66 0.02 12.05 0.02 11.76 0.02 1 + 101 128.51302 20.341817 -38.5 4.1 -17.8 4.1 18.53 0.01 16.86 0.60 16.08 0.60 15.37 0.00 15.08 0.00 13.88 0.02 13.28 0.03 12.97 0.03 0 + 102 128.51475 19.795274 -42.6 4.1 -16.0 4.1 13.77 0.00 13.08 0.60 12.73 0.60 12.52 0.60 12.39 0.00 11.32 0.02 10.82 0.02 10.68 0.02 1 + 103 128.51818 20.575100 -40.6 4.1 -15.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.04 0.02 10.57 0.02 10.44 0.02 0 + 104 128.52029 20.679791 -38.6 4.1 -11.4 4.1 17.98 0.01 16.76 0.00 15.40 0.00 14.76 0.00 14.48 0.00 13.25 0.02 12.66 0.02 12.39 0.02 0 + 105 128.52777 20.829654 -37.5 5.6 -15.0 5.6 19.97 0.02 18.64 0.01 17.08 0.00 16.33 0.00 15.98 0.00 14.65 0.03 14.06 0.02 13.86 0.04 0 + 106 128.54258 19.804923 -38.3 4.1 -14.6 4.1 14.54 0.00 13.38 0.60 13.08 0.60 12.93 0.60 12.80 0.00 11.74 0.02 11.08 0.02 10.99 0.02 0 + 107 128.54749 21.070778 -38.3 5.6 -10.1 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.00 0.03 14.28 0.04 14.09 0.05 0 + 108 128.55778 21.397819 -36.4 4.1 -16.7 4.1 19.27 0.01 17.99 0.01 16.48 0.00 15.77 0.00 15.46 0.00 14.27 0.02 13.57 0.03 13.30 0.04 0 + 109 128.57507 18.470961 -36.3 5.5 -19.8 5.5 19.87 0.02 18.54 0.01 16.89 0.00 16.11 0.00 15.73 0.00 14.50 0.03 13.79 0.04 13.72 0.04 0 + 110 128.58831 21.878825 -34.9 4.1 -14.2 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0 + 125 128.72958 20.105474 -39.0 4.1 -16.3 4.1 16.66 0.00 15.43 0.00 14.49 0.00 14.08 0.00 13.90 0.00 12.77 0.02 12.11 0.02 11.91 0.02 0 + 126 128.73069 20.184406 -40.7 4.1 -20.8 4.1 19.32 0.01 17.98 0.01 16.33 0.00 15.55 0.00 15.22 0.00 13.86 0.03 13.28 0.03 13.01 0.03 0 + 127 128.73821 17.003277 -38.9 4.2 -12.3 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.27 0.03 13.69 0.03 13.41 0.03 0 + 128 128.73878 17.487793 -39.1 5.7 -15.1 5.7 20.97 0.05 19.63 0.02 17.86 0.01 17.08 0.00 16.73 0.01 15.47 0.05 14.66 0.06 14.45 0.07 0 + 129 128.74687 21.143692 -41.3 5.5 -6.3 5.5 19.86 0.02 17.97 0.60 16.80 0.00 15.99 0.00 15.61 0.00 14.35 0.03 13.74 0.03 13.45 0.03 0 + 130 128.74697 18.301517 -33.1 4.1 -16.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.73 0.03 14.04 0.03 13.84 0.04 0 + 131 128.74849 21.097000 -32.1 1.4 -16.2 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.01 0.02 9.76 0.03 9.68 0.02 1 + 132 128.75271 17.489502 -39.9 4.2 -20.2 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.18 0.02 13.57 0.02 13.38 0.03 0 + 133 128.76576 20.831211 -33.1 4.1 -9.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.18 0.02 12.54 0.02 12.36 0.02 0 + 134 128.77586 18.823513 -37.8 4.1 -18.1 4.1 18.81 0.01 17.59 0.01 16.05 0.00 15.39 0.00 15.04 0.00 13.84 0.02 13.23 0.03 12.95 0.02 0 + 135 128.77629 19.946653 -36.6 5.5 -15.6 5.5 20.82 0.04 19.59 0.02 17.64 0.00 16.70 0.00 16.24 0.00 14.95 0.05 14.26 0.04 13.99 0.05 0 + 136 128.78278 20.339761 -38.0 4.1 -14.0 4.1 18.15 0.01 16.89 0.00 15.64 0.00 15.05 0.00 14.79 0.00 13.60 0.02 12.98 0.03 12.75 0.02 0 + 137 128.78346 19.990328 -37.4 4.1 -11.9 4.1 13.83 0.00 16.84 0.01 13.64 0.00 12.44 0.00 12.19 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 138 128.79145 18.207809 -32.7 4.1 -16.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.69 0.03 13.14 0.02 12.89 0.02 0 + 139 128.81145 19.275456 -35.5 4.1 -16.8 4.1 17.54 0.00 15.90 0.60 15.04 0.00 14.45 0.00 14.18 0.00 12.94 0.02 12.36 0.02 12.07 0.02 0 + 140 128.81330 19.375414 -34.6 5.5 -21.2 5.5 20.92 0.03 19.56 0.02 17.78 0.01 16.97 0.00 16.59 0.01 15.24 0.05 14.58 0.05 14.32 0.06 1 + 141 128.81677 19.202154 -35.2 4.1 -19.4 4.1 19.45 0.01 17.49 0.60 16.64 0.00 15.92 0.00 15.60 0.00 14.35 0.02 13.76 0.03 13.46 0.03 0 + 142 128.82053 19.914836 -33.0 4.1 -11.2 4.1 17.48 0.00 16.23 0.00 15.08 0.00 14.57 0.00 14.32 0.00 13.14 0.03 12.53 0.02 12.29 0.02 0 + 143 128.82096 17.606751 -32.4 4.2 -7.6 4.2 19.38 0.01 18.09 0.01 16.45 0.00 15.70 0.00 15.32 0.00 14.06 0.03 13.46 0.02 13.23 0.03 1 + 144 128.82100 20.969681 -36.4 5.6 -6.9 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.22 0.05 14.64 0.06 14.29 0.05 0 + 145 128.82406 19.636136 -34.5 2.1 -12.3 2.2 12.50 0.60 11.93 0.60 11.68 0.60 11.54 0.60 11.48 0.60 10.50 0.02 10.12 0.02 10.01 0.02 0 + 146 128.82407 20.564629 -33.3 1.2 -12.4 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.88 0.01 7.81 0.01 7.76 0.01 0 HD 72757 + 147 128.83047 19.761436 -35.0 4.1 -13.9 4.1 19.37 0.01 18.11 0.01 16.56 0.00 15.82 0.00 15.49 0.00 14.25 0.03 13.63 0.03 13.32 0.03 1 + 148 128.83105 19.590069 -34.0 0.7 -12.2 0.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.37 0.02 5.10 0.03 5.00 0.01 0 35 Cnc + 149 128.84030 18.492831 -37.7 4.1 -17.1 4.1 17.49 0.01 16.26 0.00 15.25 0.01 14.59 0.00 14.34 0.00 13.18 0.02 12.56 0.03 12.27 0.02 0 + 150 128.86682 20.196461 -33.4 1.5 -15.2 1.5 10.61 0.60 10.28 0.60 10.16 0.60 10.12 0.60 10.11 0.60 9.19 0.03 8.97 0.01 8.92 0.02 0 BD+20 2119 + 151 128.89103 18.929839 -39.0 4.1 -14.4 4.1 15.86 0.00 14.63 0.00 14.05 0.00 13.57 0.00 13.39 0.00 12.27 0.02 11.61 0.02 11.42 0.01 0 + 152 128.89870 18.995710 -38.8 4.1 -13.0 4.1 16.06 0.00 14.84 0.00 14.07 0.00 13.73 0.00 13.56 0.00 12.47 0.02 11.79 0.02 11.59 0.02 0 + 153 128.90047 19.532837 -43.0 5.5 -14.8 5.5 20.88 0.04 18.76 0.60 17.78 0.01 16.96 0.00 16.56 0.01 15.26 0.04 14.65 0.06 14.38 0.06 1 + 154 128.91048 18.358934 -45.1 4.1 -15.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.86 0.03 13.37 0.02 13.06 0.03 0 + 155 128.91356 20.402708 -31.7 4.1 -13.3 4.1 19.28 0.01 17.91 0.01 16.34 0.00 15.57 0.00 15.21 0.00 13.96 0.02 13.33 0.03 13.05 0.02 0 + 156 128.91522 19.126752 -35.2 4.1 -12.5 4.1 17.79 0.00 16.60 0.00 15.29 0.00 14.70 0.00 14.42 0.00 13.23 0.02 12.50 0.02 12.29 0.02 0 + 157 128.91720 18.707836 -37.8 4.1 -14.0 4.1 18.14 0.01 16.92 0.00 15.47 0.00 14.80 0.00 14.50 0.00 13.28 0.02 12.64 0.02 12.36 0.02 0 + 158 128.93589 18.960629 -40.1 4.1 -13.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.40 0.03 13.84 0.03 13.55 0.03 0 + 159 128.93772 19.771095 -36.2 0.9 -13.3 0.7 7.23 0.60 7.41 0.60 7.58 0.60 7.70 0.60 7.77 0.60 7.14 0.01 7.12 0.01 7.12 0.01 1 HD 72846 + 160 128.93804 19.640580 -40.9 4.1 -10.0 4.1 13.30 0.00 16.85 0.01 13.31 0.00 12.65 0.00 12.11 0.00 11.12 0.02 10.68 0.02 10.54 0.01 0 + 161 128.94480 19.870931 -40.7 4.1 -11.1 4.1 15.24 0.00 14.02 0.00 13.51 0.00 13.13 0.00 12.99 0.00 11.89 0.02 11.28 0.02 11.06 0.01 0 + 162 128.94667 18.141630 -41.8 4.1 -14.4 4.1 18.47 0.01 16.71 0.60 15.89 0.60 15.14 0.00 14.82 0.00 13.62 0.02 12.96 0.02 12.69 0.02 0 + 163 128.94701 19.589605 -37.5 4.1 -17.9 4.1 19.46 0.01 17.98 0.60 16.65 0.00 15.91 0.00 15.60 0.00 14.37 0.02 13.72 0.03 13.47 0.03 1 + 164 128.95933 19.850018 -38.7 4.1 -15.8 4.1 19.61 0.01 18.33 0.01 16.71 0.00 16.01 0.00 15.66 0.00 14.38 0.04 13.88 0.04 13.56 0.04 0 + 165 128.95991 20.151270 -29.2 4.1 -9.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.82 0.03 13.17 0.03 12.94 0.02 1 + 166 128.96168 18.895420 -42.3 5.6 -16.7 5.6 17.78 0.01 16.55 0.00 15.15 0.00 14.48 0.00 14.16 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 167 128.96690 20.266310 -34.3 5.5 -10.9 5.5 20.81 0.05 19.30 0.02 17.50 0.00 16.63 0.00 16.21 0.00 14.83 0.03 14.24 0.04 13.84 0.03 0 + 168 128.97033 18.314147 -40.7 4.1 -10.9 4.1 17.73 0.01 16.31 0.00 15.09 0.00 14.56 0.00 14.29 0.00 13.14 0.02 12.52 0.02 12.26 0.03 0 + 169 128.97730 18.149378 -32.7 1.5 -9.7 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.76 0.02 9.52 0.02 9.44 0.03 1 + 170 128.98533 20.618590 -39.0 5.5 -14.2 5.5 20.55 0.03 19.31 0.01 17.48 0.01 16.71 0.00 16.30 0.00 15.03 0.04 14.36 0.05 14.08 0.05 0 + 171 128.98724 20.826298 -34.3 2.2 -19.4 2.2 12.30 0.60 11.71 0.60 11.51 0.60 11.42 0.60 11.36 0.60 10.38 0.02 9.91 0.02 9.83 0.02 0 + 172 128.98840 19.351791 -34.2 5.5 -14.2 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.05 0.04 14.58 0.05 14.15 0.04 0 + 173 128.99654 18.308223 -32.2 4.1 -14.1 4.1 17.41 0.01 16.16 0.00 15.24 0.60 14.53 0.00 14.30 0.00 13.14 0.02 12.49 0.02 12.28 0.02 0 + 174 128.99763 20.077918 -34.9 4.1 -14.2 4.1 18.99 0.01 17.71 0.01 16.18 0.00 15.47 0.00 15.17 0.00 13.88 0.03 13.28 0.03 13.00 0.02 0 + 175 128.99937 19.525518 -38.4 4.1 -10.4 4.1 13.64 0.00 12.90 0.60 12.57 0.60 12.39 0.60 12.31 0.00 11.21 0.02 10.67 0.02 10.57 0.02 0 + 176 129.00189 17.975905 -34.9 4.1 -16.3 4.1 17.23 0.00 15.98 0.00 14.84 0.00 14.30 0.00 14.07 0.00 12.87 0.02 12.22 0.02 12.00 0.02 1 + 177 129.00666 19.958706 -36.8 5.5 -8.0 5.5 21.08 0.04 19.84 0.02 17.85 0.00 16.96 0.00 16.53 0.01 15.19 0.04 14.69 0.05 14.32 0.05 1 + 178 129.01342 20.837688 -41.9 4.1 -16.1 4.1 20.01 0.03 18.68 0.01 17.04 0.00 16.30 0.01 15.92 0.00 14.68 0.02 14.03 0.03 13.78 0.03 0 + 179 129.01377 19.424652 -38.7 4.1 -10.0 4.1 19.64 0.02 17.76 0.60 16.85 0.60 16.12 0.00 15.77 0.01 14.51 0.03 13.94 0.03 13.66 0.03 0 + 180 129.01839 19.920279 -39.5 4.1 -16.7 4.1 15.29 0.00 14.11 0.00 13.65 0.60 13.22 0.00 13.08 0.00 11.99 0.02 11.30 0.02 11.16 0.02 1 + 181 129.02619 20.683304 -37.8 4.1 -13.6 4.1 16.06 0.00 14.83 0.00 99.00 99.00 13.67 0.00 13.44 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 182 129.02626 20.683307 -37.8 4.1 -13.6 4.1 16.05 0.00 14.77 0.60 14.18 0.60 13.79 0.60 13.44 0.00 12.32 0.02 11.66 0.02 11.45 0.02 1 + 183 129.03477 20.814672 -35.8 4.1 -15.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.62 0.03 14.02 0.03 13.78 0.04 0 + 184 129.03547 16.954896 -36.1 4.2 -6.2 4.2 17.23 0.00 16.00 0.00 14.95 0.00 14.48 0.00 14.25 0.00 13.13 0.02 12.55 0.03 12.28 0.02 0 + 185 129.03574 19.957039 -30.5 5.5 -17.3 5.5 19.50 0.01 18.16 0.01 16.53 0.00 15.75 0.00 15.40 0.00 14.16 0.02 13.50 0.03 13.20 0.03 0 + 186 129.03591 18.747452 -39.8 4.1 -17.8 4.1 15.54 0.00 14.30 0.00 13.92 0.00 13.37 0.00 13.25 0.00 12.11 0.02 11.43 0.02 11.29 0.02 0 + 187 129.03721 19.158567 -37.6 4.1 -16.8 4.1 17.89 0.01 16.65 0.00 15.36 0.00 14.78 0.00 14.49 0.00 13.27 0.02 12.61 0.03 12.38 0.02 0 + 188 129.03728 16.957916 -35.3 4.2 -7.3 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.34 0.02 11.71 0.02 11.55 0.02 0 + 189 129.03731 19.230000 -33.9 4.1 -13.1 4.1 17.05 0.00 15.85 0.00 14.78 0.00 14.30 0.00 14.07 0.00 12.94 0.02 12.27 0.02 12.04 0.02 0 + 190 129.04508 19.694791 -35.7 5.5 -5.0 5.5 20.50 0.03 18.44 0.60 17.50 0.60 16.71 0.01 16.35 0.01 15.07 0.03 14.51 0.03 14.22 0.05 0 + 191 129.04754 19.877847 -39.5 4.1 -11.6 4.1 16.18 0.00 14.93 0.00 14.19 0.00 13.81 0.00 13.63 0.00 12.53 0.02 11.85 0.02 11.66 0.02 0 + 192 129.05005 17.879600 -37.8 4.2 -14.8 4.2 19.65 0.02 18.44 0.01 16.83 0.00 16.08 0.00 15.77 0.00 14.50 0.03 13.84 0.03 13.55 0.04 0 + 193 129.05019 18.735466 -35.3 4.1 -20.5 4.1 18.99 0.01 17.67 0.01 16.16 0.00 15.43 0.00 15.07 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 194 129.05863 19.621498 -30.8 2.2 -9.7 2.2 13.60 0.60 12.82 0.60 12.41 0.60 12.15 0.60 11.89 0.00 10.93 0.02 10.47 0.02 10.36 0.02 0 + 195 129.05943 19.425065 -37.4 4.1 -15.5 4.1 17.46 0.00 16.26 0.00 14.89 0.00 14.28 0.00 13.98 0.00 12.76 0.02 12.15 0.02 11.91 0.02 0 + 196 129.06461 20.686041 -38.6 4.1 -16.5 4.1 16.24 0.00 14.99 0.00 14.17 0.00 13.85 0.00 13.63 0.00 12.51 0.02 11.87 0.02 11.63 0.02 0 + 197 129.06618 20.120241 -45.4 4.1 -13.9 4.1 16.35 0.00 15.11 0.00 14.26 0.00 13.90 0.00 13.74 0.00 12.58 0.02 11.90 0.02 11.75 0.02 0 + 198 129.06652 20.553092 -38.7 4.1 -15.1 4.1 15.33 0.00 14.11 0.00 13.63 0.00 13.29 0.00 13.14 0.00 12.04 0.02 11.42 0.02 11.23 0.02 0 + 199 129.06723 19.377951 -44.6 5.5 -11.8 5.5 20.02 0.02 18.70 0.02 17.03 0.00 16.29 0.00 15.95 0.01 14.63 0.03 13.98 0.03 13.70 0.04 0 + 200 129.06767 17.381580 -33.5 1.6 -8.1 1.7 11.69 0.60 11.31 0.60 11.19 0.60 11.16 0.60 11.15 0.60 10.26 0.02 9.93 0.02 9.86 0.01 1 + 201 129.06820 19.542041 -34.2 2.2 -17.5 2.2 11.88 0.60 11.34 0.60 11.13 0.60 11.04 0.60 10.99 0.60 10.03 0.02 9.64 0.02 9.52 0.02 0 + 202 129.07270 20.341539 -35.9 1.0 -13.8 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.21 0.01 7.20 0.01 7.17 0.03 0 HD 72942 + 203 129.07915 18.918962 -40.3 4.1 -19.5 4.1 17.57 0.00 16.38 0.00 15.17 0.00 14.65 0.00 14.41 0.00 13.21 0.02 12.60 0.02 12.35 0.03 0 + 204 129.07975 19.898557 -36.6 4.1 -15.1 4.1 18.08 0.01 16.82 0.00 15.52 0.00 14.93 0.00 14.64 0.00 13.48 0.02 12.84 0.02 12.57 0.02 0 + 205 129.08489 20.116744 -43.2 4.1 -11.5 4.1 18.51 0.01 17.29 0.00 15.90 0.00 15.26 0.00 14.99 0.00 13.77 0.02 13.11 0.02 12.90 0.02 0 + 206 129.08978 20.897382 -39.1 4.1 -14.0 4.1 17.71 0.01 16.52 0.00 15.30 0.00 14.74 0.00 14.48 0.00 13.28 0.02 12.68 0.02 12.43 0.03 0 + 207 129.09084 20.205452 -40.6 4.1 -17.2 4.1 17.38 0.00 16.16 0.00 14.99 0.00 14.47 0.00 14.22 0.00 13.06 0.02 12.43 0.02 12.17 0.02 0 + 208 129.09330 20.118583 -31.9 4.1 -12.7 4.1 19.45 0.01 18.13 0.01 16.61 0.00 15.92 0.00 15.62 0.00 14.36 0.03 13.75 0.03 13.45 0.03 0 + 209 129.09443 19.191459 -39.4 4.1 -11.4 4.1 13.13 0.00 15.17 0.00 13.12 0.00 12.38 0.00 11.76 0.00 10.76 0.02 10.35 0.02 10.26 0.02 0 + 210 129.09750 18.405818 -38.0 4.1 -14.4 4.1 16.66 0.00 15.42 0.00 14.49 0.00 14.07 0.00 13.86 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 211 129.10471 21.149002 -35.9 4.1 -13.5 4.1 16.90 0.00 15.69 0.00 14.94 0.60 14.22 0.00 14.03 0.00 12.88 0.02 12.23 0.03 11.98 0.02 0 + 212 129.11209 22.004673 -34.9 4.1 -16.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.60 0.03 12.95 0.03 12.75 0.02 0 + 213 129.11293 19.865147 -33.5 4.1 -13.9 4.1 16.10 0.00 14.86 0.00 13.92 0.00 13.47 0.00 13.26 0.00 12.10 0.03 11.46 0.01 11.23 0.02 0 + 214 129.11594 17.914867 -38.5 1.5 -14.1 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.72 0.02 9.48 0.02 9.40 0.02 0 + 215 129.11612 21.121153 -38.7 4.1 -12.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.72 0.02 11.11 0.01 10.96 0.02 0 + 216 129.11786 20.228555 -39.5 4.1 -10.0 4.1 12.99 0.60 12.38 0.60 12.15 0.60 12.03 0.60 11.97 0.60 10.98 0.02 10.52 0.02 10.44 0.02 0 + 217 129.12255 21.052867 -36.3 4.1 -11.2 4.1 18.97 0.02 17.69 0.01 16.43 0.60 15.58 0.00 15.27 0.00 14.08 0.02 13.41 0.02 13.19 0.02 0 + 218 129.12455 18.965845 -35.8 1.1 -13.4 1.2 9.48 0.60 9.32 0.60 9.30 0.60 9.31 0.60 9.31 0.60 8.51 0.02 8.36 0.01 8.30 0.01 0 BD+19 2045 + 219 129.12700 19.920524 -39.7 5.5 -20.6 5.5 20.30 0.02 19.01 0.01 17.28 0.00 16.49 0.00 16.09 0.00 14.80 0.03 14.19 0.02 13.87 0.04 0 + 220 129.13000 19.592605 -36.6 4.1 -15.5 4.1 18.75 0.01 17.53 0.01 16.08 0.00 15.42 0.00 15.13 0.00 13.90 0.02 13.28 0.02 13.01 0.02 1 + 221 129.13120 18.315246 -39.4 4.1 -17.3 4.1 19.18 0.01 17.90 0.00 16.30 0.00 15.54 0.00 15.19 0.00 13.88 0.03 13.34 0.03 13.01 0.03 0 + 222 129.13901 19.915091 -39.1 4.1 -15.7 4.1 16.98 0.00 15.77 0.00 14.71 0.00 14.27 0.00 14.07 0.00 12.89 0.03 12.28 0.02 12.01 0.02 0 + 223 129.14540 20.275177 -40.9 4.1 -14.5 4.1 17.46 0.00 16.24 0.00 15.03 0.00 14.51 0.00 14.25 0.00 13.02 0.02 12.38 0.02 12.14 0.02 1 + 224 129.15163 19.185179 -39.7 4.1 -10.0 4.1 15.28 0.00 14.11 0.00 13.51 0.00 13.38 0.00 13.12 0.00 12.02 0.02 11.42 0.02 11.20 0.02 1 + 225 129.15932 18.458836 -29.1 4.1 -9.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.52 0.03 13.88 0.02 13.65 0.03 0 + 226 129.16439 20.376067 -33.7 4.1 -15.5 4.1 18.54 0.01 17.29 0.00 15.85 0.00 15.20 0.00 14.94 0.00 13.61 0.02 13.01 0.02 12.75 0.02 0 + 227 129.16665 20.609791 -36.6 1.3 -14.7 1.3 12.17 0.00 11.87 0.00 11.94 0.00 11.59 0.00 11.39 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 228 129.17104 18.307276 -39.3 4.1 -19.6 4.1 17.01 0.00 15.80 0.00 14.75 0.00 14.28 0.00 14.07 0.00 12.92 0.02 12.26 0.02 12.03 0.02 0 + 229 129.17152 20.277744 -40.2 4.1 -16.0 4.1 18.98 0.01 17.67 0.01 16.23 0.00 15.58 0.00 15.27 0.00 14.05 0.02 13.49 0.03 13.16 0.03 1 + 230 129.17416 20.411080 -39.1 4.1 -13.6 4.1 13.48 0.60 12.74 0.60 12.46 0.60 12.32 0.60 12.11 0.00 11.18 0.02 10.62 0.02 10.52 0.02 0 + 231 129.17778 18.895201 -38.7 4.1 -10.8 4.1 14.52 0.00 13.51 0.60 13.11 0.60 12.87 0.60 12.65 0.00 11.59 0.02 10.97 0.02 10.84 0.02 0 + 232 129.18743 20.146029 -36.3 4.1 -10.8 4.1 19.84 0.02 18.59 0.01 16.97 0.00 16.24 0.00 15.89 0.00 14.57 0.03 14.00 0.04 13.78 0.03 0 + 233 129.19039 20.123951 -35.9 1.6 -10.7 1.6 12.01 0.60 11.57 0.60 11.39 0.60 11.30 0.60 11.27 0.60 10.34 0.02 10.03 0.02 9.94 0.02 0 + 234 129.19620 18.579675 -35.8 4.1 -13.5 4.1 13.23 0.60 12.60 0.60 12.34 0.60 12.19 0.60 12.22 0.00 11.11 0.02 10.68 0.02 10.59 0.02 0 + 235 129.20000 18.882805 -35.7 0.9 -11.4 0.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.04 0.01 7.95 0.01 7.94 0.03 0 HD 73045 + 236 129.20387 19.316466 -38.8 4.1 -15.5 4.1 17.16 0.00 15.94 0.00 14.78 0.00 14.26 0.00 14.02 0.00 12.84 0.02 12.16 0.02 11.95 0.02 0 + 237 129.20388 19.257387 -36.3 1.6 -12.9 1.7 11.61 0.60 11.20 0.60 11.06 0.60 11.01 0.60 10.99 0.60 10.08 0.02 9.75 0.03 9.69 0.02 0 + 238 129.21044 18.681675 -37.4 4.1 -16.5 4.1 19.26 0.02 18.05 0.01 16.52 0.00 15.79 0.00 15.44 0.00 14.24 0.02 13.59 0.03 13.34 0.04 1 + 239 129.21277 19.071823 -36.4 4.1 -12.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.11 0.02 13.53 0.02 13.24 0.02 0 + 240 129.21501 18.838670 -38.8 5.5 -17.7 5.5 19.56 0.01 18.24 0.01 16.70 0.00 15.96 0.00 15.61 0.00 14.31 0.02 13.80 0.03 13.48 0.03 0 + 241 129.21503 19.076404 -35.8 4.1 -14.5 4.1 14.23 0.00 13.44 0.60 13.06 0.60 12.84 0.60 12.62 0.00 11.58 0.02 10.98 0.02 10.85 0.02 0 + 242 129.22384 18.495829 -37.1 4.1 -9.0 4.1 14.50 0.00 13.53 0.60 13.15 0.60 12.93 0.60 12.72 0.00 11.67 0.02 11.05 0.02 10.95 0.02 0 + 243 129.22507 19.617138 -31.8 4.1 -14.9 4.1 19.96 0.02 18.67 0.01 17.06 0.00 16.32 0.00 16.00 0.00 14.65 0.03 14.10 0.04 13.81 0.03 0 + 244 129.22532 18.756870 -37.5 4.1 -7.6 4.1 13.01 0.60 12.41 0.60 12.17 0.60 12.06 0.60 11.99 0.60 11.00 0.02 10.54 0.03 10.47 0.02 0 + 245 129.23316 19.599150 -35.4 4.1 -10.2 4.1 17.78 0.00 16.55 0.00 15.31 0.00 14.73 0.00 14.52 0.00 13.30 0.02 12.65 0.02 12.43 0.02 0 + 246 129.23442 18.963341 -37.7 4.1 -12.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.78 0.02 11.10 0.02 10.91 0.02 0 + 247 129.23571 20.319447 -39.6 4.1 -12.9 4.1 16.36 0.00 15.09 0.00 14.01 0.00 13.81 0.00 13.29 0.00 12.07 0.02 11.44 0.02 11.23 0.02 0 + 248 129.23655 19.091115 -37.0 4.1 -15.5 4.1 15.28 0.00 14.05 0.00 13.51 0.60 13.12 0.00 12.96 0.00 11.80 0.02 11.12 0.02 10.97 0.02 0 + 249 129.24088 21.565474 -34.2 4.1 -17.2 4.1 15.03 0.00 13.89 0.00 13.66 0.60 13.13 0.00 13.01 0.00 11.95 0.02 11.27 0.02 11.12 0.02 1 + 250 129.24437 18.831177 -33.2 4.1 -16.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.11 0.03 13.44 0.03 13.26 0.02 0 + 251 129.24599 17.701742 -37.6 5.7 -14.7 5.7 21.38 0.07 19.95 0.03 17.79 0.60 17.17 0.00 16.75 0.01 15.54 0.05 14.73 0.05 14.72 0.09 1 + 252 129.24946 20.406500 -37.2 4.1 -14.4 4.1 15.50 0.00 14.30 0.00 13.64 0.00 13.39 0.00 13.22 0.00 12.13 0.02 11.50 0.02 11.32 0.02 0 + 253 129.25598 20.896657 -34.1 5.6 -13.6 5.6 20.56 0.04 19.29 0.02 17.50 0.01 16.73 0.00 16.34 0.01 14.93 0.04 14.42 0.04 14.18 0.05 0 + 254 129.25847 19.604757 -35.9 1.1 -13.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.31 0.01 8.15 0.03 8.06 0.01 0 HD 73081 + 255 129.26106 19.328458 -37.5 4.1 -12.9 4.1 17.51 0.00 16.31 0.00 15.11 0.00 14.55 0.00 14.26 0.00 13.05 0.02 12.43 0.02 12.19 0.02 0 + 256 129.26428 19.178087 -42.0 4.1 -9.8 4.1 14.02 0.00 13.24 0.60 12.88 0.60 12.67 0.60 12.49 0.00 11.44 0.02 10.84 0.02 10.73 0.02 0 + 257 129.26459 19.536000 -39.2 4.1 -14.0 4.1 17.44 0.01 16.20 0.01 14.95 0.00 14.31 0.00 14.09 0.00 12.93 0.02 12.29 0.02 12.10 0.02 0 + 258 129.26551 18.667342 -39.8 4.1 -15.0 4.1 14.64 0.00 13.56 0.00 13.32 0.60 12.93 0.00 12.76 0.00 11.69 0.02 11.05 0.02 10.89 0.02 0 + 259 129.27439 19.283030 -39.6 4.1 -9.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.21 0.03 13.63 0.03 13.31 0.04 1 + 260 129.27969 20.778861 -36.8 4.1 -12.6 4.1 18.53 0.01 17.29 0.00 15.86 0.00 15.19 0.00 14.88 0.00 13.66 0.02 13.04 0.03 12.76 0.02 0 + 261 129.28010 17.796881 -31.3 4.2 -18.1 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.65 0.02 12.97 0.02 12.77 0.02 0 + 262 129.29774 19.803656 -36.4 1.3 -13.3 1.3 11.60 0.60 11.20 0.60 11.05 0.60 10.99 0.60 10.98 0.60 10.07 0.02 9.78 0.02 9.69 0.02 0 + 263 129.29946 20.679817 -32.2 4.1 -17.3 4.1 17.55 0.00 16.28 0.00 15.14 0.00 14.65 0.00 14.40 0.00 13.23 0.02 12.58 0.03 12.29 0.02 1 + 264 129.30243 20.544807 -41.0 4.1 -13.0 4.1 15.38 0.00 14.16 0.00 13.56 0.00 13.32 0.00 13.14 0.00 12.13 0.02 11.48 0.03 11.37 0.02 0 + 265 129.30785 17.513550 -42.7 4.2 -9.7 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.18 0.03 10.66 0.03 10.51 0.03 0 + 266 129.31360 20.966413 -34.3 4.1 -18.6 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.43 0.02 12.83 0.02 12.57 0.03 0 + 267 129.31806 19.486197 -38.6 4.1 -11.2 4.1 14.48 0.00 13.35 0.60 12.91 0.60 12.64 0.60 12.37 0.00 11.29 0.02 10.61 0.02 10.47 0.02 0 + 268 129.32444 19.488034 -38.0 5.5 -9.1 5.5 21.14 0.04 19.66 0.03 17.87 0.01 17.02 0.01 16.60 0.01 15.24 0.05 14.72 0.04 14.35 0.06 0 + 269 129.32610 19.698930 -35.1 2.2 -15.2 2.2 12.02 0.00 15.67 0.00 11.77 0.00 11.19 0.00 11.17 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 270 129.33008 18.148255 -43.0 4.1 -8.7 4.1 15.07 0.00 13.93 0.00 13.64 0.60 13.19 0.00 12.99 0.00 11.94 0.02 11.33 0.02 11.14 0.02 1 + 271 129.33285 19.053303 -39.3 4.1 -15.9 4.1 17.12 0.00 15.92 0.00 14.74 0.00 14.19 0.00 13.95 0.00 12.75 0.02 12.09 0.02 11.83 0.02 0 + 272 129.33492 20.535525 -29.8 5.5 -9.8 5.5 20.73 0.04 19.39 0.01 17.52 0.01 16.66 0.00 16.24 0.01 15.02 0.05 14.41 0.05 14.02 0.04 0 + 273 129.34246 20.176991 -37.5 1.3 -16.4 1.3 11.92 0.00 11.42 0.00 11.13 0.00 11.49 0.00 11.23 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 274 129.34337 22.033413 -40.0 4.1 -18.2 4.1 18.10 0.01 16.91 0.00 15.42 0.00 14.70 0.00 14.39 0.00 13.11 0.02 12.52 0.02 12.25 0.03 1 + 275 129.34502 17.687540 -36.3 4.2 -12.0 4.2 18.94 0.02 17.73 0.01 16.30 0.60 15.52 0.00 15.20 0.00 13.92 0.03 13.33 0.03 13.10 0.03 1 + 276 129.34522 17.333260 -40.0 5.7 -6.1 5.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.49 0.03 13.83 0.02 13.54 0.03 0 + 277 129.35072 19.417019 -37.0 4.1 -14.5 4.1 16.54 0.00 20.10 0.09 14.31 0.00 13.78 0.00 13.58 0.00 12.38 0.02 11.74 0.03 11.54 0.02 0 + 278 129.35196 19.786631 -38.9 4.1 -12.7 4.1 19.57 0.01 18.29 0.01 16.72 0.00 16.02 0.00 15.68 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 279 129.35747 17.455038 -29.2 4.2 -13.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.85 0.03 14.23 0.03 13.93 0.04 1 + 280 129.35983 19.486872 -43.1 4.1 -12.2 4.1 14.33 0.00 13.71 0.60 13.21 0.60 12.88 0.60 12.63 0.00 11.56 0.02 11.02 0.02 10.83 0.02 1 + 281 129.35987 19.132104 -39.9 4.1 -11.4 4.1 15.38 0.00 19.18 0.04 13.79 0.00 13.28 0.00 13.27 0.00 12.02 0.02 11.39 0.02 11.19 0.02 1 + 282 129.36264 18.976650 -39.2 4.1 -17.0 4.1 17.83 0.01 16.61 0.00 15.34 0.00 14.78 0.00 14.53 0.00 13.31 0.02 12.74 0.02 12.47 0.02 0 + 283 129.36268 18.810943 -35.6 4.1 -17.7 4.1 19.43 0.01 18.15 0.01 16.67 0.00 15.92 0.00 15.59 0.00 14.31 0.02 13.73 0.03 13.47 0.03 0 + 284 129.36470 19.617519 -31.8 2.1 -12.2 2.1 11.83 0.60 11.38 0.60 11.23 0.60 11.19 0.60 11.16 0.60 10.25 0.03 9.89 0.03 9.81 0.02 0 + 285 129.36613 19.903502 -38.3 4.0 -13.5 4.0 18.12 0.01 16.86 0.01 15.56 0.00 14.95 0.00 14.63 0.00 13.41 0.03 12.85 0.03 12.53 0.02 1 + 286 129.36638 19.562600 -37.7 0.9 -14.0 0.9 9.89 0.60 9.70 0.60 9.63 0.60 9.57 0.60 9.53 0.60 8.72 0.01 8.58 0.03 8.46 0.02 0 BD+20 2130 + 287 129.36749 19.162333 -38.3 1.2 -13.6 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.66 0.03 8.49 0.04 8.40 0.01 1 BD+19 2050 + 288 129.36849 20.607912 -45.1 4.0 -12.5 4.0 16.20 0.00 14.94 0.00 14.10 0.00 13.72 0.00 13.50 0.00 12.36 0.02 11.63 0.03 11.49 0.02 0 + 289 129.37245 18.693190 -33.9 4.0 -18.9 4.0 18.65 0.01 17.37 0.00 16.05 0.00 15.47 0.00 15.17 0.00 14.01 0.03 13.37 0.04 13.15 0.03 1 + 290 129.37802 21.127830 -37.2 4.0 -13.7 4.0 18.74 0.01 17.51 0.00 16.15 0.00 15.52 0.00 15.23 0.00 14.08 0.03 13.38 0.03 13.14 0.02 0 + 291 129.37928 19.103922 -40.2 4.0 -15.3 4.0 14.33 0.00 13.47 0.60 13.04 0.60 12.78 0.60 12.60 0.00 11.49 0.02 10.90 0.03 10.77 0.02 0 + 292 129.38327 16.959908 -40.0 4.2 -11.6 4.2 18.85 0.01 17.59 0.00 16.20 0.00 15.52 0.00 15.22 0.00 13.91 0.02 13.37 0.03 13.09 0.03 0 + 293 129.38418 18.883966 -37.6 4.0 -15.6 4.0 17.03 0.00 15.81 0.00 14.79 0.00 14.29 0.00 14.07 0.00 12.94 0.02 12.27 0.03 12.04 0.02 0 + 294 129.38503 19.521658 -40.9 4.0 -17.2 4.0 16.77 0.00 15.51 0.00 14.55 0.00 14.08 0.00 13.88 0.00 12.72 0.02 11.94 0.03 11.79 0.02 0 + 295 129.38768 20.669431 -39.0 4.0 -11.8 4.0 19.51 0.01 18.24 0.01 16.75 0.00 16.09 0.00 15.76 0.00 14.57 0.03 13.93 0.03 13.68 0.04 0 + 296 129.38778 18.654289 -37.1 1.1 -14.6 1.1 10.97 0.60 10.64 0.60 10.53 0.60 10.50 0.60 10.49 0.60 9.62 0.03 9.35 0.03 9.28 0.02 1 + 297 129.39038 19.310986 -41.0 4.0 -16.7 4.0 17.28 0.00 16.10 0.00 14.90 0.00 14.35 0.00 14.11 0.00 12.91 0.02 12.25 0.03 12.02 0.02 0 + 298 129.39091 20.013681 -34.9 1.1 -14.2 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.10 0.02 8.00 0.04 7.95 0.03 0 HD 73161 + 299 129.39573 18.935492 -41.5 4.0 -19.0 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.65 0.03 13.07 0.03 12.85 0.02 0 + 300 129.39598 20.224018 -44.3 4.0 -12.6 4.0 16.15 0.00 14.91 0.00 13.97 0.00 13.69 0.00 13.35 0.00 12.23 0.02 11.54 0.03 11.36 0.02 1 + 301 129.39892 20.990957 -38.0 4.0 -14.5 4.0 13.42 0.60 12.64 0.60 12.28 0.60 12.07 0.60 11.97 0.60 10.89 0.02 10.37 0.03 10.25 0.01 0 + 302 129.40090 19.265081 -36.9 4.0 -14.0 4.0 13.91 0.00 13.05 0.60 12.75 0.60 12.59 0.60 12.44 0.00 11.43 0.02 10.86 0.03 10.76 0.02 0 + 303 129.40415 19.732918 -35.0 1.0 -14.3 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.37 0.02 7.31 0.02 7.29 0.01 0 HD 73174 + 304 129.40913 18.482478 -41.3 4.0 -9.1 4.0 13.55 0.60 12.80 0.60 12.43 0.60 12.20 0.60 12.31 0.00 11.02 0.02 10.60 0.04 10.43 0.02 0 + 305 129.41337 19.170061 -41.2 5.4 -7.8 5.4 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.06 0.03 14.45 0.06 14.16 0.05 0 + 306 129.41912 19.550865 -40.0 4.0 -15.4 4.0 18.41 0.01 16.73 0.60 15.81 0.00 15.18 0.00 14.90 0.00 13.72 0.03 13.05 0.03 12.80 0.02 0 + 307 129.41960 19.518433 -35.2 1.1 -12.4 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.78 0.04 7.73 0.05 7.66 0.01 0 BR Cnc + 308 129.42642 19.133681 -34.3 1.3 -12.5 1.2 10.12 0.60 9.82 0.60 9.74 0.60 9.75 0.60 9.75 0.60 8.91 0.04 8.65 0.05 8.58 0.02 1 BD+19 2052 + 309 129.43721 19.674712 -34.6 4.0 -13.8 4.0 17.30 0.00 16.08 0.00 14.85 0.00 14.30 0.00 14.03 0.00 12.82 0.02 12.26 0.03 11.99 0.02 0 + 310 129.44118 20.092247 -40.0 4.0 -6.9 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.66 0.04 14.03 0.04 13.76 0.04 0 + 311 129.44326 19.599280 -38.4 4.0 -8.6 4.0 12.73 0.60 12.16 0.60 11.91 0.60 11.77 0.60 11.71 0.60 10.73 0.02 10.34 0.03 10.24 0.02 0 + 312 129.44415 19.438365 -35.0 1.3 -13.0 1.3 10.86 0.60 10.56 0.60 10.47 0.60 10.45 0.60 10.46 0.60 9.59 0.03 9.33 0.03 9.28 0.02 0 + 313 129.44482 19.267237 -35.3 0.7 -12.9 0.6 6.76 0.60 6.79 0.60 6.87 0.60 6.90 0.60 6.97 0.60 6.27 0.02 6.19 0.03 6.16 0.01 1 HD 73210 + 314 129.44735 19.106832 -35.2 2.2 -14.7 2.2 12.66 0.00 13.76 0.00 12.59 0.00 12.12 0.00 11.63 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 315 129.44744 19.106879 -35.2 2.2 -14.7 2.2 12.52 0.60 11.98 0.60 11.78 0.60 11.68 0.60 11.64 0.60 10.67 0.02 10.25 0.03 10.20 0.02 0 + 316 129.45454 17.255005 -40.6 4.2 -13.5 4.2 15.84 0.00 14.64 0.00 13.88 0.00 13.54 0.00 13.39 0.00 12.27 0.02 11.59 0.02 11.39 0.02 0 + 317 129.45496 20.785213 -31.8 5.5 -9.7 5.5 21.41 0.06 20.06 0.02 18.11 0.01 17.22 0.00 16.80 0.01 15.38 0.04 14.90 0.06 14.58 0.07 0 + 318 129.45539 19.962874 -44.2 5.4 -16.9 5.4 21.11 0.04 19.69 0.02 17.85 0.01 16.99 0.00 16.53 0.01 15.25 0.05 14.56 0.05 14.22 0.05 0 + 319 129.45752 19.514097 -33.1 4.0 -9.4 4.0 16.53 0.00 20.07 0.10 14.38 0.00 13.96 0.00 13.79 0.00 12.60 0.02 11.99 0.03 11.78 0.02 0 + 320 129.45759 20.794638 -42.9 5.3 -15.8 5.3 20.16 0.02 18.82 0.01 17.12 0.00 16.36 0.00 16.00 0.00 14.76 0.04 14.09 0.04 13.78 0.03 0 + 321 129.45812 19.891312 -31.9 1.3 -19.6 1.3 11.68 0.60 11.14 0.60 10.94 0.60 10.86 0.60 10.81 0.60 9.86 0.03 9.46 0.03 9.33 0.02 0 + 322 129.46689 19.987194 -37.5 1.3 -15.1 1.3 11.65 0.60 11.25 0.60 11.08 0.60 11.01 0.60 10.98 0.60 10.08 0.02 9.79 0.03 9.69 0.02 1 + 323 129.47474 22.293125 -29.5 4.0 -10.7 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.61 0.03 12.96 0.03 12.69 0.02 0 + 324 129.47728 20.136709 -38.4 4.0 -16.0 4.0 16.64 0.00 15.39 0.00 14.43 0.00 14.05 0.00 13.82 0.00 12.68 0.03 12.00 0.03 11.82 0.02 0 + 325 129.47861 19.486011 -38.0 4.0 -14.5 4.0 18.69 0.01 17.41 0.01 15.97 0.00 15.32 0.00 15.01 0.00 13.76 0.03 13.12 0.04 12.90 0.03 0 + 326 129.48752 19.236229 -35.5 1.6 -14.0 1.6 12.08 0.60 11.63 0.60 11.47 0.60 11.41 0.60 11.38 0.60 10.47 0.03 10.12 0.03 10.04 0.02 0 + 327 129.48795 19.463843 -36.2 4.0 -11.6 4.0 18.48 0.01 17.23 0.00 15.84 0.00 15.20 0.00 14.89 0.00 13.66 0.03 13.02 0.03 12.77 0.03 1 + 328 129.48982 18.973124 -37.1 5.3 -8.7 5.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.80 0.03 14.12 0.05 13.85 0.04 1 + 329 129.50193 19.682268 -36.8 5.3 -11.5 5.3 21.08 0.04 19.76 0.02 17.81 0.01 16.89 0.00 16.43 0.00 14.99 0.04 14.46 0.05 14.12 0.05 1 + 330 129.50246 18.964673 -36.7 4.0 -12.9 4.0 18.14 0.01 16.91 0.00 15.44 0.00 14.76 0.00 14.43 0.00 13.22 0.03 12.59 0.03 12.35 0.02 0 + 331 129.50466 19.978590 -38.5 4.0 -14.5 4.0 18.99 0.01 17.73 0.01 16.26 0.60 15.37 0.00 15.03 0.00 13.81 0.03 13.10 0.03 12.86 0.02 0 + 332 129.50556 20.541504 -31.6 5.3 -18.4 5.3 20.57 0.03 19.19 0.02 17.34 0.60 16.57 0.00 16.15 0.01 14.80 0.03 14.26 0.04 13.88 0.04 0 + 333 129.51021 20.621231 -30.8 5.3 -15.6 5.3 20.55 0.03 19.19 0.03 17.37 0.60 16.67 0.00 16.27 0.01 14.94 0.04 14.28 0.04 14.01 0.04 0 + 334 129.51088 21.205471 -28.6 4.0 -15.2 4.0 14.79 0.00 13.72 0.00 13.41 0.60 13.09 0.00 12.95 0.00 11.88 0.02 11.27 0.03 11.15 0.02 1 + 335 129.51538 19.697548 -37.2 4.0 -8.8 4.0 17.90 0.01 16.66 0.00 15.46 0.00 14.83 0.00 14.60 0.00 13.40 0.02 12.73 0.03 12.50 0.02 0 + 336 129.51878 17.256494 -37.8 5.7 -5.1 5.7 21.34 0.05 20.10 0.03 18.16 0.01 17.32 0.01 16.89 0.01 15.60 0.05 14.92 0.06 14.52 0.07 0 + 337 129.51918 20.659772 -35.8 4.0 -15.8 4.0 18.87 0.01 17.71 0.01 16.20 0.00 15.55 0.00 15.23 0.00 14.01 0.02 13.38 0.04 13.14 0.03 1 + 338 129.52808 19.571599 -36.6 4.0 -18.5 4.0 19.36 0.02 18.12 0.01 16.59 0.00 15.88 0.00 15.56 0.00 14.34 0.03 13.71 0.04 13.39 0.03 0 + 339 129.52953 17.310587 -40.8 4.2 -11.3 4.2 16.56 0.00 15.35 0.00 14.47 0.60 14.00 0.00 13.82 0.00 12.68 0.02 12.00 0.02 11.81 0.02 1 + 340 129.52985 17.782413 -38.4 4.2 -19.5 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.00 0.02 12.36 0.02 12.12 0.02 0 + 341 129.53033 20.448792 -42.3 4.0 -14.1 4.0 17.10 0.00 15.87 0.00 15.08 0.60 14.32 0.00 14.08 0.00 12.94 0.02 12.31 0.03 12.04 0.02 0 + 342 129.53159 19.987877 -36.9 2.2 -14.5 2.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.46 0.03 10.01 0.03 9.90 0.02 0 + 343 129.53218 17.050684 -36.6 1.1 -13.1 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.94 0.04 8.79 0.03 8.70 0.02 1 BD+17 1894 + 344 129.53328 20.064033 -37.7 4.0 -14.2 4.0 19.39 0.01 18.17 0.01 16.63 0.00 15.95 0.00 15.62 0.00 14.40 0.03 13.71 0.03 13.48 0.03 1 + 345 129.53359 20.439466 -35.9 1.3 -15.1 1.3 12.10 0.60 11.62 0.60 11.43 0.60 11.34 0.60 11.30 0.60 10.36 0.02 10.01 0.03 9.93 0.02 0 + 346 129.53362 18.741635 -35.7 4.0 -22.4 4.0 19.99 0.01 18.79 0.01 17.16 0.00 16.37 0.00 16.01 0.00 14.73 0.04 14.17 0.04 13.81 0.04 0 + 347 129.53398 20.446129 -36.5 4.0 -11.2 4.0 17.05 0.00 15.84 0.00 15.09 0.60 14.29 0.00 14.06 0.00 12.94 0.02 12.26 0.03 12.02 0.02 0 + 348 129.55680 21.224794 -41.0 4.0 -10.9 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.37 0.03 13.72 0.04 13.48 0.02 0 + 349 129.55789 21.157247 -35.9 4.0 -18.1 4.0 17.40 0.00 16.18 0.00 15.05 0.00 14.53 0.00 14.30 0.00 13.09 0.02 12.47 0.03 12.21 0.02 0 + 350 129.55930 19.365353 -32.1 1.3 -9.2 1.2 11.32 0.60 10.84 0.60 10.67 0.60 10.61 0.60 10.58 0.60 9.65 0.03 9.28 0.03 9.19 0.02 0 + 351 129.56231 20.567775 -34.4 1.3 -14.1 1.3 11.45 0.60 11.09 0.60 10.99 0.60 10.98 0.60 10.97 0.60 10.11 0.02 9.80 0.03 9.72 0.02 1 + 352 129.56568 21.385649 -38.0 4.0 -16.4 4.0 16.19 0.00 14.87 0.00 14.14 0.00 13.79 0.00 13.61 0.00 12.46 0.02 11.82 0.03 11.63 0.02 0 + 353 129.57014 21.498238 -42.9 4.0 -8.3 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.52 0.03 12.94 0.04 12.72 0.03 0 + 354 129.57058 20.309780 -32.4 5.3 -15.2 5.3 19.46 0.02 18.36 0.01 16.63 0.60 15.80 0.00 15.44 0.00 14.10 0.03 13.53 0.04 13.24 0.03 1 + 355 129.58903 20.448088 -34.4 4.0 -12.8 4.0 15.96 0.00 14.78 0.00 99.00 99.00 13.66 0.00 13.48 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 356 129.58950 20.137364 -43.5 5.3 -18.4 5.3 20.21 0.02 18.86 0.01 17.13 0.01 16.21 0.00 15.82 0.01 14.52 0.03 13.80 0.03 13.54 0.03 0 + 357 129.59029 18.611097 -34.0 4.0 -8.4 4.0 13.83 0.00 12.93 0.60 12.50 0.60 12.25 0.60 12.08 0.00 10.96 0.02 10.32 0.03 10.18 0.02 0 + 358 129.59104 20.093251 -40.7 4.0 -9.7 4.0 20.48 0.02 19.17 0.02 17.41 0.01 16.62 0.00 16.21 0.01 14.88 0.03 14.30 0.04 13.89 0.04 0 + 359 129.59943 20.728031 -35.7 4.0 -11.4 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.08 0.02 11.43 0.03 11.30 0.02 0 + 360 129.60117 20.106041 -33.4 1.3 -14.4 1.3 10.84 0.60 10.53 0.60 10.41 0.60 10.36 0.60 10.35 0.60 9.46 0.02 9.24 0.03 9.18 0.02 1 + 361 129.60135 20.105960 -33.4 1.3 -14.4 1.3 13.09 0.00 99.00 99.00 99.00 99.00 15.39 0.01 11.70 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 362 129.60361 16.976683 -39.8 4.2 -10.7 4.2 17.57 0.01 16.36 0.00 15.18 0.00 14.64 0.00 14.36 0.00 13.22 0.02 12.60 0.02 12.35 0.02 0 + 363 129.60565 18.941670 -42.7 4.0 -15.2 4.0 17.20 0.00 15.96 0.00 14.71 0.00 14.11 0.00 13.82 0.00 12.64 0.03 11.96 0.03 11.73 0.02 0 + 364 129.61544 19.765459 -38.3 4.0 -11.4 4.0 16.77 0.00 15.55 0.00 14.50 0.00 14.07 0.00 13.82 0.00 12.63 0.02 11.97 0.03 11.77 0.02 0 + 365 129.61738 21.546100 -40.4 4.1 -15.8 4.1 13.99 0.00 13.09 0.60 12.63 0.60 12.34 0.60 12.05 0.00 11.00 0.02 10.34 0.02 10.20 0.01 0 + 366 129.62338 19.862507 -41.2 4.1 -7.7 4.1 14.55 0.00 13.60 0.60 13.19 0.60 12.86 0.00 12.74 0.00 11.67 0.02 11.06 0.02 10.93 0.02 0 + 367 129.62773 18.121733 -38.1 4.1 -8.6 4.1 16.70 0.00 15.42 0.00 14.25 0.60 13.70 0.00 13.45 0.00 12.15 0.02 11.50 0.02 11.28 0.02 1 + 368 129.62914 16.790207 -34.4 1.7 -8.6 1.7 12.01 0.60 11.59 0.60 11.44 0.60 11.38 0.60 11.36 0.60 10.45 0.02 10.10 0.02 10.00 0.02 0 + 369 129.63460 18.781326 -37.1 4.1 -13.1 4.1 17.81 0.00 16.62 0.00 15.40 0.00 14.84 0.00 14.58 0.00 13.43 0.02 12.83 0.02 12.53 0.02 0 + 370 129.63671 19.773754 -38.3 4.1 -12.0 4.1 16.88 0.00 15.69 0.00 14.70 0.00 14.30 0.00 14.04 0.00 12.88 0.02 12.25 0.02 12.01 0.02 0 + 371 129.63687 17.393894 -40.8 4.2 -14.9 4.2 20.07 0.02 18.77 0.01 17.15 0.01 16.43 0.00 16.09 0.01 14.79 0.03 14.13 0.04 14.02 0.05 0 + 372 129.64206 20.774774 -31.7 4.1 -11.4 4.1 19.35 0.02 18.10 0.01 16.56 0.00 15.88 0.00 15.56 0.00 14.28 0.02 13.69 0.03 13.36 0.03 0 + 373 129.64301 18.188072 -38.6 4.1 -8.5 4.1 16.26 0.00 15.01 0.00 14.16 0.00 14.00 0.00 13.63 0.00 12.49 0.02 11.82 0.02 11.63 0.02 0 + 374 129.64852 21.177267 -33.3 5.6 -19.5 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.16 0.04 14.65 0.06 14.28 0.07 1 + 375 129.65445 21.246874 -31.4 4.1 -13.5 4.1 19.51 0.02 18.34 0.01 16.83 0.00 16.03 0.00 15.69 0.00 14.45 0.03 13.82 0.03 13.54 0.04 0 + 376 129.65504 19.021113 -38.0 4.1 -11.8 4.1 14.28 0.00 13.39 0.60 12.94 0.60 12.66 0.60 12.49 0.00 11.35 0.02 10.76 0.02 10.61 0.01 0 + 377 129.65607 19.257972 -35.5 4.1 -19.1 4.1 18.12 0.01 16.81 0.00 15.43 0.00 14.80 0.00 14.51 0.00 13.32 0.02 12.59 0.02 12.40 0.02 0 + 378 129.65779 19.989754 -34.9 1.2 -13.2 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.70 0.01 7.66 0.03 7.61 0.01 0 CY Cnc + 379 129.66263 20.170768 -42.6 4.1 -14.7 4.1 17.90 0.01 16.67 0.00 15.31 0.00 14.69 0.00 14.38 0.00 13.16 0.02 12.48 0.02 12.28 0.02 0 + 380 129.66301 17.496805 -34.7 4.2 -17.8 4.2 20.35 0.02 19.01 0.01 17.35 0.01 16.59 0.00 16.20 0.00 14.94 0.03 14.32 0.04 13.96 0.05 0 + 381 129.66360 19.694452 -37.6 5.5 -12.1 5.5 20.46 0.04 19.10 0.01 17.28 0.01 16.46 0.00 16.03 0.00 14.70 0.03 14.10 0.04 13.87 0.05 0 + 382 129.66637 17.905825 -40.7 4.2 -15.5 4.2 20.00 0.02 18.07 0.60 17.38 0.02 16.39 0.00 16.04 0.00 14.81 0.04 14.27 0.04 13.94 0.05 0 + 383 129.67191 19.996413 -35.9 5.6 -21.7 5.6 20.98 0.04 19.75 0.02 17.80 0.01 16.96 0.00 16.53 0.01 15.23 0.04 14.61 0.06 14.27 0.06 0 + 384 129.67266 19.421699 -37.2 4.1 -16.4 4.1 15.20 0.00 14.00 0.00 13.67 0.60 13.29 0.00 13.07 0.00 12.00 0.02 11.34 0.02 11.19 0.02 0 + 385 129.67324 19.571678 -33.9 4.1 -21.1 4.1 20.16 0.02 18.86 0.01 17.22 0.00 16.48 0.00 16.11 0.01 14.83 0.03 14.23 0.04 13.96 0.05 0 + 386 129.67550 18.382074 -35.3 4.1 -19.2 4.1 19.52 0.02 18.19 0.01 16.52 0.00 15.74 0.00 15.35 0.01 14.05 0.03 13.48 0.03 13.27 0.04 0 + 387 129.68296 19.293956 -37.6 4.1 -16.5 4.1 17.47 0.00 16.27 0.00 14.93 0.00 14.33 0.00 14.19 0.00 12.83 0.02 12.19 0.02 11.96 0.02 0 + 388 129.68522 17.808142 -36.0 1.6 -15.8 1.6 12.18 0.00 11.90 0.00 11.34 0.00 11.92 0.00 11.36 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 389 129.69202 20.576737 -43.6 4.1 -17.3 4.1 12.97 0.60 12.34 0.60 12.06 0.60 11.91 0.60 11.84 0.60 10.82 0.02 10.40 0.02 10.31 0.02 0 + 390 129.69564 19.500927 -35.9 1.1 -13.3 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.35 0.02 8.30 0.04 8.22 0.02 0 HD 73397 + 391 129.69938 21.759250 -38.6 5.5 -11.2 5.5 19.90 0.02 18.63 0.01 16.95 0.00 16.25 0.00 15.91 0.01 14.41 0.03 13.86 0.04 13.53 0.04 0 + 392 129.69984 21.298427 -31.1 4.1 -17.8 4.1 18.51 0.01 17.34 0.00 15.96 0.00 15.18 0.00 14.89 0.00 13.63 0.02 13.04 0.02 12.75 0.02 1 + 393 129.70713 18.265840 -38.6 4.1 -8.5 4.1 13.04 0.60 12.41 0.60 12.15 0.60 12.00 0.60 11.94 0.60 10.92 0.01 10.49 0.02 10.40 0.02 0 + 394 129.70826 20.067606 -37.1 1.6 -13.2 1.6 10.82 0.60 10.55 0.60 10.50 0.60 10.52 0.60 10.53 0.60 9.70 0.02 9.43 0.02 9.37 0.02 0 + 395 129.71127 19.415017 -39.6 4.1 -15.9 4.1 17.65 0.00 16.41 0.00 15.18 0.00 14.63 0.00 14.39 0.00 13.19 0.02 12.56 0.02 12.33 0.02 1 + 396 129.71174 18.014558 -39.3 4.1 -10.8 4.1 18.59 0.01 17.35 0.00 15.96 0.00 15.35 0.00 15.07 0.00 13.93 0.02 13.28 0.02 12.98 0.03 1 + 397 129.71254 19.309278 -32.3 4.1 -18.5 4.1 19.53 0.02 18.02 0.01 16.36 0.00 15.62 0.00 15.41 0.01 13.91 0.02 13.37 0.03 13.04 0.03 0 + 398 129.71254 19.850549 -42.0 4.1 -12.5 4.1 18.06 0.01 16.79 0.00 15.39 0.00 14.77 0.00 14.45 0.00 13.22 0.02 12.63 0.03 12.36 0.03 0 + 399 129.72298 19.571379 -37.1 4.1 -15.9 4.1 14.73 0.00 13.71 0.60 13.29 0.60 13.07 0.00 12.82 0.00 11.73 0.02 11.10 0.02 10.97 0.02 0 + 400 129.72574 19.862364 -30.6 4.2 -18.1 4.2 20.74 0.03 19.42 0.02 17.64 0.01 16.84 0.00 16.45 0.01 15.18 0.04 14.59 0.05 14.35 0.06 1 + 401 129.72821 20.788027 -27.9 5.5 -13.9 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.97 0.04 14.31 0.05 14.06 0.06 0 + 402 129.72988 20.219160 -44.6 4.1 -16.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.45 0.02 12.84 0.03 12.55 0.02 0 + 403 129.73020 19.283794 -38.5 4.1 -16.3 4.1 16.55 0.00 15.32 0.00 14.38 0.00 13.96 0.00 13.76 0.00 12.63 0.02 11.93 0.02 11.74 0.01 0 + 404 129.73061 18.366547 -33.6 4.1 -13.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.68 0.02 12.07 0.02 11.94 0.02 0 + 405 129.73105 19.842572 -38.1 4.1 -10.8 4.1 19.63 0.02 18.23 0.01 16.70 0.00 15.99 0.00 15.64 0.00 14.40 0.03 13.76 0.04 13.58 0.04 0 + 406 129.73182 17.252653 -37.8 4.2 -14.4 4.2 18.18 0.01 16.95 0.00 15.98 0.60 15.09 0.00 14.70 0.00 13.65 0.02 13.08 0.03 12.71 0.02 0 + 407 129.73572 20.857691 -29.0 4.1 -16.6 4.1 19.51 0.01 18.23 0.01 16.51 0.00 15.71 0.00 15.34 0.00 14.02 0.02 13.45 0.03 13.12 0.03 1 + 408 129.73713 18.858109 -39.3 4.1 -17.2 4.1 17.70 0.00 16.49 0.00 15.13 0.00 14.53 0.00 14.22 0.00 13.03 0.02 12.43 0.02 12.16 0.02 0 + 409 129.73836 20.181554 -44.1 4.1 -16.2 4.1 14.47 0.00 13.46 0.60 13.04 0.60 12.78 0.60 12.57 0.00 11.48 0.02 10.86 0.02 10.72 0.02 0 + 410 129.74056 18.775224 -32.6 4.1 -12.5 4.1 18.50 0.01 17.24 0.00 15.81 0.00 15.10 0.00 14.78 0.00 13.54 0.02 12.92 0.03 12.71 0.02 0 + 411 129.75760 17.855749 -35.3 4.2 -15.9 4.2 16.55 0.00 15.35 0.00 14.38 0.00 13.96 0.00 13.76 0.00 12.66 0.02 11.98 0.02 11.77 0.02 0 + 412 129.75945 19.326242 -38.9 4.1 -15.4 4.1 12.84 0.60 12.26 0.60 11.97 0.60 11.80 0.60 11.74 0.60 10.73 0.02 10.37 0.02 10.26 0.02 1 + 413 129.76182 19.724714 -34.8 1.1 -13.6 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.37 0.02 8.13 0.01 8.12 0.01 0 BD+20 2140 + 414 129.76206 19.532544 -37.5 4.1 -16.9 4.1 17.62 0.00 16.35 0.00 15.10 0.60 14.36 0.00 14.06 0.00 12.83 0.02 12.18 0.02 11.96 0.02 1 + 415 129.76278 19.404308 -38.9 4.1 -16.2 4.1 19.37 0.02 18.12 0.01 16.55 0.00 15.84 0.00 15.49 0.00 14.26 0.02 13.64 0.03 13.41 0.03 0 + 416 129.76331 20.043778 -44.3 4.1 -13.7 4.1 14.97 0.00 13.83 0.00 13.49 0.60 13.07 0.00 12.93 0.00 11.87 0.02 11.21 0.02 11.05 0.02 0 + 417 129.76497 19.999762 -31.5 1.1 -12.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.86 0.01 7.82 0.01 7.77 0.01 0 HD 73430 + 418 129.76634 20.567277 -38.0 4.1 -11.3 4.1 17.69 0.01 16.50 0.00 15.34 0.60 14.62 0.00 14.34 0.00 13.11 0.02 12.46 0.02 12.28 0.02 0 + 419 129.76706 19.522671 -37.4 4.1 -12.3 4.1 14.27 0.00 13.48 0.60 13.08 0.60 12.83 0.60 12.60 0.00 11.56 0.02 10.99 0.02 10.86 0.01 0 + 420 129.77123 19.757346 -36.1 4.1 -15.6 4.1 19.06 0.02 17.81 0.01 16.40 0.60 15.62 0.00 15.29 0.00 14.01 0.02 13.42 0.03 13.16 0.03 0 + 421 129.77183 20.117176 -35.1 1.1 -14.3 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.60 0.03 8.45 0.02 8.41 0.02 1 HD 73429 + 422 129.77551 19.676794 -33.7 1.2 -13.9 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.86 0.02 6.77 0.02 6.71 0.01 0 HD 73449 + 423 129.77858 20.348396 -39.1 4.1 -18.2 4.1 16.79 0.00 15.56 0.00 14.57 0.00 14.13 0.00 13.93 0.00 12.80 0.02 12.14 0.02 11.91 0.02 0 + 424 129.78791 19.592422 -34.8 1.1 -14.1 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.01 0.01 7.95 0.02 7.88 0.02 0 BS Cnc + 425 129.79099 19.783030 -39.8 4.1 -13.9 4.1 16.65 0.00 15.42 0.00 14.18 0.00 13.70 0.00 13.44 0.00 12.18 0.02 11.54 0.02 11.35 0.02 0 + 426 129.79219 19.678467 -36.3 1.3 -12.4 1.3 9.62 0.60 9.48 0.60 9.45 0.60 9.44 0.60 9.43 0.60 8.66 0.02 8.50 0.02 8.41 0.01 1 BD+20 2143B + 427 129.79225 20.408369 -37.8 4.1 -13.6 4.1 14.90 0.00 13.74 0.00 13.48 0.60 13.16 0.00 12.86 0.00 11.78 0.02 11.17 0.02 11.00 0.02 0 + 428 129.79567 18.175972 -38.7 1.2 -13.2 1.1 10.52 0.60 10.25 0.60 10.16 0.60 10.13 0.60 10.12 0.60 9.31 0.03 9.07 0.02 8.99 0.02 1 + 429 129.80073 19.115601 -35.8 1.5 -15.1 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.56 0.01 9.31 0.02 9.26 0.02 0 + 430 129.80229 21.599173 -43.9 4.1 -9.9 4.1 20.12 0.02 18.88 0.01 17.25 0.00 16.50 0.00 16.15 0.01 14.82 0.03 14.29 0.04 14.00 0.05 0 + 431 129.81046 20.021976 -38.0 4.1 -14.3 4.1 16.54 0.00 15.31 0.00 14.39 0.00 13.97 0.00 13.75 0.00 12.60 0.02 11.94 0.02 11.72 0.02 0 + 432 129.81242 20.210755 -35.8 1.5 -14.1 1.6 11.24 0.60 10.94 0.60 10.87 0.60 10.87 0.60 10.88 0.60 10.04 0.02 9.77 0.02 9.65 0.02 0 + 433 129.81282 19.725436 -33.4 4.1 -12.8 4.1 17.63 0.01 16.45 0.00 15.04 0.60 14.45 0.00 14.18 0.00 12.88 0.02 12.26 0.02 12.02 0.02 1 + 434 129.81353 19.286503 -36.2 4.1 -16.3 4.1 17.23 0.00 15.70 0.60 15.11 0.01 14.42 0.00 14.18 0.00 13.04 0.02 12.39 0.02 12.21 0.02 0 + 435 129.81399 19.324551 -35.7 5.5 -18.2 5.5 20.29 0.03 18.98 0.02 17.30 0.01 16.54 0.00 16.15 0.00 14.86 0.03 14.18 0.04 13.99 0.05 0 + 436 129.81545 19.334006 -36.5 5.5 -14.8 5.5 20.68 0.04 19.16 0.02 17.52 0.01 16.73 0.00 16.34 0.01 15.00 0.04 14.46 0.05 14.18 0.06 0 + 437 129.81584 20.070585 -38.9 4.1 -13.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.74 0.02 11.05 0.02 10.87 0.02 1 + 438 129.81930 17.462899 -33.4 1.6 -13.3 1.6 12.23 0.60 11.72 0.60 11.51 0.60 11.40 0.60 11.36 0.60 10.40 0.02 10.03 0.02 9.97 0.02 0 + 439 129.81988 19.795154 -40.7 4.1 -12.4 4.1 17.11 0.00 15.87 0.00 14.74 0.00 14.35 0.00 14.14 0.00 12.97 0.02 12.34 0.02 12.08 0.02 0 + 440 129.82515 20.739257 -36.9 4.1 -8.3 4.1 16.98 0.00 15.77 0.00 14.74 0.00 14.28 0.00 14.07 0.00 12.91 0.02 12.24 0.02 12.06 0.02 1 + 441 129.82699 19.378917 -40.7 5.5 -12.0 5.5 20.64 0.03 19.27 0.02 17.54 0.01 16.73 0.00 16.32 0.01 15.01 0.04 14.44 0.05 14.01 0.05 1 + 442 129.82865 18.810121 -38.6 4.1 -18.2 4.1 18.47 0.01 17.24 0.00 15.85 0.00 15.19 0.00 14.88 0.00 13.69 0.02 13.03 0.02 12.79 0.03 0 + 443 129.83162 20.291820 -28.6 4.1 -13.4 4.1 19.21 0.02 17.92 0.01 16.37 0.00 15.68 0.00 15.35 0.00 14.07 0.02 13.49 0.03 13.16 0.03 0 + 444 129.83469 19.517481 -30.0 4.1 -17.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.22 0.02 12.58 0.02 12.37 0.02 1 + 445 129.83504 16.900545 -38.2 4.2 -6.6 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.78 0.04 14.11 0.04 13.86 0.05 0 + 446 129.83776 18.436688 -34.4 5.5 -17.5 5.5 20.33 0.02 18.99 0.01 17.29 0.00 16.43 0.00 16.03 0.00 14.68 0.03 14.13 0.03 13.85 0.04 0 + 447 129.83871 22.089042 -40.2 4.1 -11.2 4.1 18.72 0.01 17.51 0.00 16.08 0.00 15.45 0.00 15.16 0.00 13.94 0.02 13.33 0.03 13.06 0.03 0 + 448 129.83971 20.758124 -35.1 1.6 -14.8 1.6 12.06 0.60 11.61 0.60 11.46 0.60 11.41 0.60 11.39 0.60 10.48 0.02 10.10 0.02 10.03 0.02 1 + 449 129.84090 19.861178 -37.8 4.1 -12.9 4.1 12.81 0.60 12.24 0.60 12.02 0.60 11.90 0.60 11.85 0.60 10.87 0.02 10.45 0.02 10.37 0.02 0 + 450 129.84223 20.799537 -31.9 4.1 -15.6 4.1 19.58 0.02 18.28 0.01 16.74 0.60 15.86 0.00 15.49 0.01 14.22 0.03 13.64 0.02 13.36 0.04 0 + 451 129.84346 20.081881 -38.0 4.1 -10.8 4.1 17.70 0.00 16.44 0.00 15.45 0.60 14.81 0.00 14.56 0.00 13.36 0.02 12.75 0.02 12.51 0.02 1 + 452 129.84772 18.666450 -35.8 4.1 -9.3 4.1 16.74 0.00 15.48 0.00 14.53 0.00 14.10 0.00 13.91 0.00 12.72 0.03 12.08 0.03 11.86 0.02 0 + 453 129.85409 19.459361 -35.3 1.6 -13.9 1.6 10.92 0.60 10.53 0.60 10.37 0.60 10.29 0.60 10.27 0.60 9.36 0.02 9.09 0.02 9.00 0.01 0 BD+19 2061 + 454 129.86052 20.862381 -35.3 5.6 -10.1 5.6 20.51 0.05 19.19 0.01 17.38 0.01 16.70 0.01 16.31 0.01 15.06 0.04 14.54 0.05 14.14 0.06 1 + 455 129.86104 20.431169 -38.1 4.1 -10.1 4.1 18.28 0.01 17.06 0.00 15.63 0.00 15.02 0.00 14.68 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 456 129.86107 20.431175 -38.1 4.1 -10.1 4.1 18.31 0.01 16.73 0.60 15.82 0.60 14.98 0.00 14.67 0.00 13.46 0.02 12.91 0.02 12.66 0.02 0 + 457 129.86355 20.733051 -30.6 5.6 -10.6 5.6 21.53 0.08 20.07 0.03 18.10 0.01 17.20 0.00 16.73 0.01 15.32 0.04 14.79 0.05 14.51 0.08 1 + 458 129.86431 18.248767 -37.6 4.1 -6.7 4.1 19.28 0.02 18.05 0.01 16.48 0.00 15.76 0.00 15.43 0.00 14.18 0.03 13.59 0.04 13.25 0.03 1 + 459 129.86898 19.473595 -32.9 1.6 -11.5 1.6 12.09 0.60 11.49 0.60 11.23 0.60 11.09 0.60 11.03 0.60 10.03 0.02 9.63 0.02 9.53 0.02 0 + 460 129.87239 19.786582 -39.2 4.1 -11.1 4.1 13.18 0.60 12.41 0.60 12.09 0.60 11.91 0.60 11.80 0.60 10.74 0.02 10.22 0.02 10.06 0.01 0 + 461 129.87678 20.069092 -35.3 1.6 -12.9 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.14 0.04 8.84 0.03 8.81 0.01 1 BD+20 2146 + 462 129.87764 17.980894 -37.7 4.2 -13.5 4.2 19.54 0.02 18.20 0.01 16.69 0.00 15.99 0.00 15.66 0.00 14.43 0.03 13.74 0.03 13.51 0.04 0 + 463 129.87885 19.967164 -39.7 4.1 -16.5 4.1 18.84 0.01 17.53 0.00 15.96 0.00 15.24 0.00 14.85 0.00 13.55 0.02 12.92 0.02 12.69 0.02 1 + 464 129.88221 19.404843 -34.4 4.1 -10.2 4.1 18.41 0.01 17.14 0.00 15.77 0.00 15.12 0.00 14.82 0.00 13.57 0.02 12.99 0.02 12.73 0.03 0 + 465 129.88338 20.655642 -34.4 2.2 -12.1 2.2 12.64 0.60 11.95 0.60 11.65 0.60 11.47 0.60 11.39 0.60 10.36 0.02 9.90 0.02 9.77 0.01 0 + 466 129.88504 21.047928 -33.7 5.5 -14.0 5.5 19.71 0.01 18.51 0.01 16.89 0.00 16.15 0.00 15.81 0.01 14.61 0.03 13.91 0.04 13.63 0.04 0 + 467 129.89346 20.955739 -36.5 4.1 -10.7 4.1 14.00 0.00 13.37 0.60 12.95 0.60 12.70 0.60 12.49 0.00 11.44 0.02 10.89 0.02 10.75 0.02 0 + 468 129.89797 18.876825 -38.1 1.5 -12.6 1.6 10.92 0.60 10.61 0.60 10.52 0.60 10.51 0.60 10.52 0.60 9.66 0.02 9.38 0.02 9.33 0.02 1 + 469 129.89875 21.739249 -34.9 5.6 -14.1 5.6 21.35 0.06 20.01 0.03 17.79 0.60 17.21 0.01 16.81 0.01 15.53 0.05 14.83 0.06 14.66 0.10 0 + 470 129.90055 18.680263 -33.0 4.1 -11.7 4.1 20.57 0.02 19.18 0.01 17.46 0.00 16.61 0.00 16.18 0.00 14.89 0.04 14.37 0.05 13.89 0.05 0 + 471 129.90180 19.485484 -37.7 4.1 -12.4 4.1 15.73 0.00 14.54 0.00 13.82 0.00 13.66 0.00 13.37 0.00 12.25 0.02 11.60 0.02 11.39 0.02 1 + 472 129.90182 19.260517 -39.5 4.1 -12.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.81 0.02 11.18 0.02 11.01 0.02 0 + 473 129.90423 17.788836 -37.1 4.2 -10.6 4.2 16.13 0.00 14.91 0.00 14.12 0.00 13.75 0.00 13.58 0.00 12.48 0.02 11.76 0.02 11.60 0.02 1 + 474 129.90471 19.816105 -35.3 4.1 -10.6 4.1 15.85 0.00 14.62 0.00 13.86 0.00 13.56 0.00 13.40 0.00 12.29 0.02 11.62 0.02 11.39 0.01 0 + 475 129.90622 18.170371 -36.5 2.2 -15.1 2.2 12.30 0.60 11.84 0.60 11.70 0.60 11.67 0.60 11.64 0.60 10.76 0.02 10.32 0.02 10.24 0.02 0 + 476 129.90969 19.440906 -32.6 2.2 -10.3 2.2 13.04 0.60 12.38 0.60 12.10 0.60 11.94 0.60 11.86 0.60 10.84 0.02 10.39 0.02 10.29 0.02 0 + 477 129.91687 18.846989 -37.5 5.5 -15.2 5.5 20.44 0.03 19.12 0.02 17.44 0.01 16.63 0.00 16.27 0.01 14.97 0.04 14.34 0.04 14.02 0.06 0 + 478 129.91868 19.314940 -39.3 4.1 -9.5 4.1 19.10 0.01 17.88 0.01 16.35 0.00 15.65 0.00 15.31 0.00 14.08 0.03 13.47 0.02 13.23 0.03 0 + 479 129.91904 18.616286 -36.1 4.1 -12.4 4.1 19.19 0.01 17.96 0.00 16.33 0.00 15.55 0.00 15.20 0.00 13.95 0.02 13.32 0.03 13.00 0.03 0 + 480 129.92089 19.991330 -36.3 4.1 -10.9 4.1 15.95 0.00 14.75 0.00 13.98 0.00 13.55 0.00 13.33 0.00 12.18 0.02 11.50 0.02 11.32 0.02 0 + 481 129.92386 20.028191 -37.4 4.1 -12.1 4.1 16.37 0.00 15.14 0.00 14.30 0.00 13.93 0.00 13.74 0.00 12.60 0.02 11.94 0.02 11.76 0.02 0 + 482 129.92504 20.295849 -42.0 4.1 -14.5 4.1 19.19 0.01 17.89 0.00 16.29 0.00 15.59 0.00 15.24 0.00 14.03 0.02 13.46 0.03 13.14 0.03 0 + 483 129.92721 19.307965 -35.3 4.1 -19.6 4.1 18.68 0.01 17.53 0.00 15.91 0.00 15.20 0.00 14.86 0.00 13.60 0.02 13.01 0.02 12.70 0.02 0 + 484 129.92774 19.778486 -36.4 0.6 -12.4 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.11 0.02 5.99 0.01 6.00 0.01 0 BT Cnc + 485 129.92833 20.086218 -32.3 1.3 -12.7 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.25 0.01 7.18 0.01 7.16 0.01 0 HD 73574 + 486 129.93161 20.494304 -37.0 4.1 -12.6 4.1 18.40 0.01 17.13 0.00 15.77 0.00 15.15 0.00 14.86 0.00 13.62 0.02 12.98 0.02 12.75 0.02 1 + 487 129.93612 19.275230 -33.8 1.3 -14.0 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.23 0.01 7.22 0.01 7.09 10.00 0 BU Cnc + 488 129.93830 20.124289 -36.9 4.1 -13.5 4.1 18.19 0.01 16.96 0.00 15.43 0.00 14.75 0.00 14.41 0.00 13.14 0.02 12.50 0.02 12.30 0.02 1 + 489 129.94043 18.976122 -41.9 4.1 -12.9 4.1 18.06 0.01 16.85 0.00 15.71 0.60 14.91 0.00 14.64 0.00 13.46 0.02 12.80 0.02 12.56 0.02 0 + 490 129.94064 19.366995 -37.0 1.2 -14.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.56 0.02 9.36 0.02 9.26 0.02 0 + 491 129.94404 19.217806 -38.9 4.1 -11.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.35 0.02 11.68 0.02 11.51 0.02 0 + 492 129.94469 19.736814 -35.0 4.1 -9.3 4.1 19.77 0.02 18.43 0.01 16.89 0.60 16.12 0.00 15.78 0.00 14.52 0.03 13.94 0.04 13.64 0.05 1 + 493 129.94602 19.827628 -39.7 4.1 -10.2 4.1 13.20 0.60 12.54 0.60 12.26 0.60 12.10 0.60 12.06 0.00 11.01 0.02 10.56 0.02 10.44 0.02 0 + 494 129.94699 19.659530 -36.2 4.1 -14.5 4.1 18.93 0.01 17.64 0.01 16.14 0.60 15.38 0.00 15.05 0.00 13.78 0.02 13.20 0.03 12.90 0.03 0 + 495 129.95669 18.347410 -34.3 1.1 -14.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.44 0.02 8.29 0.02 8.28 0.02 0 HD 73620 + 496 129.96135 19.540823 -31.8 1.3 -12.9 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.18 0.24 4.63 0.19 4.39 0.04 0 HD 73598 + 497 129.96161 19.550557 -35.4 2.1 -14.2 2.1 12.75 0.00 99.00 99.00 99.00 99.00 12.85 0.00 11.52 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 498 129.96195 19.550633 -35.4 2.1 -14.2 2.1 12.42 0.60 11.89 0.60 11.63 0.60 11.47 0.60 11.41 0.60 10.42 0.02 10.09 0.02 10.00 0.02 0 + 499 129.96361 20.580526 -42.1 4.1 -12.1 4.1 16.65 0.00 15.43 0.00 14.44 0.00 13.97 0.00 13.72 0.00 12.49 0.02 11.78 0.02 11.59 0.02 0 DO Cnc + 500 129.96796 19.312554 -34.3 1.6 -15.2 1.6 10.60 0.60 10.33 0.60 10.23 0.60 10.17 0.60 10.13 0.60 9.28 0.02 9.09 0.02 9.01 0.02 0 + 501 129.96926 20.512866 -30.6 4.1 -12.0 4.1 18.06 0.01 16.82 0.00 15.69 0.60 14.88 0.00 14.59 0.00 13.37 0.02 12.73 0.02 12.52 0.02 1 + 502 129.96974 20.018088 -37.0 4.1 -15.4 4.1 18.27 0.01 17.00 0.00 15.65 0.00 15.06 0.00 14.77 0.00 13.57 0.02 12.90 0.03 12.66 0.03 0 + 503 129.97139 19.401014 -34.8 4.1 -5.6 4.1 18.69 0.01 17.41 0.00 16.04 0.00 15.39 0.00 15.11 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 504 129.97636 20.560240 -34.5 1.1 -13.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.54 0.01 8.40 0.01 8.38 0.02 1 HD 73597 + 505 129.97659 19.460309 -36.1 4.1 -18.1 4.1 19.31 0.01 18.04 0.01 16.51 0.00 15.83 0.00 15.50 0.00 14.24 0.02 13.61 0.04 13.36 0.04 0 + 506 129.97945 20.065042 -38.4 1.6 -12.7 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.20 0.02 8.99 0.02 8.96 0.02 0 BD+20 2151 + 507 129.98032 18.150421 -43.1 4.1 -17.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.83 0.03 14.14 0.04 13.96 0.05 0 + 508 129.98540 19.552986 -36.0 1.3 -16.4 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.91 0.01 6.86 0.02 6.79 0.01 0 HD 73618 + 509 129.99075 19.541483 -32.7 1.3 -15.1 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.07 0.01 7.03 0.00 7.01 0.02 0 HD 73619 + 510 129.99182 19.201538 -38.5 1.1 -13.6 1.2 9.47 0.60 9.34 0.60 9.36 0.60 9.43 0.60 9.47 0.60 8.69 0.01 8.51 0.01 8.48 0.02 0 HD 73641 + 511 129.99328 20.158283 -35.9 1.1 -13.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.27 0.01 8.22 0.01 8.10 0.01 0 HD 73616 + 512 129.99619 20.031467 -34.4 1.1 -16.9 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.45 0.03 8.25 0.01 8.21 0.02 1 + 513 129.99925 19.566718 -33.8 2.2 -12.1 2.2 12.29 0.60 11.61 0.60 11.34 0.60 11.21 0.60 11.12 0.60 10.11 0.02 9.63 0.02 9.48 0.02 0 + 514 129.99990 19.577891 -39.9 4.1 -8.2 4.1 13.14 0.60 12.52 0.60 12.30 0.60 12.19 0.60 12.10 0.00 11.13 0.01 10.65 0.02 10.55 0.02 0 + 515 130.00027 17.368334 -38.4 5.6 -14.9 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.01 0.03 14.44 0.04 14.06 0.04 1 + 516 130.00176 19.403193 -32.5 4.1 -13.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.99 0.04 14.23 0.04 14.03 0.05 0 + 517 130.00279 19.806551 -37.0 1.2 -12.8 1.2 10.41 0.60 10.20 0.60 10.15 0.60 10.15 0.60 10.15 0.60 9.33 0.02 9.15 0.02 9.08 0.02 1 + 518 130.00285 19.309625 -40.8 4.1 -14.8 4.1 15.00 0.00 13.83 0.00 13.41 0.60 12.99 0.00 12.81 0.00 11.74 0.02 11.10 0.02 10.92 0.02 0 + 519 130.00542 20.135612 -35.7 1.2 -14.3 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.85 0.03 8.69 0.02 8.62 0.01 1 HD 73640 + 520 130.00713 18.999870 -38.9 1.2 -12.0 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.05 0.01 8.77 0.02 8.70 0.02 0 TX Cnc + 521 130.00919 18.949157 -36.7 4.1 -11.9 4.1 16.25 0.00 15.01 0.00 13.99 0.00 13.44 0.00 13.19 0.00 12.00 0.01 11.38 0.01 11.16 0.02 0 + 522 130.01028 19.277691 -41.7 4.1 -17.5 4.1 18.86 0.01 17.66 0.01 16.21 0.00 15.57 0.00 15.28 0.00 14.02 0.03 13.40 0.03 13.23 0.04 0 + 523 130.01029 19.676472 -39.2 4.1 -16.1 4.1 18.44 0.01 17.22 0.00 15.94 0.60 15.20 0.00 14.91 0.00 13.71 0.02 13.12 0.02 12.85 0.02 0 + 524 130.01518 19.916535 -35.1 4.1 -16.2 4.1 18.24 0.01 17.04 0.00 15.54 0.00 14.83 0.00 14.50 0.00 13.23 0.02 12.60 0.02 12.35 0.02 0 + 525 130.01621 18.932585 -43.3 5.5 -13.4 5.5 20.72 0.03 19.39 0.02 17.64 0.01 16.82 0.01 16.43 0.01 15.09 0.04 14.49 0.05 14.17 0.06 1 + 526 130.01724 19.784408 -33.0 1.6 -14.1 1.6 12.50 0.60 11.94 0.60 11.68 0.60 11.54 0.60 11.49 0.60 10.51 0.02 10.12 0.02 10.00 0.02 0 + 527 130.01735 19.413961 -37.9 4.1 -14.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.84 0.02 13.21 0.02 12.97 0.03 0 + 528 130.02036 19.729239 -35.4 1.3 -10.9 1.3 10.04 0.60 9.80 0.60 9.73 0.60 9.73 0.60 9.75 0.60 8.91 0.01 8.69 0.02 8.65 0.02 1 BD+20 2157 + 529 130.02372 19.025199 -39.4 4.1 -11.2 4.1 13.10 0.00 12.44 0.00 12.58 0.00 12.99 0.00 11.74 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 530 130.02640 19.307332 -30.8 1.6 -14.0 1.6 11.48 0.60 10.98 0.60 10.78 0.60 10.69 0.60 10.65 0.60 9.70 0.02 9.32 0.02 9.23 0.02 0 + 531 130.02681 20.007801 -36.2 0.7 -11.5 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 4.69 0.03 4.47 0.19 4.20 0.02 0 39 CnC + 532 130.02704 21.264795 -34.3 4.1 -11.5 4.1 18.76 0.01 17.46 0.00 15.98 0.00 15.26 0.00 14.94 0.00 13.66 0.02 13.04 0.02 12.77 0.02 0 + 533 130.03209 21.062697 -37.9 0.9 -14.6 0.7 9.44 0.60 9.30 0.60 9.29 0.60 9.32 0.60 9.33 0.60 8.54 0.01 8.39 0.01 8.35 0.02 0 HD 73639 + 534 130.04025 19.621394 -34.0 2.2 -11.3 2.2 12.31 0.60 11.85 0.60 11.64 0.60 11.53 0.60 11.50 0.60 10.56 0.01 10.24 0.02 10.13 0.02 0 + 535 130.04091 18.097237 -36.3 4.1 -7.0 4.1 13.91 0.00 12.98 0.00 12.63 0.00 12.59 0.00 12.55 0.00 11.37 0.02 10.79 0.02 10.68 0.02 0 + 536 130.04169 20.418936 -36.3 4.1 -12.8 4.1 16.17 0.00 14.97 0.00 14.02 0.00 13.54 0.00 13.32 0.00 12.17 0.02 11.50 0.02 11.31 0.02 0 + 537 130.04511 18.982490 -37.9 4.1 -12.2 4.1 17.69 0.00 16.47 0.00 15.05 0.00 14.41 0.00 14.11 0.00 12.87 0.02 12.21 0.02 12.01 0.02 0 + 538 130.04773 19.971149 -31.2 1.5 -12.4 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.50 0.01 6.55 0.01 6.53 0.02 0 40 Cnc + 539 130.04820 19.653249 -38.4 4.1 -13.0 4.1 17.90 0.01 16.68 0.00 15.22 0.60 14.50 0.00 14.18 0.00 12.94 0.02 12.33 0.02 12.07 0.02 0 + 540 130.05124 19.639503 -34.8 1.3 -13.2 1.3 10.03 0.60 9.80 0.60 9.74 0.60 9.74 0.60 9.75 0.60 8.93 0.01 8.71 0.01 8.67 0.02 0 BD+20 2160 + 541 130.05449 20.057809 -40.3 4.1 -11.2 4.1 18.03 0.01 16.80 0.00 15.31 0.00 14.68 0.00 14.37 0.00 13.12 0.02 12.51 0.02 12.25 0.02 0 + 542 130.05597 19.778789 -38.0 4.1 -11.9 4.1 13.49 0.60 12.79 0.60 12.53 0.60 12.40 0.60 12.27 0.00 11.28 0.02 10.74 0.02 10.64 0.02 0 + 543 130.05736 19.748873 -38.7 4.1 -13.6 4.1 16.70 0.00 15.49 0.00 14.60 0.60 14.07 0.00 13.85 0.00 12.75 0.02 12.06 0.02 11.88 0.02 0 + 544 130.06325 20.087206 -38.6 4.1 -9.0 4.1 18.86 0.01 17.63 0.00 15.99 0.00 15.25 0.00 14.86 0.00 13.57 0.02 12.97 0.02 12.69 0.02 1 + 545 130.06396 19.994301 -34.3 1.1 -12.2 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.19 0.01 8.14 0.05 8.04 9.99 0 BD+20 2161 + 546 130.06451 19.458570 -40.7 4.1 -12.8 4.1 14.43 0.00 13.40 0.60 12.98 0.60 12.72 0.60 12.54 0.00 11.43 0.02 10.85 0.02 10.69 0.02 0 + 547 130.06536 19.915052 -39.6 4.1 -11.8 4.1 13.13 0.60 12.36 0.60 12.03 0.60 11.85 0.60 11.75 0.60 10.68 0.02 10.15 0.02 10.01 0.02 0 + 548 130.07033 20.711699 -41.1 5.5 -10.0 5.5 18.71 0.01 17.54 0.00 16.02 0.00 15.36 0.00 15.07 0.00 13.86 0.02 13.25 0.02 12.97 0.03 0 + 549 130.07053 20.700153 -37.3 5.6 -16.2 5.6 20.57 0.03 19.39 0.02 17.63 0.01 16.82 0.01 16.43 0.01 15.14 0.04 14.55 0.06 14.26 0.07 1 + 550 130.07101 18.608258 -36.6 4.1 -13.0 4.1 19.36 0.01 18.07 0.00 16.55 0.00 15.81 0.00 15.48 0.00 14.24 0.03 13.65 0.04 13.35 0.04 0 + 551 130.07344 19.787561 -35.3 1.3 -13.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.89 0.01 8.62 10.00 8.58 0.02 1 BD+20 2162 + 552 130.07544 19.532022 -34.0 1.3 -11.7 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.21 0.01 7.20 10.00 7.16 0.01 0 HD 73711 + 553 130.07821 17.550699 -28.4 4.2 -16.3 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.60 0.03 13.92 0.03 13.69 0.04 0 + 554 130.07875 20.191855 -43.4 4.1 -13.7 4.1 13.35 0.60 12.52 0.60 12.16 0.60 11.94 0.60 11.82 0.60 10.73 0.02 10.17 0.02 10.04 0.01 0 + 555 130.07947 18.361925 -34.0 4.1 -15.1 4.1 17.79 0.01 16.56 0.00 15.38 0.00 14.83 0.00 14.58 0.00 13.39 0.02 12.77 0.02 12.56 0.02 1 + 556 130.08003 18.211390 -38.7 4.2 -19.3 4.2 20.95 0.04 19.58 0.02 17.85 0.01 17.07 0.00 16.67 0.01 15.44 0.05 14.77 0.06 14.79 0.10 1 + 557 130.08251 18.927268 -38.9 4.1 -7.4 4.1 16.84 0.00 15.59 0.00 14.43 0.00 13.87 0.00 13.62 0.00 12.42 0.02 11.76 0.02 11.54 0.02 0 + 558 130.08391 19.349001 -35.8 1.4 -12.9 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.17 0.01 6.09 10.00 6.05 0.01 0 HD 73712 + 559 130.08647 19.686691 -36.4 1.2 -12.6 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.33 0.01 7.29 0.01 7.28 0.02 0 HD 73709 + 560 130.08883 19.181564 -39.2 4.1 -13.2 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.48 0.02 10.76 0.02 10.76 0.03 0 + 561 130.09206 19.669907 -34.7 1.2 -14.1 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 4.91 0.21 4.32 0.21 4.18 0.03 0 HR 3428 + 562 130.09229 18.123537 -36.9 4.1 -8.6 4.1 16.07 0.00 14.82 0.00 14.03 0.00 13.70 0.00 13.52 0.00 12.38 0.02 11.71 0.02 11.52 0.02 0 + 563 130.09295 20.106789 -36.6 1.4 -12.9 1.5 10.14 0.60 9.92 0.60 9.89 0.60 9.92 0.60 9.95 0.60 9.14 0.02 8.92 0.03 8.85 0.01 0 BD+20 2164 + 564 130.09328 20.640851 -43.2 4.1 -14.6 4.1 15.47 0.00 14.25 0.00 13.61 0.00 13.90 0.00 13.19 0.00 12.09 0.02 11.43 0.02 11.22 0.01 0 + 565 130.09459 19.464775 -33.7 1.6 -11.7 1.6 11.08 0.60 10.74 0.60 10.61 0.60 10.55 0.60 10.52 0.60 9.64 0.02 9.40 0.02 9.34 0.02 0 + 566 130.09696 19.673239 -36.2 1.5 -14.5 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.37 0.03 9.08 0.03 9.01 0.02 1 + 567 130.09780 19.834986 -33.8 1.2 -13.1 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.64 0.01 7.67 0.02 7.59 0.01 0 HD 73730 + 568 130.10145 21.231138 -37.1 4.1 -14.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.76 0.03 14.05 0.04 13.89 0.06 0 + 569 130.10163 18.453835 -34.3 2.1 -11.2 2.2 12.16 0.60 11.56 0.60 11.30 0.60 11.16 0.60 11.10 0.60 10.10 0.02 9.70 0.02 9.60 0.02 0 + 570 130.10664 19.475762 -33.2 1.6 -12.0 1.6 9.98 0.60 9.78 0.60 9.76 0.60 9.79 0.60 9.82 0.60 9.00 0.03 8.78 0.02 8.76 0.02 0 + 571 130.10895 19.686443 -35.8 1.0 -15.3 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.56 0.01 8.46 0.04 8.36 0.02 0 BD+20 2170 + 572 130.11061 20.253657 -40.1 4.1 -14.7 4.1 17.26 0.00 16.06 0.00 14.97 0.00 14.45 0.00 14.21 0.00 13.06 0.02 12.45 0.03 12.19 0.02 0 + 573 130.11112 17.350071 -33.3 4.2 -10.6 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.04 0.02 13.39 0.03 13.19 0.03 0 + 574 130.11148 20.182007 -32.2 1.2 -12.5 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.56 0.01 7.43 0.02 7.43 0.01 0 BQ Cnc + 575 130.11258 19.544865 -35.2 0.6 -12.8 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.94 0.01 5.92 0.02 5.88 0.01 0 eps Cnc + 576 130.11290 16.785095 -31.7 1.7 -13.5 1.7 12.48 0.00 13.72 0.00 99.00 99.00 11.57 0.00 11.38 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 577 130.11298 16.785190 -31.7 1.7 -13.5 1.7 11.97 0.60 11.58 0.60 11.43 0.60 11.37 0.60 11.35 0.60 10.51 0.02 10.12 0.02 10.03 0.02 1 + 578 130.11421 19.278004 -33.6 1.6 -11.4 1.6 11.54 0.60 11.15 0.60 11.00 0.60 10.94 0.60 10.92 0.60 10.01 0.02 9.70 0.02 9.65 0.02 1 + 579 130.11463 19.655497 -35.9 4.1 -10.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.30 0.02 10.81 0.02 10.69 0.02 0 + 580 130.11754 18.935793 -39.8 4.1 -13.2 4.1 16.43 0.00 15.19 0.00 14.33 0.00 13.92 0.00 13.74 0.00 12.58 0.02 11.90 0.02 11.71 0.02 0 + 581 130.12174 18.952671 -32.7 9.9 -14.4 9.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.75 0.02 11.06 0.02 10.86 0.02 0 + 582 130.12735 19.932989 -39.9 4.1 -10.3 4.1 18.21 0.01 16.59 0.00 15.30 0.00 14.76 0.00 14.47 0.00 13.27 0.02 12.61 0.02 12.39 0.03 0 + 583 130.12802 21.392551 -30.8 4.1 -12.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.09 0.02 13.43 0.03 13.26 0.03 0 + 584 130.12934 18.432256 -34.3 4.1 -11.9 4.1 17.62 0.00 16.39 0.00 15.22 0.00 14.69 0.00 14.44 0.00 13.25 0.02 12.63 0.02 12.37 0.02 0 + 585 130.13119 19.695279 -37.1 4.1 -10.1 4.1 15.82 0.00 14.60 0.60 13.99 0.60 13.43 0.00 13.21 0.00 12.10 0.02 11.46 0.02 11.22 0.02 0 + 586 130.13206 19.850292 -34.5 1.6 -14.0 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.29 0.02 9.98 0.02 9.91 0.02 1 + 587 130.13262 20.201716 -37.1 1.6 -14.4 1.6 11.64 0.60 11.26 0.60 11.15 0.60 11.13 0.60 11.12 0.60 10.26 0.02 9.90 0.02 9.83 0.01 0 + 588 130.13736 19.194330 -37.6 1.1 -14.1 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.08 0.03 7.99 0.02 7.96 0.00 0 BV Cnc + 589 130.13932 19.633547 -39.8 4.1 -10.0 4.1 12.62 0.60 12.05 0.60 11.83 0.60 11.73 0.60 11.68 0.60 10.71 0.02 10.26 0.02 10.17 0.02 0 + 590 130.13974 21.315163 -42.9 1.6 -10.5 1.6 11.95 0.60 11.56 0.60 11.43 0.60 11.39 0.60 11.37 0.60 10.47 0.02 10.15 0.02 10.10 0.02 0 + 591 130.13994 18.674534 -35.0 1.3 -12.2 1.3 12.01 0.60 11.54 0.60 11.34 0.60 11.23 0.60 11.19 0.60 10.25 0.02 9.94 0.02 9.83 0.01 1 + 592 130.14225 18.359168 -33.3 4.1 -13.0 4.1 19.59 0.01 18.29 0.01 16.78 0.00 16.04 0.00 15.72 0.00 14.42 0.03 13.74 0.03 13.62 0.05 0 + 593 130.14553 20.601029 -37.9 5.5 -19.1 5.5 20.26 0.02 18.98 0.01 17.27 0.00 16.49 0.00 16.11 0.01 14.88 0.03 14.24 0.05 13.96 0.05 0 + 594 130.15089 21.561703 -36.1 1.6 -12.5 1.6 12.00 0.60 11.56 0.60 11.40 0.60 11.33 0.60 11.31 0.60 10.39 0.02 10.02 0.02 9.97 0.02 0 + 595 130.15099 17.950097 -36.4 4.2 -15.0 4.2 18.27 0.01 17.01 0.00 15.63 0.00 14.95 0.00 14.67 0.00 13.47 0.02 12.84 0.02 12.58 0.02 0 + 596 130.15774 20.338282 -38.2 4.1 -17.7 4.1 15.68 0.00 14.47 0.00 13.81 0.00 13.72 0.00 13.32 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 597 130.16350 19.228298 -35.1 1.3 -13.5 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.33 0.01 7.24 0.01 7.23 0.01 0 BN Cnc + 598 130.16406 19.939966 -36.2 4.1 -13.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.56 0.03 12.99 0.03 12.69 0.03 0 + 599 130.16415 19.715386 -39.4 4.1 -15.1 4.1 18.34 0.01 17.12 0.00 15.70 0.00 15.09 0.00 14.86 0.00 13.46 0.02 12.87 0.02 12.59 0.02 0 + 600 130.16458 18.818244 -37.0 4.1 -10.9 4.1 14.93 0.00 13.63 0.00 13.39 0.60 13.15 0.00 12.82 0.00 11.76 0.01 11.16 0.02 11.00 0.01 0 + 601 130.16629 19.669215 -35.2 1.3 -13.8 1.3 11.43 0.60 10.95 0.60 10.73 0.60 10.60 0.60 10.56 0.60 9.61 0.02 9.31 0.02 9.19 0.02 0 + 602 130.17348 19.500204 -35.3 5.5 -13.3 5.5 20.08 0.03 18.74 0.01 16.96 0.00 16.12 0.00 15.71 0.00 14.36 0.03 13.87 0.03 13.54 0.04 0 + 603 130.17453 19.223679 -34.1 1.6 -9.4 1.6 11.07 0.60 10.65 0.60 10.48 0.60 10.40 0.60 10.38 0.60 9.46 0.02 9.15 0.02 9.06 0.02 0 + 604 130.17695 19.565966 -36.1 1.3 -15.4 1.3 11.61 0.60 11.22 0.60 11.07 0.60 11.01 0.60 10.99 0.60 10.09 0.02 9.78 0.02 9.71 0.02 0 + 605 130.18005 19.719328 -34.7 0.7 -11.6 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.40 0.01 6.37 0.02 6.33 0.01 0 42 Cnc + 606 130.18445 20.471863 -37.4 4.1 -14.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.24 0.02 10.58 0.02 10.40 0.02 0 + 607 130.18489 18.656539 -37.5 4.1 -7.4 4.1 13.32 0.00 12.90 0.60 12.54 0.60 12.31 0.60 12.10 0.00 11.13 0.02 10.65 0.02 10.50 0.01 1 + 608 130.19192 16.818708 -41.3 5.6 -8.7 5.6 20.55 0.05 19.27 0.01 17.50 0.01 16.62 0.00 16.25 0.01 14.93 0.04 14.35 0.04 14.06 0.05 0 + 609 130.19201 19.309612 -35.6 1.3 -13.3 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.73 0.02 8.56 0.02 8.53 0.02 0 BD+19 2074 + 610 130.19541 20.474765 -33.8 4.1 -18.8 4.1 16.69 0.00 15.48 0.00 14.45 0.00 14.04 0.00 13.74 0.00 12.58 0.02 11.97 0.02 11.68 0.02 0 + 611 130.19841 18.903316 -36.1 1.6 -14.9 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.13 0.02 9.78 0.02 9.69 0.02 0 + 612 130.19904 20.479955 -38.1 4.1 -13.4 4.1 17.74 0.01 16.56 0.00 15.25 0.00 14.64 0.00 14.38 0.00 13.13 0.02 12.51 0.02 12.25 0.02 0 + 613 130.19988 19.658928 -38.2 1.3 -14.7 1.3 11.43 0.60 10.95 0.60 10.76 0.60 10.67 0.60 10.63 0.60 9.69 0.02 9.34 0.02 9.25 0.02 0 + 614 130.20137 19.921923 -35.5 1.6 -15.2 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.86 0.02 9.59 0.02 9.51 0.02 0 + 615 130.20220 21.497087 -33.9 4.1 -18.2 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.03 0.02 12.39 0.02 12.22 0.02 0 + 616 130.21603 19.941717 -39.9 4.1 -15.7 4.1 17.68 0.00 16.42 0.00 15.49 0.60 14.70 0.00 14.46 0.01 13.30 0.02 12.68 0.03 12.40 0.02 0 + 617 130.21789 19.174544 -36.7 4.1 -11.9 4.1 17.91 0.01 16.68 0.00 15.22 0.00 14.57 0.00 14.26 0.00 12.99 0.02 12.38 0.02 12.10 0.02 0 + 618 130.21866 20.266527 -32.7 1.1 -12.1 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.91 0.01 7.85 0.04 7.80 0.01 0 BW Cnc + 619 130.21880 19.483208 -36.2 1.6 -13.8 1.6 10.65 0.60 10.42 0.60 10.31 0.60 10.21 0.60 10.17 0.60 9.34 0.02 9.15 0.02 9.05 0.02 0 + 620 130.22184 18.748313 -34.8 4.1 -16.7 4.1 19.93 0.02 18.62 0.01 17.01 0.00 16.21 0.01 15.87 0.01 14.59 0.03 13.97 0.04 13.67 0.04 1 + 621 130.22415 19.378876 -40.1 4.1 -13.9 4.1 16.37 0.00 15.11 0.00 99.00 99.00 13.86 0.00 13.67 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 622 130.22435 20.050632 -29.9 4.1 -17.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.73 0.03 14.08 0.04 13.76 0.05 1 + 623 130.22479 20.090093 -43.3 4.1 -16.7 4.1 18.36 0.01 17.15 0.00 15.81 0.00 15.24 0.00 14.95 0.00 13.80 0.02 13.19 0.03 12.88 0.03 0 + 624 130.22856 19.935194 -37.4 1.6 -15.9 1.6 12.45 0.60 11.91 0.60 11.71 0.60 11.61 0.60 11.56 0.60 10.60 0.02 10.19 0.02 10.13 0.02 0 + 625 130.23037 18.583094 -37.9 4.1 -10.6 4.1 15.09 0.00 13.93 0.00 13.55 0.60 13.22 0.00 13.01 0.00 11.92 0.02 11.26 0.02 11.13 0.02 0 + 626 130.23110 18.297845 -43.4 4.1 -10.0 4.1 19.72 0.01 18.51 0.01 16.99 0.00 16.28 0.00 15.94 0.00 14.71 0.03 14.05 0.04 13.86 0.05 0 + 627 130.23208 18.826146 -33.5 4.1 -15.2 4.1 19.31 0.01 18.06 0.01 16.60 0.00 15.90 0.00 15.58 0.00 14.34 0.02 13.75 0.03 13.45 0.04 0 + 628 130.23292 18.246181 -36.9 4.1 -13.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.68 0.02 13.01 0.03 12.83 0.03 0 + 629 130.23460 19.580351 -34.4 0.7 -10.7 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.37 0.01 6.33 0.01 6.28 0.01 0 EP Cnc + 630 130.23624 19.734787 -36.7 2.2 -11.9 2.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.72 0.02 10.31 0.03 10.21 0.02 0 + 631 130.23723 19.934862 -36.4 1.1 -11.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.13 0.01 8.08 0.03 8.05 0.02 0 HD 73818 + 632 130.23957 20.478164 -41.4 5.6 -7.7 5.6 20.64 0.05 19.40 0.02 17.63 0.01 16.85 0.00 16.45 0.01 15.09 0.03 14.56 0.06 14.31 0.08 1 + 633 130.24344 18.846168 -36.6 4.1 -19.9 4.1 19.02 0.01 17.77 0.01 16.24 0.00 15.58 0.00 15.24 0.00 13.99 0.03 13.43 0.03 13.13 0.03 0 + 634 130.24432 18.675075 -39.3 4.1 -11.5 4.1 14.19 0.00 13.41 0.60 12.91 0.60 12.60 0.60 12.35 0.00 11.25 0.02 10.69 0.02 10.49 0.01 0 + 635 130.24473 22.480510 -35.1 5.6 -14.6 5.6 21.69 0.10 20.39 0.02 18.44 0.01 17.54 0.01 17.08 0.01 15.69 0.06 15.23 0.10 14.89 0.12 0 + 636 130.24860 18.367892 -38.5 7.5 -5.8 7.0 12.17 0.60 11.58 0.60 11.38 0.60 11.29 0.60 11.23 0.60 10.26 0.02 9.78 0.02 9.68 0.02 1 + 637 130.25317 16.613337 -36.8 1.7 -5.4 1.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.83 0.02 9.40 0.03 9.33 0.01 0 + 638 130.25727 17.814317 -38.8 3.9 -20.2 3.9 19.63 0.01 18.38 0.01 16.80 0.00 16.06 0.00 15.69 0.01 14.51 0.04 13.80 0.04 13.51 0.03 0 + 639 130.26090 20.457693 -37.6 4.1 -10.7 4.1 12.83 0.60 12.25 0.60 11.98 0.60 11.83 0.60 11.77 0.60 10.78 0.02 10.41 0.02 10.29 0.01 0 + 640 130.26091 18.168523 -30.8 4.1 -10.0 4.1 20.39 0.02 19.04 0.01 17.31 0.01 16.55 0.00 16.14 0.01 14.87 0.03 14.27 0.04 13.89 0.05 0 + 641 130.26295 18.931933 -32.8 4.1 -9.1 4.1 18.81 0.01 17.55 0.00 16.13 0.00 15.50 0.00 15.19 0.00 13.96 0.02 13.28 0.03 13.10 0.03 0 + 642 130.26391 18.621075 -41.4 5.6 -11.6 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.32 0.05 14.88 0.07 14.36 0.08 0 + 643 130.27208 20.473473 -40.0 4.1 -13.7 4.1 15.72 0.00 14.50 0.00 13.82 0.00 13.52 0.00 13.35 0.00 12.27 0.02 11.60 0.02 11.41 0.02 0 + 644 130.27685 19.102931 -39.8 5.5 -12.8 5.5 20.72 0.04 19.44 0.02 17.63 0.01 16.80 0.01 16.39 0.01 15.11 0.04 14.52 0.04 14.17 0.06 0 + 645 130.27863 19.443587 -38.8 4.1 -16.7 4.1 18.56 0.01 17.30 0.00 15.93 0.60 15.25 0.00 14.95 0.00 13.75 0.02 13.09 0.02 12.85 0.03 0 + 646 130.28010 19.446905 -45.1 4.1 -12.4 4.1 12.85 0.60 12.24 0.60 12.01 0.60 11.89 0.60 11.83 0.60 10.84 0.02 10.40 0.02 10.29 0.02 0 + 647 130.28075 19.071274 -39.5 1.0 -12.3 1.1 10.35 0.60 9.99 0.60 9.89 0.60 9.88 0.60 9.87 0.60 8.99 0.01 8.67 0.02 8.64 0.02 0 BD+19 2076 + 648 130.28851 17.571369 -32.2 3.9 -13.5 3.9 20.27 0.02 18.91 0.01 17.22 0.00 16.44 0.00 16.07 0.01 14.74 0.04 14.22 0.04 13.86 0.04 0 + 649 130.28995 19.855122 -38.0 1.3 -14.5 1.3 11.28 0.60 10.76 0.60 10.53 0.60 10.41 0.60 10.36 0.60 9.41 0.02 9.06 0.02 8.94 0.02 0 BD+19 2077 + 650 130.29073 19.935349 -45.1 4.1 -11.9 4.1 14.10 0.00 13.14 0.60 12.83 0.60 12.68 0.60 12.48 0.00 11.50 0.02 10.89 0.02 10.76 0.02 0 + 651 130.29159 19.508765 -36.7 1.3 -10.4 1.3 12.67 0.00 99.00 99.00 99.00 99.00 11.41 0.00 11.86 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 BD+20 2176 + 652 130.29177 19.508927 -36.7 1.3 -10.4 1.3 10.33 0.60 10.07 0.60 10.01 0.60 10.01 0.60 10.02 0.60 9.18 0.01 8.93 0.02 8.91 0.02 1 BD+20 2176 + 653 130.29229 19.203668 -34.6 5.5 -19.8 5.5 20.65 0.03 19.24 0.02 19.44 0.60 16.60 0.00 16.22 0.00 14.89 0.04 14.29 0.05 13.90 0.05 0 + 654 130.29289 19.818599 -35.6 1.6 -11.9 1.6 11.80 0.60 11.35 0.60 11.19 0.60 11.13 0.60 11.10 0.60 10.18 0.02 9.82 0.02 9.75 0.02 1 + 655 130.29373 18.268613 -38.8 4.1 -14.8 4.1 17.12 0.00 15.88 0.00 14.78 0.00 14.26 0.00 14.04 0.00 12.87 0.02 12.20 0.02 11.96 0.02 0 + 656 130.29377 19.935187 -32.7 4.1 -10.1 4.1 17.51 0.00 16.27 0.00 15.08 0.60 14.35 0.00 14.06 0.00 12.86 0.02 12.26 0.02 11.97 0.02 1 + 657 130.29450 19.829590 -37.2 1.1 -11.1 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.36 0.02 8.24 0.01 8.19 0.01 0 HD 73854 + 658 130.29472 19.031613 -32.8 4.1 -8.6 4.1 19.01 0.01 17.74 0.01 16.07 0.60 15.34 0.00 14.98 0.00 13.70 0.02 13.08 0.03 12.84 0.03 0 + 659 130.29602 20.377347 -33.0 4.1 -19.4 4.1 16.64 0.00 15.45 0.00 14.48 0.00 14.04 0.00 13.83 0.00 12.69 0.02 12.00 0.02 11.82 0.01 0 + 660 130.29701 19.529628 -37.4 4.1 -11.0 4.1 17.16 0.00 15.95 0.00 14.76 0.00 14.21 0.00 13.94 0.00 12.77 0.02 12.14 0.02 11.94 0.02 1 + 661 130.29828 22.264299 -36.4 4.1 -18.6 4.1 17.26 0.01 16.03 0.00 14.89 0.00 14.37 0.00 14.13 0.00 12.99 0.02 12.40 0.02 12.10 0.02 1 + 662 130.30730 20.347727 -31.3 4.1 -12.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.67 0.02 13.02 0.03 12.86 0.02 0 + 663 130.30738 19.921982 -36.4 1.1 -11.6 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.88 0.01 7.82 0.01 7.77 0.01 0 HD 73872 + 664 130.30793 18.969147 -40.0 4.1 -10.1 4.1 16.98 0.00 15.75 0.00 14.59 0.00 14.06 0.00 13.81 0.00 12.64 0.02 11.99 0.02 11.76 0.01 0 + 665 130.30847 20.741569 -40.6 4.1 -14.3 4.1 17.98 0.01 16.64 0.60 15.38 0.60 14.58 0.00 14.27 0.00 13.01 0.02 12.38 0.02 12.15 0.02 1 + 666 130.31013 20.996198 -38.6 5.5 -16.2 5.5 19.81 0.02 18.60 0.01 16.96 0.00 16.23 0.00 15.88 0.00 14.62 0.03 14.02 0.04 13.66 0.04 0 + 667 130.31204 17.460153 -37.4 5.2 -6.9 5.2 20.14 0.02 18.84 0.01 17.22 0.00 16.46 0.00 16.09 0.01 14.84 0.04 14.23 0.05 13.95 0.04 0 + 668 130.31414 20.037763 -42.4 4.1 -10.9 4.1 14.83 0.00 13.74 0.00 13.46 0.60 13.01 0.00 12.87 0.00 11.83 0.02 11.18 0.02 11.02 0.02 0 + 669 130.31420 19.086234 -32.0 4.1 -10.7 4.1 17.46 0.00 16.24 0.00 15.06 0.00 14.53 0.00 14.30 0.00 13.11 0.02 12.49 0.02 12.25 0.02 0 + 670 130.31786 20.815221 -42.5 5.6 -17.7 5.6 21.37 0.06 19.91 0.02 17.96 0.01 17.09 0.00 16.68 0.01 15.36 0.05 14.63 0.05 14.48 0.09 1 + 671 130.32280 20.542493 -42.8 4.1 -11.5 4.1 19.07 0.01 17.83 0.00 16.29 0.00 15.59 0.00 15.29 0.00 14.02 0.02 13.42 0.03 13.11 0.03 1 + 672 130.32669 19.260960 -35.9 1.1 -11.4 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.43 0.01 7.35 0.04 7.29 0.02 0 HI Cnc + 673 130.32949 19.088511 -40.6 4.1 -16.9 4.1 19.11 0.01 17.76 0.01 16.10 0.60 15.36 0.00 15.04 0.00 13.70 0.02 13.13 0.03 12.87 0.02 1 + 674 130.33004 20.777552 -38.2 4.1 -15.5 4.1 13.86 0.00 13.31 0.60 12.90 0.60 12.64 0.60 12.41 0.00 11.38 0.02 10.84 0.02 10.70 0.02 0 + 675 130.33198 19.098448 -34.9 4.1 -16.2 4.1 20.42 0.03 18.43 0.60 17.20 0.60 16.60 0.00 16.20 0.01 14.93 0.04 14.34 0.05 14.00 0.06 0 + 676 130.33293 19.634624 -39.0 4.1 -8.3 4.1 14.01 0.00 13.25 0.60 12.89 0.60 12.67 0.60 12.47 0.00 11.45 0.02 10.90 0.02 10.76 0.02 1 + 677 130.33466 18.961917 -33.9 4.1 -12.9 4.1 19.13 0.01 17.87 0.01 16.36 0.00 15.64 0.00 15.31 0.01 14.09 0.03 13.47 0.03 13.16 0.03 0 + 678 130.33509 19.622886 -40.2 4.1 -12.5 4.1 16.50 0.00 15.28 0.00 14.38 0.00 13.97 0.00 13.78 0.00 12.68 0.02 11.96 0.02 11.79 0.02 1 + 679 130.33687 20.346544 -37.1 4.1 -18.7 4.1 17.35 0.00 16.14 0.00 15.03 0.00 14.54 0.00 14.32 0.00 13.16 0.02 12.52 0.02 12.27 0.02 0 + 680 130.33710 21.915005 -39.6 4.1 -8.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.29 0.02 12.60 0.02 12.43 0.02 0 + 681 130.34401 18.933893 -38.1 4.1 -10.1 4.1 13.36 0.00 12.94 0.60 12.57 0.60 12.35 0.60 12.17 0.00 11.16 0.02 10.68 0.02 10.54 0.02 0 + 682 130.34871 20.939491 -41.9 5.6 -19.9 5.6 20.91 0.04 19.66 0.02 17.84 0.01 17.01 0.01 16.62 0.01 15.33 0.05 14.71 0.06 14.46 0.08 0 + 683 130.34952 20.249232 -44.3 4.1 -11.8 4.1 15.28 0.00 14.06 0.00 13.53 0.60 13.11 0.00 12.75 0.00 11.63 0.02 10.98 0.02 10.78 0.02 0 + 684 130.35066 18.234055 -36.4 4.1 -16.8 4.1 16.35 0.00 15.11 0.00 14.21 0.00 13.77 0.00 13.55 0.00 12.28 0.02 11.62 0.02 11.45 0.02 0 + 685 130.35182 20.130431 -40.0 4.1 -19.3 4.1 17.17 0.00 15.96 0.00 14.64 0.00 14.03 0.00 13.74 0.00 12.53 0.02 11.90 0.02 11.64 0.02 0 + 686 130.35753 19.943461 -34.7 1.1 -10.5 1.1 11.04 0.00 12.54 0.00 12.46 0.00 11.30 0.00 10.60 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 BD+20 2181 + 687 130.35758 19.943593 -34.7 1.1 -10.5 1.1 11.04 0.60 10.71 0.60 10.59 0.60 10.54 0.60 10.53 0.60 9.66 0.02 9.40 0.02 9.33 0.02 1 BD+20 2181 + 688 130.35835 19.987520 -38.2 4.1 -14.3 4.1 18.86 0.01 17.50 0.01 16.11 0.00 15.43 0.00 15.10 0.00 13.89 0.02 13.26 0.03 13.08 0.03 1 + 689 130.35886 17.803518 -35.3 3.9 -11.1 3.9 18.44 0.01 17.18 0.00 15.80 0.00 15.14 0.00 14.82 0.00 13.65 0.03 12.97 0.03 12.77 0.02 0 + 690 130.36157 19.876831 -33.0 4.1 -7.0 4.1 20.10 0.02 18.82 0.01 17.08 0.00 16.28 0.00 15.89 0.00 14.66 0.03 13.95 0.04 13.77 0.05 0 + 691 130.36242 19.542488 -37.5 1.1 -12.0 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.94 0.01 8.78 0.02 8.72 0.02 0 BD+20 2180 + 692 130.36493 19.277601 -35.6 4.1 -8.9 4.1 16.62 0.00 15.41 0.00 14.43 0.00 13.96 0.00 13.74 0.00 12.59 0.02 11.92 0.02 11.72 0.02 0 + 693 130.36542 21.061336 -38.7 4.1 -14.2 4.1 18.54 0.01 17.35 0.00 15.89 0.00 15.24 0.00 14.92 0.00 13.66 0.02 13.06 0.03 12.76 0.03 0 + 694 130.36955 19.746706 -38.5 1.3 -13.2 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.81 0.02 9.55 0.02 9.47 0.02 0 + 695 130.36997 19.975588 -41.4 4.1 -14.4 4.1 17.46 0.00 16.26 0.00 15.04 0.00 14.43 0.00 14.15 0.00 12.99 0.02 12.36 0.02 12.10 0.02 0 + 696 130.37057 22.268163 -38.6 1.0 -16.0 1.0 10.49 0.60 10.15 0.60 10.01 0.60 9.95 0.60 9.93 0.60 9.04 0.01 8.78 0.01 8.70 0.01 0 BD+22 1975 + 697 130.37071 18.759704 -29.5 5.5 -9.5 5.5 20.67 0.03 19.32 0.02 17.52 0.60 16.81 0.00 16.42 0.01 15.12 0.04 14.51 0.06 14.19 0.06 0 + 698 130.37784 18.871887 -39.1 4.1 -13.6 4.1 13.34 0.60 12.57 0.60 12.19 0.60 11.96 0.60 11.85 0.60 10.77 0.02 10.31 0.03 10.15 0.02 0 + 699 130.38139 18.500551 -35.1 1.2 -13.3 1.3 10.40 0.60 10.18 0.60 10.14 0.60 10.16 0.60 10.18 0.60 9.36 0.02 9.14 0.02 9.10 0.02 0 + 700 130.38470 18.669642 -30.0 4.1 -10.1 4.1 18.44 0.01 17.21 0.00 15.91 0.00 15.27 0.00 14.98 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 701 130.38473 18.669647 -30.0 4.1 -10.1 4.1 18.47 0.01 16.90 0.60 16.08 0.60 15.28 0.00 14.99 0.00 13.80 0.02 13.18 0.03 12.92 0.03 1 + 702 130.38539 20.101895 -33.2 4.1 -11.2 4.1 19.65 0.02 18.38 0.01 16.86 0.00 16.16 0.00 15.84 0.01 14.58 0.03 13.94 0.04 13.72 0.04 0 + 703 130.38969 19.550054 -39.8 4.1 -13.2 4.1 19.75 0.01 18.50 0.01 16.91 0.01 16.19 0.00 15.87 0.00 14.57 0.03 13.95 0.04 13.68 0.04 0 + 704 130.39087 19.969095 -36.7 1.3 -16.4 1.3 12.23 0.00 99.00 99.00 99.00 99.00 11.74 0.00 11.23 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 705 130.39103 19.969041 -36.7 1.3 -16.4 1.3 12.35 0.60 11.83 0.60 11.57 0.60 11.42 0.60 11.35 0.60 10.39 0.02 10.08 0.02 9.93 0.01 0 + 706 130.39826 17.490854 -41.3 3.9 -11.1 3.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.86 0.02 13.27 0.04 13.05 0.03 0 + 707 130.39862 18.743064 -35.7 4.1 -14.7 4.1 17.66 0.01 16.47 0.00 15.25 0.60 14.65 0.00 14.39 0.00 13.15 0.02 12.53 0.02 12.32 0.02 1 + 708 130.39931 21.293613 -38.9 5.5 -16.3 5.5 19.41 0.01 18.15 0.00 16.60 0.00 15.88 0.00 15.53 0.00 14.29 0.03 13.64 0.04 13.45 0.04 0 + 709 130.39989 19.107092 -35.0 4.1 -13.1 4.1 14.70 0.00 13.72 0.60 13.30 0.60 12.93 0.00 12.81 0.00 11.75 0.02 11.12 0.02 10.98 0.02 0 + 710 130.40087 19.142651 -36.0 1.0 -14.3 1.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.52 0.01 8.43 0.01 8.35 0.02 0 HD 73937 + 711 130.40200 20.567871 -43.5 4.1 -18.1 4.1 19.22 0.01 17.93 0.01 16.40 0.00 15.68 0.00 15.36 0.00 14.14 0.03 13.48 0.03 13.18 0.03 1 + 712 130.40248 18.904285 -36.6 4.1 -18.3 4.1 18.06 0.01 17.14 0.02 15.46 0.00 14.80 0.00 14.52 0.00 13.31 0.02 12.67 0.02 12.44 0.03 0 + 713 130.40561 20.210232 -39.8 4.1 -13.8 4.1 19.18 0.01 17.95 0.01 16.40 0.00 15.71 0.00 15.41 0.01 14.19 0.03 13.48 0.03 13.33 0.03 0 + 714 130.40578 19.520531 -39.3 4.1 -11.0 4.1 14.04 0.00 13.35 0.60 12.94 0.60 12.68 0.60 12.47 0.00 11.43 0.02 10.88 0.02 10.73 0.01 1 + 715 130.41027 17.640000 -40.2 3.9 -11.7 3.9 15.85 0.00 14.62 0.00 13.90 0.00 13.56 0.00 13.41 0.00 12.28 0.02 11.60 0.03 11.43 0.01 0 + 716 130.41331 19.674503 -39.2 4.1 -13.8 4.1 17.76 0.01 16.59 0.00 15.25 0.00 14.65 0.00 14.39 0.00 13.19 0.02 12.56 0.02 12.30 0.02 0 + 717 130.41673 20.672199 -36.8 0.8 -13.9 0.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.79 0.02 8.64 0.01 8.59 0.01 0 BD+21 1891 + 718 130.42382 19.832625 -37.1 4.1 -20.0 4.1 19.50 0.01 18.21 0.01 16.72 0.00 16.04 0.00 15.74 0.01 14.52 0.03 13.83 0.03 13.60 0.04 1 + 719 130.42519 20.568891 -41.3 5.5 -13.9 5.5 20.02 0.03 18.79 0.01 17.14 0.00 16.36 0.00 16.01 0.00 14.80 0.03 14.07 0.04 13.83 0.04 0 + 720 130.42622 19.660547 -37.1 0.9 -12.9 0.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.69 0.03 8.57 0.02 8.48 0.02 1 BD+20 2183 + 721 130.42633 18.213284 -36.4 4.2 -19.6 4.2 20.97 0.03 19.66 0.02 17.83 0.01 17.00 0.00 16.60 0.01 15.27 0.04 14.71 0.06 14.27 0.06 0 + 722 130.43048 21.361687 -38.5 5.5 -15.9 5.5 20.24 0.02 18.90 0.01 17.28 0.00 16.48 0.00 16.14 0.00 14.90 0.03 14.20 0.04 13.91 0.05 0 + 723 130.43080 21.497311 -34.8 5.5 -16.5 5.5 20.71 0.04 19.29 0.01 17.43 0.01 16.49 0.00 16.05 0.00 14.71 0.03 14.06 0.04 13.63 0.04 0 + 724 130.43194 19.962133 -40.5 2.2 -13.8 2.2 12.82 0.60 12.23 0.60 11.97 0.60 11.83 0.60 11.77 0.60 10.77 0.02 10.37 0.02 10.26 0.02 0 + 725 130.43260 20.226891 -36.0 1.3 -16.9 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.46 0.02 9.23 0.02 9.14 0.01 0 BD+20 2184 + 726 130.43276 19.302276 -38.1 4.1 -9.4 4.1 16.13 0.00 14.89 0.00 14.11 0.00 13.74 0.00 13.57 0.00 12.48 0.02 11.80 0.02 11.64 0.02 1 + 727 130.43381 20.604438 -35.1 4.1 -14.1 4.1 18.78 0.01 17.60 0.00 16.11 0.00 15.44 0.00 15.12 0.00 13.87 0.02 13.20 0.03 12.99 0.03 0 + 728 130.43946 19.267325 -37.6 1.2 -10.9 1.3 10.46 0.60 10.20 0.60 10.11 0.60 10.08 0.60 10.06 0.60 9.22 0.02 9.03 0.02 8.93 0.02 1 BD+19 2081 + 729 130.44956 20.389607 -38.8 5.6 -14.8 5.6 20.68 0.03 19.39 0.02 18.16 0.04 16.79 0.00 16.40 0.01 15.08 0.04 14.54 0.05 14.23 0.06 0 + 730 130.45071 19.458669 -43.1 4.1 -8.3 4.1 14.13 0.00 13.35 0.60 12.93 0.60 12.68 0.60 12.50 0.00 11.41 0.02 10.88 0.02 10.73 0.01 0 + 731 130.45552 19.196401 -37.8 4.1 -12.2 4.1 15.09 0.00 13.95 0.00 13.39 0.60 12.96 0.00 12.76 0.00 11.65 0.02 11.01 0.02 10.83 0.01 0 + 732 130.45600 20.076716 -33.9 4.1 -15.8 4.1 17.59 0.00 16.35 0.00 14.99 0.00 14.37 0.00 14.08 0.00 12.84 0.02 12.27 0.02 11.97 0.02 1 + 733 130.45847 19.659624 -37.2 4.1 -8.3 4.1 16.79 0.00 15.57 0.00 14.57 0.00 14.13 0.00 13.92 0.00 12.74 0.02 12.13 0.02 11.89 0.02 0 + 734 130.45867 19.874176 -38.4 1.1 -13.7 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.24 0.01 5.03 0.18 4.68 0.00 0 HD 73974 + 735 130.46067 19.494303 -33.0 5.5 -11.4 5.5 20.50 0.02 19.17 0.01 17.45 0.01 16.69 0.00 16.32 0.01 15.03 0.04 14.39 0.04 14.12 0.06 0 + 736 130.46624 20.346612 -38.1 4.1 -18.2 4.1 17.23 0.00 16.02 0.00 14.92 0.00 14.44 0.00 14.25 0.00 13.05 0.02 12.39 0.03 12.19 0.02 0 + 737 130.46652 20.166984 -39.5 1.6 -15.9 1.6 12.53 0.00 14.85 0.00 99.00 99.00 12.17 0.00 11.66 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 738 130.46753 19.707833 -32.7 4.1 -8.0 4.1 19.05 0.01 17.79 0.01 16.23 0.00 15.49 0.00 15.16 0.00 13.89 0.02 13.24 0.02 13.01 0.02 1 + 739 130.46774 18.051851 -38.3 4.1 -15.0 4.1 15.63 0.00 14.43 0.00 13.75 0.00 13.47 0.00 13.38 0.00 12.17 0.02 11.51 0.02 11.34 0.02 0 + 740 130.47143 20.159456 -36.8 1.1 -12.6 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.93 0.02 7.81 0.01 7.79 0.01 0 HD 73993 + 741 130.47219 19.265475 -32.1 5.5 -12.1 5.5 20.36 0.03 18.79 0.01 17.19 0.01 16.45 0.00 16.07 0.00 14.76 0.03 14.24 0.04 13.89 0.05 0 + 742 130.47658 19.257416 -35.9 1.3 -12.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.03 0.02 9.73 0.02 9.64 0.02 0 + 743 130.47758 18.302799 -30.3 4.1 -17.3 4.1 19.79 0.02 18.50 0.01 16.85 0.00 16.06 0.00 15.71 0.00 14.39 0.02 13.79 0.03 13.49 0.04 0 + 744 130.47971 17.094664 -44.9 2.2 -13.1 2.2 12.69 0.60 11.96 0.60 11.65 0.60 11.48 0.60 11.39 0.60 10.33 0.01 9.84 0.03 9.69 0.02 1 + 745 130.48273 19.689641 -36.2 1.1 -15.0 1.1 11.39 0.60 11.03 0.60 10.88 0.60 10.80 0.60 10.77 0.60 9.87 0.02 9.63 0.02 9.54 0.01 0 + 746 130.49076 18.911690 -37.5 1.0 -10.4 1.0 9.51 0.60 9.39 0.60 9.39 0.60 9.40 0.60 9.40 0.60 8.64 0.04 8.49 0.02 8.43 0.02 0 HD 73994 + 747 130.49510 20.107527 -43.9 4.1 -10.6 4.1 13.65 0.00 12.93 0.60 12.60 0.60 12.42 0.60 12.24 0.00 11.24 0.02 10.72 0.02 10.60 0.01 0 + 748 130.49671 20.918618 -36.0 1.3 -16.4 1.3 11.60 0.60 11.20 0.60 11.06 0.60 10.99 0.60 10.98 0.60 10.07 0.02 9.76 0.02 9.70 0.02 1 + 749 130.49721 19.745859 -40.8 4.1 -8.7 4.1 16.72 0.00 15.52 0.00 14.68 0.60 14.10 0.00 13.86 0.00 12.74 0.02 12.07 0.03 11.89 0.02 1 + 750 130.51354 21.172635 -43.5 5.5 -13.2 5.5 20.36 0.03 19.12 0.02 17.39 0.00 16.57 0.00 16.18 0.01 14.87 0.03 14.20 0.04 13.94 0.05 0 + 751 130.52147 20.965686 -39.6 4.1 -18.7 4.1 16.61 0.00 15.38 0.00 14.26 0.00 13.77 0.00 13.55 0.00 12.38 0.02 11.70 0.02 11.51 0.02 0 + 752 130.52709 19.411251 -37.5 1.1 -12.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.55 0.01 7.46 0.01 7.43 0.02 0 BX Cnc + 753 130.53260 22.184732 -38.8 4.1 -16.9 4.1 18.75 0.01 17.51 0.00 16.08 0.00 15.42 0.00 15.15 0.00 13.88 0.02 13.25 0.02 13.02 0.03 1 + 754 130.54277 18.766757 -33.2 4.1 -17.8 4.1 19.93 0.02 18.60 0.01 17.02 0.00 16.25 0.00 15.91 0.00 14.64 0.03 14.03 0.04 13.75 0.05 1 + 755 130.54504 18.934366 -35.8 1.0 -12.8 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.44 0.01 7.39 0.01 7.35 0.01 0 BY Cnc + 756 130.54788 19.277044 -37.5 2.2 -9.8 2.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.66 0.02 10.24 0.02 10.17 0.01 0 + 757 130.54891 17.519143 -43.8 7.1 -9.8 17.1 19.68 0.01 18.43 0.01 16.93 0.00 16.24 0.00 15.93 0.00 14.72 0.04 14.06 0.04 13.94 0.05 0 + 758 130.55133 19.213525 -38.8 4.1 -10.7 4.1 14.18 0.00 13.35 0.60 12.98 0.60 12.78 0.60 12.54 0.00 11.54 0.02 10.94 0.02 10.83 0.01 0 + 759 130.55271 21.996883 -31.4 1.3 -8.8 1.3 12.15 0.60 11.58 0.60 11.35 0.60 11.25 0.60 11.19 0.60 10.21 0.02 9.79 0.02 9.72 0.02 1 + 760 130.55289 18.683618 -34.1 4.1 -13.8 4.1 19.53 0.02 18.14 0.01 16.68 0.00 15.96 0.00 15.67 0.00 14.39 0.03 13.84 0.03 13.56 0.04 0 + 761 130.55343 19.267747 -38.4 4.1 -9.5 4.1 13.93 0.00 14.87 0.00 14.72 0.00 12.43 0.00 12.23 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 762 130.55673 19.835726 -40.8 4.1 -16.5 4.1 17.06 0.00 15.86 0.00 15.33 0.02 14.31 0.00 14.09 0.00 12.95 0.02 12.29 0.03 12.05 0.02 0 + 763 130.56446 19.687675 -38.0 1.1 -13.4 1.1 10.20 0.60 9.96 0.60 9.89 0.60 9.87 0.60 9.87 0.60 9.03 0.01 8.85 0.02 8.77 0.01 1 + 764 130.56447 19.815992 -40.0 4.1 -11.0 4.1 17.96 0.01 16.76 0.00 15.48 0.00 14.92 0.00 14.65 0.00 13.48 0.02 12.87 0.03 12.62 0.02 0 + 765 130.56926 20.092344 -43.1 4.1 -15.2 4.1 14.78 0.00 13.68 0.00 13.33 0.60 12.96 0.00 12.84 0.00 11.75 0.02 11.14 0.02 10.99 0.01 0 + 766 130.57298 17.987436 -42.2 4.1 -16.7 4.1 18.55 0.01 17.35 0.00 15.90 0.00 15.25 0.00 14.95 0.00 13.71 0.02 13.07 0.03 12.83 0.03 1 + 767 130.57629 18.392199 -34.6 4.1 -13.9 4.1 18.47 0.01 17.33 0.01 16.06 0.60 15.29 0.00 15.01 0.00 13.79 0.02 13.19 0.03 12.92 0.02 1 + 768 130.57841 20.409718 -33.8 10.3 -9.5 10.7 12.58 0.60 12.02 0.60 11.84 0.60 11.76 0.60 11.71 0.60 10.76 0.02 10.29 0.02 10.19 0.02 0 + 769 130.58004 19.037437 -32.2 4.1 -9.4 4.1 17.84 0.01 16.60 0.00 15.39 0.00 14.86 0.00 14.61 0.00 13.44 0.02 12.84 0.02 12.53 0.02 0 + 770 130.58360 19.151583 -37.1 4.1 -12.0 4.1 13.96 0.00 13.32 0.60 12.90 0.60 12.65 0.60 12.42 0.00 11.39 0.02 10.85 0.02 10.70 0.01 0 + 771 130.58374 20.036445 -35.9 1.1 -15.2 1.1 9.89 0.60 9.65 0.60 9.56 0.60 9.52 0.60 9.50 0.60 8.68 0.02 8.45 0.05 8.41 0.02 0 BD+20 2189 + 772 130.58784 20.055770 -38.0 4.1 -20.1 4.1 19.19 0.01 17.93 0.01 16.53 0.01 15.74 0.00 15.45 0.01 14.22 0.02 13.53 0.03 13.27 0.03 0 + 773 130.59013 20.181425 -34.4 1.1 -14.3 1.1 12.20 0.00 99.00 99.00 99.00 99.00 99.00 99.00 11.14 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 HD 74058 + 774 130.59014 20.181807 -34.4 1.1 -14.3 1.1 9.32 0.60 9.22 0.60 9.19 0.60 9.16 0.60 9.22 0.60 8.44 0.01 8.28 0.03 8.28 0.01 0 HD 74058 + 775 130.60829 21.230832 -37.1 4.1 -9.9 4.1 17.81 0.00 16.40 0.00 15.04 0.00 14.67 0.00 14.08 0.00 12.75 0.02 12.11 0.02 11.90 0.02 0 + 776 130.61681 17.246758 -43.9 3.9 -8.7 3.9 18.68 0.01 17.48 0.00 16.10 0.00 15.45 0.00 15.17 0.00 13.97 0.03 13.37 0.03 13.11 0.02 0 + 777 130.62355 19.331247 -32.2 4.1 -13.1 4.1 20.42 0.02 19.08 0.01 17.41 0.01 16.64 0.00 16.24 0.00 15.02 0.04 14.35 0.05 14.05 0.05 0 + 778 130.62442 19.975457 -39.0 4.1 -14.8 4.1 20.02 0.03 18.62 0.01 17.19 0.60 16.29 0.01 15.97 0.01 14.74 0.03 14.14 0.04 13.73 0.05 0 + 779 130.62798 19.116032 -36.9 4.1 -17.6 4.1 19.83 0.01 18.50 0.01 16.91 0.00 16.16 0.00 15.81 0.01 14.51 0.03 13.99 0.04 13.59 0.04 0 + 780 130.62811 19.491935 -36.3 4.1 -11.5 4.1 18.90 0.01 17.66 0.00 16.47 0.00 15.61 0.00 15.30 0.00 14.10 0.03 13.53 0.03 13.23 0.03 1 + 781 130.63347 18.591117 -32.6 4.1 -9.7 4.1 16.61 0.00 15.41 0.00 14.50 0.00 14.02 0.00 13.81 0.00 12.64 0.02 12.04 0.03 11.79 0.02 1 + 782 130.63429 19.396172 -36.1 1.5 -10.4 1.6 11.36 0.60 10.97 0.60 10.82 0.60 10.76 0.60 10.74 0.60 9.84 0.02 9.54 0.02 9.46 0.01 0 + 783 130.64018 18.458064 -44.7 5.6 -10.6 5.6 21.45 0.06 20.25 0.03 18.33 0.01 17.41 0.01 16.97 0.01 15.62 0.07 14.93 0.08 14.63 0.10 0 + 784 130.64168 19.603415 -38.4 4.1 -14.5 4.1 17.64 0.00 16.45 0.00 15.09 0.00 14.45 0.00 14.16 0.00 12.93 0.02 12.38 0.02 12.11 0.02 0 + 785 130.64512 20.994658 -40.7 4.1 -15.5 4.1 16.52 0.00 15.28 0.00 14.35 0.00 13.94 0.00 13.74 0.00 12.63 0.02 11.93 0.02 11.75 0.02 0 + 786 130.64520 17.991003 -32.4 1.5 -6.6 1.6 11.05 0.60 10.66 0.60 10.50 0.60 10.42 0.60 10.40 0.60 9.48 0.02 9.22 0.02 9.13 0.01 0 + 787 130.65332 18.388878 -32.5 1.4 -14.3 1.5 10.40 0.60 10.14 0.60 10.05 0.60 10.02 0.60 10.01 0.60 9.16 0.02 8.97 0.02 8.88 0.02 1 BD+18 2020 + 788 130.65408 20.142149 -43.5 4.1 -11.8 4.1 14.54 0.00 13.55 0.60 13.15 0.60 12.91 0.60 12.71 0.00 11.63 0.02 11.04 0.02 10.87 0.02 0 + 789 130.65664 19.988580 -31.8 4.1 -12.4 4.1 19.43 0.01 18.18 0.01 16.58 0.00 15.86 0.00 15.50 0.00 14.26 0.03 13.67 0.04 13.39 0.04 0 + 790 130.65953 18.541069 -41.3 5.6 -10.8 5.6 21.86 0.09 20.32 0.03 20.16 0.60 17.56 0.01 17.11 0.01 15.72 0.08 15.21 0.11 14.78 0.10 0 + 791 130.66422 19.414409 -40.0 4.1 -8.3 4.1 19.46 0.01 18.14 0.01 16.62 0.00 15.94 0.00 15.60 0.00 14.35 0.03 13.77 0.04 13.50 0.04 1 + 792 130.66748 19.133071 -35.4 1.6 -10.8 1.6 12.65 0.60 12.08 0.60 11.84 0.60 11.72 0.60 11.66 0.60 10.69 0.01 10.29 0.02 10.19 0.01 0 + 793 130.66964 19.543164 -38.1 1.3 -10.9 1.3 9.97 0.60 9.77 0.60 9.73 0.60 9.75 0.60 9.77 0.60 8.95 0.04 8.76 0.02 8.72 0.02 0 BD+20 2192 + 794 130.67064 19.532940 -30.5 4.1 -16.9 4.1 20.41 0.03 18.96 0.01 17.34 0.01 16.63 0.00 16.27 0.01 15.00 0.04 14.55 0.05 14.09 0.06 1 + 795 130.67521 19.292300 -37.1 4.1 -10.2 4.1 15.30 0.00 14.14 0.00 13.63 0.60 13.16 0.00 12.98 0.00 11.86 0.02 11.23 0.02 11.05 0.02 1 + 796 130.67698 19.099691 -35.7 1.6 -13.3 1.6 11.86 0.60 11.44 0.60 11.29 0.60 11.23 0.60 11.21 0.60 10.30 0.02 9.98 0.02 9.88 0.02 0 + 797 130.68209 19.623185 -36.3 2.2 -14.5 2.2 12.66 0.60 11.97 0.60 11.67 0.60 11.49 0.60 11.41 0.60 10.37 0.02 9.91 0.02 9.80 0.02 0 + 798 130.68498 19.579865 -38.3 1.1 -12.0 1.1 9.79 0.60 9.66 0.60 9.62 0.60 9.58 0.60 9.61 0.60 8.86 0.04 8.68 0.02 8.63 0.02 0 BD+20 2193 + 799 130.68578 18.466664 -40.2 4.1 -12.7 4.1 19.87 0.02 18.63 0.01 17.04 0.00 16.21 0.00 15.86 0.01 14.52 0.04 13.94 0.04 13.64 0.05 0 + 800 130.68869 18.859935 -35.9 1.5 -9.0 1.6 11.12 0.60 10.71 0.60 10.55 0.60 10.47 0.60 10.45 0.60 9.53 0.02 9.23 0.02 9.17 0.01 0 + 801 130.68899 20.592618 -35.4 5.6 -10.4 5.6 20.40 0.03 19.17 0.01 17.43 0.01 16.65 0.00 16.25 0.01 99.00 99.00 99.00 99.00 99.00 99.00 0 + 802 130.68901 20.592627 -35.4 5.6 -10.4 5.6 20.42 0.04 21.17 0.60 19.40 0.60 16.64 0.01 16.24 0.01 14.91 0.04 14.35 0.06 14.13 0.07 0 + 803 130.69142 21.271197 -40.6 4.1 -12.5 4.1 14.87 0.00 13.83 0.00 13.47 0.60 13.15 0.00 12.84 0.00 11.73 0.02 11.09 0.03 10.92 0.01 0 + 804 130.69395 18.438601 -36.5 5.5 -6.6 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.99 0.05 14.43 0.06 14.09 0.07 0 + 805 130.70192 20.573432 -34.9 2.2 -12.7 2.2 13.02 0.60 12.37 0.60 12.07 0.60 11.90 0.60 11.82 0.60 10.79 0.02 10.36 0.02 10.26 0.02 0 + 806 130.70691 18.859742 -31.7 4.1 -18.0 4.1 16.65 0.00 15.45 0.00 14.47 0.00 13.99 0.00 13.76 0.00 12.61 0.02 11.92 0.02 11.74 0.02 0 + 807 130.71032 19.917688 -40.5 4.1 -9.5 4.1 18.14 0.00 16.94 0.00 15.60 0.00 15.03 0.00 14.73 0.00 13.53 0.02 12.90 0.03 12.66 0.02 0 + 808 130.71040 20.334397 -39.4 5.5 -15.4 5.5 20.39 0.03 19.14 0.01 17.42 0.01 16.65 0.00 16.28 0.01 14.96 0.04 14.40 0.05 14.07 0.05 1 + 809 130.71775 19.862756 -39.7 4.1 -8.4 4.1 17.59 0.00 16.41 0.00 15.10 0.00 14.52 0.00 14.24 0.00 13.00 0.02 12.37 0.02 12.21 0.02 0 + 810 130.72113 20.819223 -38.3 1.0 -15.0 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.21 0.02 8.14 0.05 8.06 0.02 0 HD 74135 + 811 130.72961 20.520607 -42.1 5.6 -10.8 5.6 21.60 0.05 20.27 0.03 18.31 0.01 17.42 0.01 16.96 0.01 15.55 0.06 14.94 0.09 14.57 0.08 0 + 812 130.73635 20.071677 -39.2 4.1 -12.2 4.1 18.73 0.01 17.47 0.00 15.99 0.00 15.31 0.00 14.99 0.00 13.77 0.03 13.17 0.02 12.79 0.03 1 + 813 130.75219 20.337814 -36.3 1.6 -14.7 1.6 11.69 0.00 12.14 0.00 12.15 0.00 11.85 0.00 11.03 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 814 130.75769 19.901260 -39.0 5.5 -19.7 5.5 20.39 0.02 19.13 0.01 17.38 0.01 16.59 0.00 16.20 0.00 14.98 0.04 14.27 0.04 14.05 0.06 1 + 815 130.75999 19.167558 -37.3 2.2 -11.3 2.2 12.44 0.60 11.94 0.60 11.72 0.60 11.61 0.60 11.57 0.60 10.61 0.01 10.25 0.02 10.16 0.02 0 + 816 130.76196 21.753765 -42.5 4.1 -18.1 4.1 19.63 0.03 18.32 0.01 16.61 0.00 15.75 0.00 15.37 0.00 13.98 0.02 13.43 0.04 13.07 0.03 1 + 817 130.77203 19.465176 -39.3 4.1 -12.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.36 0.02 11.65 0.02 11.55 0.02 0 + 818 130.77312 18.918331 -32.7 5.5 -12.5 5.5 20.75 0.05 19.51 0.02 17.75 0.01 16.91 0.00 16.51 0.01 15.08 0.05 14.62 0.06 14.31 0.08 0 + 819 130.77474 19.437574 -36.3 1.4 -15.5 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.79 0.02 8.52 0.01 8.46 0.02 0 BD+19 2087 + 820 130.77552 19.414488 -36.7 4.1 -16.5 4.1 18.54 0.01 17.26 0.00 15.85 0.00 15.21 0.00 14.90 0.00 13.69 0.02 13.04 0.02 12.79 0.02 1 + 821 130.77783 19.791586 -38.9 4.1 -14.6 4.1 14.16 0.00 13.32 0.60 12.94 0.60 12.71 0.60 12.52 0.00 11.47 0.02 10.89 0.02 10.77 0.02 0 + 822 130.77783 19.791580 -38.9 4.1 -14.6 4.1 14.15 0.00 13.26 0.00 12.77 0.00 12.98 0.00 12.55 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 823 130.77940 19.068342 -37.0 1.1 -10.1 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.59 0.02 8.39 0.02 8.36 0.03 0 HD 74186 + 824 130.78419 19.713199 -38.1 4.1 -12.5 4.1 13.91 0.00 12.92 0.60 12.62 0.60 12.45 0.60 12.35 0.00 11.30 0.02 10.77 0.02 10.67 0.01 0 + 825 130.78497 19.468365 -36.3 4.1 -12.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.25 0.02 12.65 0.02 12.39 0.02 1 + 826 130.79486 19.526299 -38.6 2.1 -17.1 2.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.44 0.02 10.07 0.02 10.01 0.02 0 + 827 130.80009 18.695849 -35.4 5.5 -14.4 5.5 20.33 0.03 18.95 0.01 17.26 0.60 16.51 0.00 16.15 0.01 14.88 0.04 14.27 0.04 14.03 0.06 0 + 828 130.80263 19.574717 -38.0 4.1 -21.6 4.1 19.52 0.02 18.21 0.01 16.63 0.60 15.89 0.00 15.56 0.01 14.31 0.03 13.65 0.02 13.46 0.03 0 + 829 130.80374 18.530784 -35.7 4.1 -11.3 4.1 17.62 0.01 16.29 0.00 15.29 0.60 14.59 0.00 14.34 0.00 13.17 0.02 12.51 0.02 12.28 0.02 1 + 830 130.81102 17.708384 -33.2 3.8 -15.0 3.8 19.76 0.01 18.50 0.01 16.92 0.00 16.16 0.00 15.77 0.01 14.55 0.04 13.91 0.04 13.61 0.03 0 + 831 130.81335 20.065564 -43.1 4.1 -12.8 4.1 13.98 0.00 13.16 0.60 12.79 0.60 12.58 0.60 12.41 0.00 11.36 0.02 10.80 0.02 10.68 0.02 0 + 832 130.81569 20.475746 -37.3 4.1 -7.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.39 0.03 13.70 0.04 13.45 0.04 0 + 833 130.81611 19.109200 -32.1 4.1 -15.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.21 0.03 13.63 0.03 13.34 0.04 0 + 834 130.82100 20.411288 -37.1 5.6 -10.8 5.6 20.72 0.03 19.45 0.02 17.73 0.01 16.96 0.00 16.61 0.01 15.29 0.06 14.72 0.07 14.54 0.09 0 + 835 130.82427 20.510359 -37.2 1.6 -13.8 1.6 12.33 0.60 11.73 0.60 11.49 0.60 11.38 0.60 11.31 0.60 10.32 0.02 9.89 0.02 9.78 0.02 0 + 836 130.82671 19.518834 -42.3 4.1 -9.3 4.1 18.08 0.01 16.92 0.00 15.61 0.00 15.03 0.00 14.76 0.00 13.57 0.02 12.89 0.02 12.68 0.03 0 + 837 130.83410 19.769019 -40.7 1.5 -14.5 1.5 10.96 0.60 10.66 0.60 10.56 0.60 10.53 0.60 10.53 0.60 9.66 0.02 9.42 0.02 9.36 0.02 0 + 838 130.83446 20.079312 -43.5 4.1 -10.6 4.1 19.59 0.01 18.35 0.01 16.77 0.00 16.05 0.00 15.72 0.01 14.46 0.03 13.81 0.03 13.56 0.04 0 + 839 130.84321 19.200204 -38.0 4.1 -16.1 4.1 18.25 0.01 17.00 0.00 15.66 0.00 15.05 0.00 14.75 0.00 13.57 0.03 12.94 0.03 12.65 0.03 0 + 840 130.84348 20.907018 -38.6 5.6 -10.9 5.6 20.92 0.05 19.62 0.02 17.83 0.01 17.01 0.00 16.63 0.01 15.29 0.05 14.64 0.06 14.34 0.08 0 + 841 130.84390 21.671656 -38.1 1.3 -16.0 1.3 10.77 0.60 10.39 0.60 10.27 0.60 10.24 0.60 10.23 0.60 9.33 0.02 8.98 0.02 8.97 0.02 1 BD+22 1980 + 842 130.84627 19.785973 -31.1 4.1 -6.6 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.09 0.04 14.47 0.06 14.20 0.07 1 + 843 130.84959 18.679167 -30.3 4.1 -13.9 4.1 17.17 0.00 15.97 0.00 14.95 0.60 14.40 0.00 14.16 0.00 13.02 0.02 12.34 0.02 12.16 0.02 0 + 844 130.85413 20.565339 -39.2 4.1 -12.0 4.1 15.82 0.00 14.52 0.00 13.82 0.00 13.49 0.00 13.35 0.00 12.27 0.02 11.57 0.02 11.40 0.01 0 + 845 130.86488 19.827923 -39.9 4.1 -11.6 4.1 18.71 0.01 17.54 0.00 16.03 0.00 15.34 0.00 15.04 0.00 13.78 0.03 13.18 0.03 12.92 0.03 1 + 846 130.87931 18.548516 -36.0 4.1 -12.4 4.1 14.48 0.00 13.67 0.60 13.19 0.60 12.88 0.60 12.59 0.00 11.52 0.02 10.91 0.02 10.74 0.01 0 + 847 130.88487 19.743830 -39.8 2.2 -15.8 2.2 12.87 0.60 12.24 0.60 11.98 0.60 11.83 0.60 11.77 0.60 10.76 0.02 10.32 0.02 10.22 0.02 1 + 848 130.88582 19.992489 -41.1 3.9 -18.9 3.9 18.71 0.01 17.47 0.01 15.99 0.00 15.35 0.00 15.02 0.00 13.77 0.02 13.13 0.03 12.92 0.03 1 + 849 130.89052 19.406867 -28.4 4.1 -13.3 4.1 19.52 0.02 18.21 0.01 16.54 0.00 15.77 0.00 15.37 0.01 14.13 0.03 13.58 0.04 13.24 0.03 0 + 850 130.89416 18.753821 -33.4 4.1 -18.0 4.1 19.34 0.02 18.07 0.01 16.52 0.00 15.81 0.00 15.41 0.00 14.17 0.03 13.56 0.04 13.30 0.03 0 + 851 130.89721 19.003923 -35.0 4.1 -13.1 4.1 17.30 0.00 16.07 0.00 14.78 0.00 14.14 0.00 13.86 0.00 12.65 0.02 12.00 0.02 11.79 0.02 1 + 852 130.89793 19.456524 -38.5 3.9 -20.7 3.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.53 0.03 13.93 0.03 13.60 0.04 0 + 853 130.89806 20.189678 -39.6 1.2 -13.4 1.2 10.31 0.60 10.06 0.60 10.01 0.60 10.03 0.60 10.05 0.60 9.23 0.02 9.02 0.04 8.92 0.02 1 BD+20 2196 + 854 130.90235 21.319335 -37.4 4.1 -20.6 4.1 18.74 0.01 17.56 0.01 16.09 0.00 15.42 0.00 15.12 0.00 13.88 0.03 13.26 0.03 13.04 0.02 0 + 855 130.90342 20.540090 -36.2 4.1 -14.9 4.1 18.25 0.01 17.00 0.00 15.61 0.00 14.98 0.00 14.68 0.00 13.50 0.03 12.83 0.03 12.59 0.02 0 + 856 130.90361 20.537198 -40.5 4.1 -16.4 4.1 16.87 0.00 15.65 0.00 14.65 0.00 14.21 0.00 14.00 0.00 12.84 0.02 12.19 0.02 11.94 0.01 0 + 857 130.91159 22.269268 -39.5 1.6 -13.2 1.6 12.48 0.60 11.97 0.60 11.79 0.60 11.73 0.60 11.68 0.60 10.74 0.02 10.30 0.02 10.25 0.02 1 + 858 130.91381 18.425573 -36.1 4.1 -11.1 4.1 18.02 0.01 16.74 0.00 15.64 0.60 14.93 0.00 14.66 0.00 13.41 0.02 12.85 0.03 12.54 0.02 0 + 859 130.92938 17.908298 -38.7 4.0 -16.0 4.0 15.76 0.00 14.57 0.00 13.79 0.00 13.47 0.00 13.29 0.00 12.21 0.02 11.50 0.03 11.31 0.02 0 + 860 130.93143 19.075875 -32.5 6.7 -11.9 6.7 14.05 0.00 12.99 0.00 13.55 0.00 12.54 0.00 12.45 0.00 11.44 0.02 10.88 0.02 10.79 0.02 1 + 861 130.93629 19.066310 -36.0 4.1 -12.1 4.1 16.69 0.00 15.47 0.00 14.53 0.00 14.09 0.00 13.90 0.00 12.78 0.02 12.08 0.02 11.87 0.02 0 + 862 130.93631 21.209506 -38.1 4.1 -16.9 4.1 15.90 0.00 14.71 0.00 14.07 0.60 13.59 0.00 13.35 0.00 12.24 0.02 11.56 0.02 11.41 0.02 1 + 863 130.94385 17.156750 -42.0 2.2 -7.2 2.2 13.18 0.60 12.48 0.60 12.22 0.60 12.09 0.60 12.00 0.60 10.96 0.03 10.42 0.04 10.35 0.02 0 + 864 130.94726 18.050003 -27.7 4.1 -11.8 4.1 19.28 0.02 18.12 0.01 16.53 0.00 15.74 0.00 15.37 0.00 14.09 0.03 13.49 0.03 13.22 0.03 0 + 865 130.95056 18.800779 -35.4 1.4 -10.6 1.4 10.32 0.60 10.08 0.60 10.02 0.60 10.02 0.60 10.03 0.60 9.19 0.02 8.97 10.00 8.93 0.02 1 BD+19 2089 + 866 130.96007 17.418831 -32.9 4.0 -17.2 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.83 0.04 14.22 0.04 13.92 0.04 0 + 867 130.96177 20.365741 -35.7 4.1 -7.9 4.1 16.22 0.00 15.00 0.00 14.12 0.00 13.79 0.00 13.54 0.00 12.40 0.02 11.76 0.02 11.51 0.02 1 + 868 130.96576 17.655603 -34.1 3.8 -6.1 3.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.09 0.03 13.41 0.03 13.20 0.02 0 + 869 130.96580 19.913577 -42.1 4.1 -16.3 4.1 14.80 0.00 13.73 0.60 13.33 0.60 13.01 0.00 12.87 0.00 11.82 0.02 11.09 0.02 10.97 0.02 0 + 870 130.97775 18.893580 -34.7 4.1 -16.0 4.1 13.28 0.60 12.60 0.60 12.28 0.60 12.09 0.60 12.01 0.60 10.96 0.02 10.52 0.02 10.41 0.02 0 + 871 130.98597 19.271685 -32.1 5.6 -15.8 5.6 21.37 0.05 19.84 0.03 18.07 0.01 17.24 0.00 16.83 0.01 15.43 0.06 14.94 0.08 14.61 0.08 1 + 872 130.98628 19.725630 -39.8 4.1 -14.6 4.1 13.67 0.00 13.03 0.60 12.66 0.60 12.45 0.60 12.24 0.00 11.24 0.02 10.69 0.02 10.53 0.02 0 + 873 130.99686 19.316131 -33.7 4.1 -13.8 4.1 17.42 0.00 16.22 0.00 15.04 0.00 14.51 0.00 14.26 0.00 13.08 0.02 12.49 0.03 12.16 0.02 1 + 874 131.02903 19.791695 -33.1 4.1 -11.8 4.1 18.58 0.01 17.30 0.00 15.76 0.00 15.08 0.00 14.76 0.00 13.51 0.02 12.92 0.03 12.65 0.03 0 + 875 131.04542 21.793947 -40.1 4.1 -15.6 4.1 18.27 0.01 17.11 0.00 15.71 0.00 15.08 0.00 14.80 0.00 13.60 0.03 12.96 0.02 12.72 0.03 0 + 876 131.04840 20.216674 -32.4 4.1 -19.1 4.1 19.00 0.01 17.76 0.00 16.15 0.00 15.41 0.00 15.08 0.00 13.79 0.03 13.19 0.02 12.86 0.03 0 + 877 131.04989 17.902222 -36.4 1.4 -9.2 1.5 10.11 0.60 9.88 0.60 9.83 0.60 9.85 0.60 9.87 0.60 9.05 0.02 8.84 0.04 8.78 0.02 1 BD+18 2026 + 878 131.05509 18.819835 -41.3 3.9 -11.6 3.9 14.69 0.00 13.64 0.00 13.44 0.60 12.93 0.00 12.79 0.00 11.75 0.02 11.13 0.03 10.94 0.02 1 + 879 131.07104 18.736632 -35.5 2.1 -11.6 2.1 12.70 0.60 12.13 0.60 11.91 0.60 11.80 0.60 11.74 0.60 10.77 0.02 10.35 0.02 10.26 0.02 1 + 880 131.07596 20.830101 -28.4 4.1 -14.5 4.1 19.83 0.01 18.49 0.01 16.81 0.60 16.03 0.00 15.68 0.00 14.37 0.03 13.72 0.03 13.46 0.04 0 + 881 131.07962 18.936085 -38.0 4.1 -14.9 4.1 17.70 0.00 16.49 0.00 15.13 0.00 14.47 0.00 14.20 0.00 12.97 0.02 12.39 0.03 12.06 0.02 0 + 882 131.08841 19.936566 -37.2 4.1 -21.2 4.1 19.36 0.01 18.06 0.01 16.46 0.00 15.73 0.00 15.40 0.00 14.11 0.02 13.55 0.02 13.26 0.03 0 + 883 131.09406 18.385898 -35.0 4.1 -13.0 4.1 18.77 0.01 17.05 0.60 16.19 0.60 15.36 0.00 15.09 0.01 13.85 0.02 13.21 0.03 12.94 0.03 1 + 884 131.09662 20.232116 -37.6 4.1 -20.8 4.1 19.36 0.01 18.10 0.01 16.57 0.00 15.89 0.00 15.56 0.00 14.32 0.03 13.74 0.04 13.35 0.03 0 + 885 131.10549 19.808472 -28.0 4.1 -10.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.71 0.04 14.06 0.03 13.88 0.05 0 + 886 131.11330 18.872423 -34.9 4.1 -21.9 4.1 19.91 0.02 18.55 0.01 16.95 0.00 16.20 0.00 15.83 0.01 14.58 0.04 13.91 0.04 13.64 0.04 0 + 887 131.11533 18.969349 -37.4 4.1 -13.3 4.1 18.65 0.01 17.46 0.00 16.00 0.00 15.33 0.00 15.00 0.00 13.78 0.03 13.18 0.03 12.87 0.03 0 + 888 131.13264 19.554795 -34.9 4.1 -17.5 4.1 18.83 0.01 17.64 0.01 16.06 0.00 15.44 0.00 15.13 0.00 13.84 0.03 13.24 0.03 12.96 0.03 0 + 889 131.13412 21.403794 -41.3 4.1 -6.0 4.1 16.51 0.00 15.27 0.00 14.29 0.60 13.62 0.00 13.37 0.00 12.02 0.02 11.48 0.02 11.22 0.02 0 + 890 131.13570 21.683062 -41.5 4.1 -12.5 4.1 18.01 0.00 16.78 0.00 15.45 0.00 14.82 0.00 14.53 0.00 13.33 0.02 12.71 0.02 12.45 0.03 0 + 891 131.13697 18.964016 -38.8 4.1 -14.9 4.1 17.97 0.01 16.77 0.00 15.42 0.00 14.80 0.00 14.51 0.00 13.31 0.02 12.66 0.03 12.39 0.02 1 + 892 131.14362 20.341500 -32.6 4.1 -15.5 4.1 18.94 0.01 17.73 0.00 16.30 0.00 15.63 0.00 15.33 0.00 14.10 0.03 13.51 0.03 13.26 0.03 0 + 893 131.15047 18.599185 -39.0 4.1 -12.7 4.1 15.13 0.00 13.89 0.00 13.30 0.60 12.81 0.00 12.63 0.00 11.52 0.02 10.82 0.02 10.65 0.01 0 + 894 131.15430 19.710846 -34.7 1.2 -14.3 1.3 10.68 0.60 10.35 0.60 10.27 0.60 10.27 0.60 10.27 0.60 9.42 0.02 9.10 0.02 9.05 0.01 1 BD+20 2201 + 895 131.16861 21.764907 -39.0 4.1 -19.5 4.1 17.79 0.01 16.59 0.00 15.30 0.00 14.70 0.00 14.44 0.00 13.23 0.02 12.61 0.02 12.34 0.02 1 + 896 131.16971 20.193614 -37.9 4.1 -15.8 4.1 14.96 0.00 13.82 0.00 13.51 0.60 13.07 0.00 12.93 0.00 11.88 0.02 11.22 0.02 11.07 0.02 1 + 897 131.17812 20.963019 -42.4 4.1 -20.0 4.1 18.92 0.01 17.67 0.00 16.08 0.00 15.33 0.00 14.99 0.00 13.72 0.02 13.12 0.03 12.84 0.03 1 + 898 131.18660 19.406149 -36.3 4.1 -13.6 4.1 18.05 0.01 16.77 0.00 15.37 0.00 14.73 0.00 14.44 0.00 13.25 0.02 12.63 0.03 12.34 0.02 0 + 899 131.19332 22.205464 -40.0 5.9 -16.8 5.9 21.62 0.10 19.46 0.60 18.47 0.01 17.73 0.02 17.31 0.01 15.95 0.10 15.23 0.10 15.32 0.17 0 + 900 131.20296 20.290552 -38.6 4.1 -14.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.08 0.02 10.58 0.02 10.51 0.02 0 + 901 131.22143 16.966932 -31.7 5.5 -16.9 5.5 19.73 0.02 18.44 0.01 16.83 0.00 16.10 0.00 15.77 0.00 14.53 0.03 13.89 0.04 13.68 0.04 1 + 902 131.22435 21.618460 -37.6 4.1 -16.2 4.1 17.09 0.00 15.86 0.00 14.79 0.00 14.29 0.00 14.07 0.00 12.88 0.02 12.26 0.02 12.01 0.02 0 + 903 131.23261 20.931026 -28.7 4.1 -10.2 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.71 0.02 13.08 0.03 12.84 0.03 0 + 904 131.23508 18.371397 -39.2 4.1 -5.5 4.1 16.51 0.00 15.25 0.00 14.51 0.60 13.87 0.00 13.63 0.00 12.47 0.02 11.82 0.02 11.60 0.02 1 + 905 131.26090 20.512134 -28.8 4.1 -9.3 4.1 15.90 0.00 14.66 0.00 13.72 0.00 13.28 0.00 13.10 0.00 11.95 0.02 11.33 0.02 11.10 0.02 1 + 906 131.26752 20.357704 -39.1 1.3 -14.8 1.3 10.56 0.60 10.31 0.60 10.24 0.60 10.22 0.60 10.22 0.60 9.39 0.02 9.17 0.02 9.13 0.02 0 BD+20 2204 + 907 131.27450 19.299304 -33.5 4.1 -16.0 4.1 19.51 0.01 18.27 0.01 16.73 0.00 16.02 0.00 15.68 0.00 14.40 0.03 13.86 0.04 13.55 0.04 0 + 908 131.27534 20.453661 -35.4 5.7 -12.4 5.7 21.59 0.06 20.30 0.02 18.42 0.01 17.49 0.00 17.06 0.01 15.61 0.07 15.31 0.09 14.68 0.09 0 + 909 131.28053 20.394959 -39.8 1.6 -17.3 1.6 12.25 0.00 11.98 0.00 99.00 99.00 12.25 0.00 11.43 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 910 131.28063 20.395050 -39.8 1.6 -17.3 1.6 12.33 0.60 11.82 0.60 11.61 0.60 11.50 0.60 11.46 0.60 10.50 0.02 10.14 0.02 10.05 0.02 0 + 911 131.28553 19.152267 -37.0 3.9 -14.2 3.9 19.81 0.02 18.43 0.01 17.06 0.60 16.15 0.00 15.79 0.01 14.52 0.03 13.91 0.03 13.58 0.04 1 + 912 131.30460 19.686876 -36.9 1.5 -14.0 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.56 0.02 9.27 0.02 9.16 0.02 1 + 913 131.31118 20.997580 -40.3 1.1 -13.5 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.72 0.02 8.54 0.01 8.48 0.02 0 HD 74547 + 914 131.31214 18.761064 -31.1 4.1 -12.7 4.1 19.41 0.01 18.39 0.04 16.62 0.60 15.86 0.00 15.51 0.00 14.24 0.03 13.67 0.03 13.41 0.04 0 + 915 131.31477 21.059967 -36.1 4.1 -11.6 4.1 18.10 0.01 16.88 0.00 15.86 0.60 15.02 0.00 14.75 0.00 13.55 0.02 12.93 0.03 12.65 0.02 0 + 916 131.32505 18.890390 -37.8 1.1 -13.8 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.80 0.01 8.64 0.02 8.60 0.02 0 BD+19 2093 + 917 131.32981 19.002992 -36.6 4.1 -15.6 4.1 14.41 0.00 13.57 0.60 13.17 0.60 12.93 0.60 12.67 0.00 11.65 0.02 11.05 0.02 10.90 0.01 1 + 918 131.33556 18.875380 -36.3 1.1 -10.0 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.91 0.01 7.86 0.01 7.84 0.01 0 HD 74589 + 919 131.33765 18.825746 -40.0 5.5 -11.5 5.5 20.74 0.05 19.37 0.01 17.53 0.60 16.88 0.00 16.51 0.01 15.25 0.05 14.49 0.06 14.22 0.06 0 + 920 131.33811 18.884270 -30.4 5.5 -18.3 5.5 19.47 0.02 18.36 0.01 16.61 0.60 16.02 0.00 15.75 0.00 14.50 0.03 13.80 0.03 13.63 0.04 0 + 921 131.34302 19.827790 -39.4 3.9 -15.0 3.9 18.52 0.01 17.25 0.00 15.68 0.00 14.98 0.00 14.62 0.00 13.35 0.02 12.77 0.02 12.50 0.02 0 + 922 131.34325 19.035620 -33.4 3.9 -16.6 3.9 18.81 0.01 17.57 0.01 16.15 0.60 15.29 0.00 14.94 0.00 13.70 0.02 13.09 0.02 12.83 0.02 0 + 923 131.35700 18.713344 -39.2 3.9 -13.3 3.9 13.29 0.00 12.54 0.00 99.00 99.00 12.24 0.00 12.09 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 924 131.35817 20.427359 -44.9 4.1 -15.8 4.1 14.78 0.00 13.64 0.00 13.31 0.60 12.96 0.00 12.76 0.00 11.68 0.02 11.04 0.02 10.88 0.02 1 + 925 131.35818 20.422915 -39.6 4.1 -15.4 4.1 15.38 0.00 14.19 0.00 13.56 0.00 13.32 0.00 13.14 0.00 12.05 0.02 11.38 0.02 11.21 0.02 0 + 926 131.35845 19.698447 -35.2 3.9 -15.6 3.9 17.22 0.00 16.05 0.00 14.94 0.00 14.44 0.00 14.20 0.00 13.05 0.02 12.39 0.02 12.18 0.02 1 + 927 131.35954 19.784405 -34.0 3.9 -12.8 3.9 17.11 0.00 15.88 0.00 14.63 0.00 14.06 0.00 13.79 0.00 12.62 0.02 12.02 0.02 11.72 0.02 1 + 928 131.36090 19.236867 -36.1 3.9 -16.8 3.9 19.29 0.01 17.67 0.60 16.37 0.60 15.57 0.00 15.20 0.00 13.92 0.02 13.28 0.03 13.04 0.03 0 + 929 131.36648 21.653565 -35.2 0.9 -16.3 0.9 10.83 0.60 10.54 0.60 10.44 0.60 10.41 0.60 10.41 0.60 9.55 0.02 9.32 0.02 9.26 0.02 0 BD+22 1985 + 930 131.36683 21.800808 -37.3 4.1 -22.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.13 0.02 13.52 0.03 13.24 0.03 0 + 931 131.36772 20.395433 -38.5 0.8 -13.7 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.01 0.01 7.91 0.01 7.86 0.01 0 HD 74587 + 932 131.36906 18.996694 -33.0 5.6 -14.5 5.6 20.88 0.05 19.54 0.01 99.00 99.00 16.94 0.00 16.55 0.01 99.00 99.00 99.00 99.00 99.00 99.00 0 + 933 131.37705 20.590157 -37.3 0.8 -14.1 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.98 0.01 8.82 0.04 8.77 0.02 1 BD+21 1904 + 934 131.38152 20.751987 -33.9 4.1 -19.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.14 0.03 13.43 0.02 13.30 0.03 1 + 935 131.38399 18.964479 -29.3 3.9 -15.2 3.9 18.53 0.01 17.17 0.60 16.22 0.60 15.31 0.00 15.02 0.00 13.79 0.02 13.21 0.03 12.92 0.03 0 + 936 131.40092 21.255838 -38.4 4.1 -15.7 4.1 17.14 0.00 15.97 0.00 15.03 0.60 14.34 0.00 14.10 0.00 12.90 0.02 12.27 0.02 12.04 0.02 0 + 937 131.40350 18.598671 -32.2 3.7 -17.7 3.7 17.83 0.01 16.54 0.00 15.30 0.60 14.51 0.00 14.21 0.00 12.99 0.02 12.32 0.02 12.06 0.02 0 + 938 131.40358 18.723635 -31.3 3.7 -12.4 3.7 19.86 0.02 18.49 0.01 17.07 0.60 16.15 0.00 15.83 0.01 14.54 0.03 13.98 0.04 13.68 0.04 0 + 939 131.41861 20.173766 -36.5 3.9 -21.5 3.9 17.95 0.01 16.74 0.00 15.50 0.00 14.93 0.00 14.65 0.00 13.48 0.02 12.82 0.03 12.59 0.02 0 + 940 131.43522 19.675665 -35.8 3.7 -19.9 3.7 17.94 0.01 16.76 0.00 15.42 0.00 14.78 0.00 14.48 0.00 13.27 0.02 12.66 0.02 12.42 0.02 0 + 941 131.44111 20.494709 -39.3 4.1 -16.0 4.1 17.23 0.00 16.00 0.00 14.90 0.00 14.38 0.00 14.15 0.00 12.93 0.02 12.33 0.03 12.10 0.02 0 + 942 131.44390 19.049486 -40.7 0.9 -13.5 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.58 0.01 7.56 0.01 7.53 0.02 0 HD 74656 + 943 131.45272 21.532346 -32.6 4.1 -19.6 4.1 18.23 0.01 17.06 0.00 99.00 99.00 14.97 0.00 14.70 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 944 131.46413 19.424228 -39.0 3.7 -20.9 3.7 17.38 0.00 16.20 0.00 15.02 0.00 14.48 0.00 14.23 0.00 13.10 0.02 12.40 0.02 12.18 0.02 0 + 945 131.46750 18.926319 -36.3 3.7 -9.0 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.35 0.03 13.68 0.02 13.49 0.04 0 + 946 131.46989 19.316835 -41.4 3.7 -16.4 3.7 17.16 0.00 15.95 0.00 14.87 0.00 14.39 0.00 14.13 0.00 12.95 0.02 12.36 0.02 12.10 0.02 1 + 947 131.49643 19.253454 -42.2 3.7 -10.9 3.7 18.68 0.01 17.42 0.01 16.23 0.60 15.32 0.00 14.99 0.00 13.77 0.02 13.15 0.03 12.88 0.03 1 + 948 131.50000 17.827418 -38.0 3.7 -12.5 3.7 19.19 0.01 17.88 0.01 16.36 0.00 15.65 0.00 15.26 0.00 14.06 0.03 13.41 0.03 13.16 0.03 1 + 949 131.50930 19.116543 -34.2 3.7 -15.1 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.27 0.02 12.65 0.02 12.37 0.02 1 + 950 131.51127 17.021543 -37.4 3.7 -14.2 3.7 18.82 0.01 17.54 0.00 16.18 0.00 15.54 0.00 15.25 0.00 14.01 0.02 13.41 0.03 13.23 0.03 1 + 951 131.51318 19.529757 -40.1 3.7 -15.0 3.7 18.04 0.01 16.81 0.00 15.50 0.00 14.90 0.00 14.62 0.00 13.41 0.02 12.76 0.03 12.54 0.02 0 + 952 131.53395 18.040904 -34.2 5.0 -16.0 5.0 20.14 0.02 18.86 0.02 17.15 0.00 16.37 0.00 16.00 0.01 14.72 0.03 14.12 0.04 13.85 0.04 1 + 953 131.53818 21.607974 -38.5 4.1 -19.1 4.1 19.33 0.01 18.06 0.00 16.65 0.60 15.75 0.00 15.39 0.00 14.15 0.02 13.45 0.03 13.18 0.03 0 + 954 131.54198 19.528841 -41.1 3.7 -16.0 3.7 15.70 0.00 14.50 0.00 13.79 0.00 13.50 0.00 13.33 0.00 12.23 0.02 11.54 0.02 11.37 0.02 1 + 955 131.54881 20.633520 -33.3 4.1 -15.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.54 0.02 13.02 0.03 12.73 0.02 0 + 956 131.54890 18.179728 -35.3 1.3 -8.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.77 0.01 7.69 0.01 7.66 0.01 0 HD 74720 + 957 131.55448 20.728682 -36.4 4.1 -12.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.12 0.02 10.63 0.02 10.49 0.01 0 + 958 131.55743 20.856876 -37.8 4.1 -18.0 4.1 14.69 0.00 13.58 0.00 13.36 0.60 12.98 0.00 12.74 0.00 11.66 0.02 11.03 0.02 10.86 0.02 0 + 959 131.56442 19.709164 -37.7 0.8 -12.8 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.89 0.00 7.82 0.01 7.79 0.01 0 HD 74718 + 960 131.56539 18.097196 -32.1 5.0 -15.1 5.0 20.67 0.05 19.45 0.02 17.68 0.01 16.89 0.00 16.52 0.01 15.32 0.05 14.69 0.05 14.34 0.07 0 + 961 131.57603 18.719216 -30.4 3.7 -12.6 3.7 19.25 0.01 17.84 0.01 16.47 0.60 15.67 0.00 15.35 0.00 14.10 0.03 13.46 0.03 13.26 0.04 0 + 962 131.58348 21.008887 -31.2 5.5 -13.3 5.5 20.21 0.02 18.90 0.01 17.20 0.01 16.37 0.00 16.01 0.00 14.73 0.04 14.03 0.05 13.85 0.05 1 + 963 131.59942 19.967846 -37.6 3.7 -11.9 3.7 18.22 0.00 17.02 0.00 15.89 0.02 15.04 0.00 14.72 0.00 13.46 0.02 12.85 0.02 12.61 0.02 0 + 964 131.60132 20.527418 -32.6 4.1 -12.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.03 0.04 14.36 0.04 14.07 0.06 0 + 965 131.60959 17.845683 -43.6 3.7 -14.2 3.7 19.78 0.02 18.45 0.01 16.82 0.00 16.08 0.00 15.71 0.00 14.46 0.03 13.82 0.04 13.51 0.04 0 + 966 131.61414 19.209026 -32.1 5.0 -17.2 5.0 20.56 0.04 18.67 0.60 17.34 0.60 16.52 0.00 16.13 0.01 14.87 0.04 14.22 0.05 13.92 0.05 0 + 967 131.61494 18.118523 -42.4 3.7 -11.7 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.90 0.03 13.35 0.03 13.09 0.03 0 + 968 131.63757 18.906698 -36.2 3.7 -11.0 3.7 13.68 0.00 13.24 0.60 12.82 0.60 12.55 0.60 12.30 0.00 11.30 0.02 10.77 0.02 10.62 0.01 0 + 969 131.63866 18.760958 -37.0 0.9 -11.1 0.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.44 0.02 8.31 0.02 8.25 0.01 0 HD 74780 + 970 131.63971 18.235997 -35.9 3.7 -9.0 3.7 13.65 0.00 13.06 0.60 12.69 0.60 12.46 0.60 12.32 0.00 11.25 0.02 10.72 0.03 10.60 0.02 0 + 971 131.64520 19.257207 -40.0 3.7 -21.3 3.7 18.61 0.01 17.38 0.60 16.11 0.60 15.29 0.00 15.00 0.00 13.75 0.02 13.12 0.02 12.87 0.02 0 + 972 131.65281 17.259150 -31.2 3.7 -8.8 3.7 19.42 0.01 18.27 0.01 16.61 0.00 15.86 0.00 15.49 0.00 14.20 0.02 13.62 0.03 13.32 0.03 0 + 973 131.65323 19.226017 -30.8 3.7 -14.6 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.76 0.02 13.11 0.02 12.90 0.03 0 + 974 131.65913 19.879103 -30.1 3.6 -14.8 3.6 16.80 0.00 15.54 0.00 14.37 0.00 13.84 0.00 13.57 0.00 12.40 0.03 11.73 0.03 11.51 0.02 0 + 975 131.66218 19.619091 -40.2 3.6 -15.8 3.6 18.60 0.01 17.41 0.00 15.96 0.00 15.31 0.00 14.99 0.00 13.77 0.03 13.14 0.03 12.93 0.03 0 + 976 131.66791 19.275080 -40.0 3.6 -21.1 3.6 20.00 0.03 18.69 0.01 17.23 0.60 16.27 0.00 15.90 0.01 14.65 0.04 14.03 0.04 13.70 0.03 0 + 977 131.67829 19.426206 -40.7 3.6 -16.0 3.6 19.03 0.01 17.74 0.01 16.20 0.60 15.58 0.00 15.26 0.00 13.94 0.02 13.34 0.04 13.16 0.03 1 + 978 131.69710 19.644806 -38.0 1.3 -15.1 1.3 10.96 0.60 10.67 0.60 10.56 0.60 10.51 0.60 10.49 0.60 9.62 0.02 9.42 0.03 9.34 0.02 0 + 979 131.70871 21.020205 -37.1 2.2 -15.8 2.2 13.48 0.00 12.95 0.60 12.57 0.60 12.34 0.60 12.15 0.00 11.14 0.02 10.67 0.03 10.54 0.02 0 + 980 131.72367 19.049141 -38.2 4.8 -15.6 4.8 19.91 0.02 18.57 0.01 17.02 0.60 16.15 0.00 15.79 0.01 14.55 0.03 13.85 0.04 13.62 0.04 0 + 981 131.74762 19.916491 -36.7 4.9 -17.3 4.9 21.09 0.03 19.79 0.01 17.99 0.01 17.15 0.00 16.73 0.01 15.48 0.05 14.78 0.07 14.47 0.07 0 + 982 131.77058 18.928523 -36.3 4.8 -9.9 4.8 19.57 0.02 18.39 0.01 16.92 0.60 15.98 0.00 15.62 0.01 14.40 0.03 13.73 0.04 13.48 0.03 0 + 983 131.77175 19.036114 -34.7 4.9 -10.5 4.9 20.88 0.05 19.52 0.02 17.98 0.01 16.93 0.00 16.52 0.01 15.33 0.05 14.51 0.05 14.27 0.06 0 + 984 131.78785 18.193691 -37.5 3.6 -13.0 3.6 17.76 0.00 16.50 0.00 15.29 0.00 14.73 0.00 14.47 0.00 13.25 0.02 12.62 0.03 12.42 0.02 0 + 985 131.82542 20.485814 -34.5 4.0 -18.5 4.0 17.16 0.00 15.96 0.00 14.88 0.00 14.46 0.00 14.22 0.00 13.09 0.03 12.44 0.03 12.19 0.02 1 + 986 131.82939 21.183901 -39.9 5.3 -16.5 5.3 19.79 0.02 18.54 0.01 16.95 0.00 16.18 0.00 15.84 0.01 14.55 0.03 13.92 0.04 13.64 0.04 0 + 987 131.83071 19.214452 -34.9 3.6 -21.4 3.6 19.07 0.01 17.80 0.60 16.49 0.60 15.63 0.00 15.29 0.01 14.07 0.03 13.40 0.04 13.15 0.03 0 + 988 131.84004 20.656104 -40.7 4.0 -19.2 4.0 16.69 0.00 15.42 0.00 14.44 0.00 14.02 0.00 13.80 0.00 12.68 0.02 12.03 0.03 11.76 0.02 1 + 989 131.85583 19.643591 -33.0 3.6 -8.3 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.79 0.02 13.18 0.03 12.90 0.03 0 + 990 131.89366 17.630684 -35.5 3.7 -18.9 3.7 18.16 0.01 16.92 0.00 15.64 0.00 15.08 0.00 14.80 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 991 131.89443 19.138278 -37.4 3.6 -11.1 3.6 16.39 0.00 15.16 0.00 14.17 0.00 13.70 0.00 13.47 0.00 12.33 0.03 11.67 0.03 11.41 0.02 0 + 992 131.89895 21.926775 -37.5 2.2 -11.2 2.2 12.34 0.60 11.88 0.60 11.75 0.60 11.71 0.60 11.68 0.60 10.77 0.02 10.37 0.03 10.32 0.02 0 + 993 131.92408 18.941286 -35.8 3.6 -13.5 3.6 18.17 0.01 16.97 0.00 15.60 0.00 14.97 0.00 14.65 0.00 13.41 0.02 12.79 0.03 12.55 0.03 0 + 994 131.93787 18.356636 -37.4 3.6 -13.9 3.6 17.14 0.00 15.91 0.00 14.58 0.00 14.08 0.00 13.80 0.00 12.62 0.02 12.01 0.03 11.74 0.02 1 + 995 131.95349 18.605421 -38.9 3.6 -15.4 3.6 18.79 0.01 17.33 0.60 16.05 0.60 15.25 0.00 14.89 0.00 13.63 0.03 13.00 0.03 12.77 0.03 0 + 996 131.96041 19.153042 -31.6 3.6 -18.7 3.6 19.47 0.02 18.15 0.01 16.65 0.00 15.92 0.00 15.59 0.00 14.36 0.03 13.81 0.05 13.57 0.05 0 + 997 131.97423 19.131467 -28.3 3.6 -14.4 3.6 19.54 0.02 18.25 0.01 16.73 0.00 15.96 0.00 15.62 0.00 14.38 0.03 13.81 0.05 13.54 0.04 0 + 998 131.98889 19.513580 -38.6 4.9 -16.2 4.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.01 0.02 13.43 0.04 13.18 0.03 0 + 999 132.00089 20.401886 -39.2 4.1 -18.9 4.1 19.91 0.03 18.65 0.01 17.14 0.01 16.21 0.00 15.84 0.01 14.54 0.03 13.90 0.04 13.69 0.04 0 +1000 132.00715 18.677109 -36.3 1.1 -12.2 1.0 10.93 0.60 10.47 0.60 10.28 0.60 10.19 0.60 10.16 0.60 9.23 0.02 8.91 0.03 8.81 0.02 1 FV Cnc +1001 132.02054 19.470488 -40.1 3.6 -21.4 3.6 17.85 0.01 16.62 0.00 15.38 0.00 14.82 0.00 14.56 0.00 13.37 0.02 12.73 0.03 12.54 0.03 0 +1002 132.03096 19.916423 -36.6 3.6 -21.9 3.6 19.33 0.01 18.22 0.03 16.61 0.00 15.89 0.00 15.57 0.01 14.38 0.03 13.73 0.04 13.40 0.04 0 +1003 132.04785 18.191128 -41.1 3.6 -17.8 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.30 0.03 12.66 0.04 12.40 0.02 0 +1004 132.04808 21.443084 -38.1 5.4 -6.0 5.4 20.74 0.03 19.40 0.02 17.67 0.01 16.90 0.00 16.57 0.01 15.28 0.05 14.79 0.07 14.48 0.06 1 +1005 132.07279 17.540796 -44.2 3.7 -11.0 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.58 0.02 10.99 0.02 10.84 0.02 0 +1006 132.09381 18.612435 -33.1 3.6 -7.1 3.6 17.45 0.00 16.26 0.00 15.10 0.00 14.59 0.00 14.32 0.00 13.17 0.02 12.58 0.03 12.32 0.02 0 +1007 132.09814 19.836573 -39.6 3.6 -15.6 3.6 18.47 0.01 17.19 0.00 15.84 0.00 15.22 0.00 14.92 0.00 13.72 0.02 13.13 0.03 12.84 0.02 0 +1008 132.11589 18.345526 -36.2 1.6 -9.7 1.6 11.58 0.60 11.19 0.60 11.08 0.60 11.07 0.60 11.05 0.60 10.16 0.02 9.82 0.03 9.77 0.02 0 +1009 132.12522 18.950947 -38.3 4.9 -9.5 4.9 21.06 0.05 19.75 0.02 17.89 0.01 17.07 0.00 16.63 0.01 15.32 0.04 14.62 0.06 14.36 0.06 0 +1010 132.14370 19.932611 -40.7 3.6 -13.8 3.6 17.09 0.00 15.87 0.00 14.73 0.00 14.22 0.00 13.98 0.00 12.76 0.02 12.16 0.03 11.89 0.02 1 +1011 132.17179 19.497593 -33.7 3.6 -10.9 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.82 0.04 14.16 0.05 13.90 0.04 1 +1012 132.20464 20.224201 -43.7 3.6 -16.1 3.6 16.63 0.00 15.53 0.01 14.75 0.01 14.06 0.00 13.86 0.00 12.71 0.02 12.06 0.03 11.83 0.02 1 +1013 132.20815 20.443312 -38.9 4.0 -17.6 4.0 16.69 0.00 15.44 0.00 14.48 0.00 14.06 0.00 13.86 0.00 12.76 0.02 12.05 0.03 11.86 0.02 0 +1014 132.21411 20.904259 -38.2 5.4 -10.9 5.4 20.80 0.04 19.38 0.02 17.50 0.60 16.81 0.00 16.42 0.01 15.11 0.04 14.52 0.06 14.30 0.06 0 +1015 132.21922 19.046391 -29.6 3.6 -8.4 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.68 0.03 13.04 0.03 12.82 0.02 1 +1016 132.24953 19.672588 -28.1 4.8 -13.5 4.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.79 0.03 14.18 0.05 13.79 0.05 0 +1017 132.26029 21.556666 -42.3 4.0 -19.5 4.0 18.40 0.01 17.16 0.00 15.65 0.00 14.95 0.00 14.61 0.00 13.34 0.02 12.69 0.03 12.45 0.02 0 +1018 132.27775 19.686445 -34.1 2.2 -12.8 2.2 12.33 0.00 12.57 0.00 99.00 99.00 11.53 0.00 11.45 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 +1019 132.31686 18.410137 -29.8 3.6 -13.3 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.40 0.03 13.85 0.04 13.56 0.04 0 +1020 132.31881 21.586718 -36.6 4.0 -8.6 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.41 0.02 11.83 0.03 11.53 0.02 1 +1021 132.36141 18.522101 -36.7 3.6 -14.9 3.6 16.02 0.00 14.75 0.00 13.94 0.00 13.58 0.00 13.39 0.00 12.24 0.02 11.59 0.03 11.39 0.02 0 +1022 132.39123 20.508083 -37.9 1.1 -12.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.53 0.01 8.42 0.04 8.37 0.01 0 HD 75248 +1023 132.41125 20.961109 -37.4 6.0 -17.9 6.0 21.82 0.08 20.48 0.03 18.64 0.01 17.90 0.01 17.44 0.02 16.11 0.09 15.50 0.15 15.32 0.17 0 +1024 132.49733 19.166941 -39.1 3.6 -19.3 3.6 18.77 0.01 17.57 0.00 16.10 0.00 15.39 0.00 15.06 0.00 13.87 0.03 13.19 0.04 13.01 0.03 0 +1025 132.49981 18.364986 -35.7 1.3 -12.2 1.3 11.52 0.00 12.73 0.00 12.32 0.00 10.80 0.00 10.77 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 BD+18 2049 +1026 132.49992 18.365058 -35.7 1.3 -12.2 1.3 11.40 0.60 11.05 0.60 10.93 0.60 10.90 0.60 10.89 0.60 10.02 0.02 9.73 0.03 9.64 0.02 0 +1027 132.52260 20.703691 -29.4 4.1 -11.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.29 0.05 14.61 0.07 14.36 0.07 1 +1028 132.54267 20.148646 -40.0 3.6 -15.9 3.6 15.60 0.00 14.37 0.00 13.67 0.00 13.44 0.00 13.23 0.00 12.11 0.02 11.48 0.03 11.25 0.02 0 +1029 132.54515 21.101212 -34.9 4.0 -9.6 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.90 0.03 13.24 0.04 13.05 0.03 1 +1030 132.54571 19.551449 -30.1 3.6 -19.8 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.58 0.03 13.94 0.04 13.69 0.04 0 +1031 132.55689 19.690012 -30.4 5.0 -14.9 5.0 21.54 0.06 20.20 0.04 18.30 0.01 17.39 0.00 16.97 0.01 15.75 0.06 14.95 0.07 14.51 0.07 0 +1032 132.55876 20.567427 -36.3 6.2 -12.4 6.2 22.33 0.17 20.86 0.05 19.04 0.02 18.28 0.01 17.94 0.02 16.45 0.11 16.06 0.17 15.80 0.21 0 +1033 132.57719 19.428511 -34.6 3.7 -14.3 3.7 13.40 0.60 12.72 0.60 12.40 0.60 12.21 0.60 12.14 0.00 11.09 0.02 10.66 0.02 10.53 0.02 0 +1034 132.59472 20.150953 -32.4 3.7 -6.0 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.69 0.03 14.04 0.04 13.77 0.04 0 +1035 132.64699 20.710466 -32.7 4.2 -5.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.37 0.03 13.66 0.03 13.40 0.04 0 +1036 132.65042 19.951848 -36.8 5.0 -16.1 5.0 20.78 0.04 21.33 0.60 19.57 0.60 16.84 0.00 16.47 0.01 15.08 0.04 14.53 0.05 14.33 0.06 0 +1037 132.70759 19.810125 -38.4 3.7 -18.3 3.7 18.55 0.01 17.23 0.00 15.74 0.00 15.08 0.00 14.78 0.00 13.53 0.02 12.91 0.02 12.65 0.02 0 +1038 132.73688 19.616067 -38.8 3.7 -14.3 3.7 18.20 0.01 16.95 0.00 15.68 0.00 15.02 0.00 14.78 0.00 13.57 0.02 12.94 0.02 12.69 0.03 0 +1039 132.84667 19.855061 -34.0 5.0 -21.9 5.0 20.77 0.05 19.41 0.02 17.61 0.60 16.97 0.00 16.65 0.01 15.23 0.04 14.71 0.05 14.45 0.06 1 +1040 132.85758 19.315661 -37.2 3.7 -20.0 3.7 18.09 0.01 16.83 0.00 15.43 0.00 14.79 0.00 14.49 0.00 13.30 0.02 12.68 0.02 12.40 0.02 0 diff --git a/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit b/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit new file mode 100644 index 00000000..77254ca3 --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit @@ -0,0 +1,2694 @@ +SIMPLE = T / Standard FITS Format BITPIX = 8 / Character data NAXIS = 0 / No Image --- just extension(s) EXTEND = T / There are standard extensions ORIGIN = 'xml2fits_v1.95' / Converted from XML-Astrores to FITS e-mail: question@simbad.u-strasbg.fr LONGSTRN= 'OGIP 1.0' / Long string convention (&/CONTINUE) may be usedDATE = '2026-01-30' / Written on 2026-01-30:20:36:47 (GMT) by: www-data@vizier.cds.unistra.fr ********************************************************** EXCERPT from catalogues stored in VizieR (CDS) with the following conditions: ********************************************************** VizieR Astronomical Server vizier.cds.unistra.fr Date: 2026-01-30T20:36:47 [V7.5.4] In case of problem, please report to: cds-question@unistra.fr INFO = 'service_protocol=ASU' / IVOID of the protocol through which the data was retrieved # INFO = 'request_date=2026-01-30T20:36:47' / Query execution date # INFO = 'request=https://vizier.cds.unistra.fr/viz-bin/asu-fits?-oc.form=dec&'CONTINUE '&-out.max=unlimited&#out.form=FITS (ascii) Table&-order=I&-out.src=&'CONTINUE 'J/MNRAS/422/1495/tablea1,J/MNRAS/422/1495/tablec1&-c.eq=J2000&-c.r=&'CONTINUE ' 2&-c.u=arcmin&-c.geom=r&-x.rs=10&-source=J/MNRAS/422/1495/tablea1&'CONTINUE ' J/MNRAS/422/1495/tablec1&-out=RAJ2000&-out=DEJ2000&-out=M&-out=Zma&'CONTINUE 'g&-out=Ymag&-out=Jmag&-out=Hmag&-out=K1mag&-out=K2mag&-out=pmRA&-ou&'CONTINUE 't=e_pmRA&-out=pmDE&-out=e_pmDE&-out=chi2&-out=Mmb&-out=n_Mmb&-out=O&'CONTINUE 'Name&-out=GCS9&-out=SimbadName&-out=e_Zmag&-out=e_Ymag&-out=e_Jmag&&'CONTINUE '-out=e_Hmag&-out=e_K1mag&-out=e_K2mag&-out=n_Mmb&' / Full request URL (POST) # INFO = 'contact=cds-question@unistra.fr' / Email or URL to contact publisher # INFO = 'server_software=VizieR/7.5.4' / Software version # INFO = 'publisher=CDS' / Data centre that produced the VOTable # INFO = 'CatalogsExamined=2' 2 catalogues with potential matches were examined. INFO = 'ivoid=ivo://cds.vizier/j/mnras/422/1495' / IVOID of underlying data collection # INFO = 'data_ivoid=ivo://cds.vizier/j/mnras/422/1495' / IVOID of underlying data collection # INFO = 'creator=Lodieu N.' / First author or institution # INFO = 'cites=bibcode:2012MNRAS.422.1495L' / Article or Data origin sources # INFO = 'journal=MNRA' / Journal name # INFO = 'original_date=2012' / Year of the article publication # INFO = 'reference_url=https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/422&'CONTINUE '/1495 ' / Dataset landing page # INFO = 'citation=doi:10.26093/cds/vizier.74221495' / Dataset identifier that can be used for citation # INFO = 'publication_date=2024-07-17' / Date of first publication in the data centre # INFO = 'rights_uri=https://cds.unistra.fr/vizier-org/licences_vizier.html' / Licence URI # END XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 224 / Number of bytes per record NAXIS2 = 1379 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 19 / Number of data fields (columns) CDS-CAT = 'J/MNRAS/422/1495' / Catalogue designation in CDS nomenclature UKIDSS Galactic Clusters Survey Pleiades members (Lodieu+ 2012) EXTNAME = 'J_MNRAS_422_1495_tablea1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablea1' / Table name in METAtab Sample of 1379 known Pleiades member candidates previously published in the literature and recovered in GCS DR9 TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:11 offset=1 TFORM1 = 'A11 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 14 / UCD=pos.eq.dec;meta.main char:11 offset=13 TFORM2 = 'A11 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 26 / UCD=meta.number short: ....... offset=25 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 30 / UCD=phot.mag;em.opt.I float: . offset=29 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 37 / UCD=phot.mag;em.IR.J float: .. offset=36 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 44 / UCD=phot.mag;em.IR.J float: .. offset=43 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 51 / UCD=phot.mag;em.IR.H float: .. offset=50 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 58 / UCD=phot.mag;em.IR.K float: .. offset=57 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude at first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 65 / UCD=phot.mag;em.IR.K float: .. offset=64 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude at second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 72 / UCD=pos.pm;pos.eq.ra float: .. offset=71 TFORM10 = 'F7.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 80 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=79 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 86 / UCD=pos.pm;pos.eq.dec float: . offset=85 TFORM12 = 'F7.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.fit.chi2;stat.fit.param;m offset=99 TFORM14 = 'F6.2 ' / Fortran Format TTYPE14 = 'chi2 ' / ? Reduced chi^2^ statistic of the astrometric fit (only in tablea1.dat) TBCOL15 = 107 / UCD=stat.probability float: .. offset=106 TFORM15 = 'F5.2 ' / Fortran Format TTYPE15 = 'Mmb ' / [0/1]? Membership probability (tablea1.dat) TBCOL16 = 113 / UCD=meta.note char:4 ......... offset=112 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'n_Mmb ' / Note on Mmb (tablea1.dat) (1) (link) TBCOL17 = 118 / UCD=meta.id;meta.main char:77* offset=117 TFORM17 = 'A77 ' / Fortran Format TTYPE17 = 'OName ' / Other name(s) (2) (link) TBCOL18 = 196 / UCD=meta.ref.url char:4 ...... offset=195 TFORM18 = 'A4 ' / Fortran Format TTYPE18 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL19 = 201 / UCD=meta.id char:23 .......... offset=200 TFORM19 = 'A23 ' / Fortran Format TTYPE19 = 'SimbadName' / Simbad column added by the CDS END +03 27 54.26 +24 56 10.9 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.47 0.93 DH003 GCS9 Cl* Melotte 22 DH 003 +03 29 58.76 +23 22 18.3 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.47 0.81 DH009 GCS9 Cl* Melotte 22 DH 009 +03 30 08.28 +22 38 38.3 0 15.346 14.741 13.680 13.020 12.895 12.805 16.33 3.42 -72.94 3.42 74.29 0.00 DH010 GCS9 Cl* Melotte 22 DH 010 +03 30 35.39 +23 03 07.9 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.44 0.63 DH012 GCS9 Cl* Melotte 22 DH 012 +03 31 14.64 +25 58 51.7 0 12.750 12.443 11.925 11.381 11.076 11.107 33.12 3.36 -39.84 3.36 2.35 0.00 DH015 GCS9 Cl* Melotte 22 DH 015 +03 31 29.60 +26 30 12.1 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 0.26 0.42 DH016 GCS9 Cl* Melotte 22 DH 016 +03 32 07.87 +23 13 57.2 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.46 0.84 DH017 GCS9 Cl* Melotte 22 DH 017 +03 32 28.30 +22 28 06.3 0 17.598 17.092 16.403 15.754 15.410 15.409 10.61 3.53 -31.53 3.53 0.64 0.00 DH019 GCS9 Cl* Melotte 22 DH 019 +03 32 32.97 +22 18 12.1 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 0.54 0.36 DH020 GCS9 Cl* Melotte 22 DH 020 +03 32 57.57 +27 17 19.4 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.87 0.80 DH024 GCS9 Cl* Melotte 22 DH 024 +03 33 10.51 +22 31 19.0 0 12.839 12.556 12.064 11.566 11.249 11.279 31.96 3.41 -37.15 3.41 2.39 0.00 DH027 GCS9 Cl* Melotte 22 DH 027 +03 33 39.01 +22 21 08.5 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.58 0.87 DH029 GCS9 Cl* Melotte 22 DH 029 +03 33 46.65 +23 48 19.4 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 5.20 0.94 DH030 GCS9 Cl* Melotte 22 DH 030 +03 33 49.81 +22 56 19.6 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.39 0.83 DH031 GCS9 Cl* Melotte 22 DH 031 +03 34 19.15 +22 27 59.6 0 14.056 13.606 13.051 12.550 12.227 12.213 27.04 2.94 -46.83 2.94 0.19 0.23 DH033 GCS9 Cl* Melotte 22 DH 033 +03 34 19.37 +22 48 42.2 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 0.22 0.12 DH034 GCS9 Cl* Melotte 22 DH 034 +03 34 41.55 +26 09 27.1 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 1.01 0.04 DH036 GCS9 Cl* Melotte 22 DH 036 +03 34 47.54 +26 22 13.4 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.12 0.60 DH039 GCS9 Cl* Melotte 22 DH 039 +03 34 54.95 +22 04 46.2 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.10 0.66 DH040 GCS9 Cl* Melotte 22 DH 040 +03 35 04.72 +25 50 48.0 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.30 0.85 DH041 GCS9 Cl* Melotte 22 DH 041 +03 35 09.41 +24 14 19.8 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 1.67 0.83 DH042 GCS9 Cl* Melotte 22 DH 042 +03 35 10.44 +24 31 54.4 0 14.645 14.122 13.531 12.972 12.661 12.629 17.82 2.63 -32.24 2.63 0.87 0.06 DH043 GCS9 Cl* Melotte 22 DH 043 +03 35 16.28 +23 47 57.8 0 15.198 14.649 14.056 13.523 13.179 13.195 21.38 2.61 -33.42 2.61 1.99 0.09 DH044 GCS9 Cl* Melotte 22 DH 044 +03 35 39.80 +25 38 45.2 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.31 0.68 DH046 GCS9 Cl* Melotte 22 DH 046 +03 35 44.28 +22 27 09.7 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.15 0.90 DH047 GCS9 Cl* Melotte 22 DH 047 +03 35 49.68 +25 04 48.0 0 13.372 13.029 12.506 11.959 11.707 11.697 32.03 2.62 -31.99 2.62 9.53 0.00 DH048 GCS9 Cl* Melotte 22 DH 048 +03 35 50.29 +25 42 20.5 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 0.36 0.02 DH049 GCS9 Cl* Melotte 22 DH 049 +03 35 56.06 +25 21 59.3 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.21 0.87 DH050 GCS9 Cl* Melotte 22 DH 050 +03 36 05.33 +25 21 03.4 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.66 0.94 DH051 GCS9 Cl* Melotte 22 DH 051 +03 36 11.07 +23 48 23.0 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 1.57 0.61 DH053 GCS9 Cl* Melotte 22 DH 053 +03 36 16.32 +25 08 48.8 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.50 0.91 DH054 GCS9 Cl* Melotte 22 DH 054 +03 36 22.33 +22 44 32.7 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 1.33 0.85 DH055 GCS9 Cl* Melotte 22 DH 055 +03 36 24.18 +22 37 24.3 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 1.67 0.93 DH056 GCS9 Cl* Melotte 22 DH 056 +03 36 26.17 +22 14 45.4 0 14.225 13.898 13.373 12.799 12.515 12.522 26.10 2.94 -40.80 2.94 0.12 0.51 DH057 GCS9 Cl* Melotte 22 DH 057 +03 36 27.37 +24 41 17.1 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 0.63 0.35 DH058 GCS9 Cl* Melotte 22 DH 058 +03 36 42.67 +28 21 05.9 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.46 0.78 DH062 GCS9 Cl* Melotte 22 DH 062 +03 36 44.12 +22 01 38.8 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.74 0.85 DH063 GCS9 Cl* Melotte 22 DH 063 +03 36 46.48 +28 15 05.9 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 0.80 0.06 DH064 GCS9 Cl* Melotte 22 DH 064 +03 37 03.48 +24 44 35.3 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 1.44 0.46 HCG6_HHJ392_DH066 GCS9 Cl* Melotte 22 DH 066 +03 37 10.51 +25 17 34.6 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 0.86 0.16 DH067 GCS9 Cl* Melotte 22 DH 067 +03 37 11.98 +26 46 28.7 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.54 0.65 HHJ242_DH068 GCS9 Cl* Melotte 22 HHJ 242 +03 37 15.61 +26 29 29.8 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.75 0.85 HHJ19 GCS9 Cl* Melotte 22 HHJ 19 +03 37 16.53 +23 11 04.2 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.52 0.84 HHJ179_DH069 GCS9 Cl* Melotte 22 HHJ 179 +03 37 16.71 +25 44 11.1 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 0.91 0.22 HHJ154_DH070 GCS9 Cl* Melotte 22 HHJ 154 +03 37 26.39 +24 34 01.2 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.79 0.93 DH071 GCS9 Cl* Melotte 22 DH 071 +03 37 26.62 +26 41 27.5 0 12.870 12.622 12.135 11.531 11.320 11.360 40.30 3.30 -47.71 3.30 0.63 0.00 HHJ422 GCS9 Cl* Melotte 22 HHJ 422 +03 37 30.28 +28 32 26.4 0 15.565 15.067 14.516 13.880 13.587 13.594 6.71 4.97 -40.29 4.97 0.50 0.02 DH072 GCS9 Cl* Melotte 22 DH 072 +03 37 30.69 +24 50 54.3 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 6.17 0.34 HHJ110 GCS9 Cl* Melotte 22 HHJ 110 +03 37 34.18 +24 42 15.1 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2.46 0.51 DH074 GCS9 Cl* Melotte 22 DH 074 +03 37 36.01 +26 32 48.4 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 1.02 0.48 DH075 GCS9 Cl* Melotte 22 DH 075 +03 37 37.70 +26 21 04.0 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.33 0.83 HHJ172_DH076 GCS9 Cl* Melotte 22 HHJ 172 +03 37 40.23 +24 42 58.0 0 13.786 13.370 12.837 12.338 12.034 12.043 27.94 2.48 -39.53 2.48 1.40 0.09 HHJ283 GCS9 Cl* Melotte 22 HHJ 283 +03 37 47.51 +24 53 46.1 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 1.67 0.86 HHJ72_DH078 GCS9 Cl* Melotte 22 HHJ 72 +03 37 48.93 +26 51 45.1 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.33 0.86 HHJ137_DH079 GCS9 Cl* Melotte 22 HHJ 137 +03 37 54.79 +25 26 31.3 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.58 0.83 HHJ402 GCS9 Cl* Melotte 22 HHJ 402 +03 37 56.42 +23 22 56.6 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.16 0.65 HHJ268_DH080 GCS9 Cl* Melotte 22 HHJ 268 +03 38 02.05 +24 20 15.1 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 2.15 0.92 HHJ248_DH081 GCS9 Cl* Melotte 22 HHJ 248 +03 38 08.81 +21 14 49.0 0 12.497 12.220 11.788 11.506 10.905 11.259 29.15 3.41 -36.90 3.41 0.74 0.04 DH082 GCS9 Cl* Melotte 22 DH 082 +03 38 10.27 +25 11 32.9 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 3.31 0.88 HHJ35 GCS9 Cl* Melotte 22 HHJ 35 +03 38 13.04 +23 37 20.7 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 2.58 0.84 HHJ32 GCS9 Cl* Melotte 22 HHJ 32 +03 38 13.04 +24 38 16.8 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 0.97 0.42 HHJ149_DH084 GCS9 Cl* Melotte 22 HHJ 149 +03 38 24.22 +25 19 49.2 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.46 0.92 DH086 GCS9 Cl* Melotte 22 DH 086 +03 38 24.80 +26 15 23.5 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 0.30 0.27 HHJ295_DH087 GCS9 Cl* Melotte 22 HHJ 295 +03 38 27.52 +25 30 18.1 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 1.94 0.69 HHJ2 GCS9 Cl* Melotte 22 HHJ 2 +03 38 27.83 +26 51 25.4 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.31 0.81 HHJ121_DH089 GCS9 Cl* Melotte 22 HHJ 121 +03 38 34.21 +23 43 07.3 0 14.985 14.547 13.940 13.424 13.106 13.105 8.23 2.50 -53.41 2.50 12.39 0.00 HHJ98_DH090 GCS9 Cl* Melotte 22 HHJ 98 +03 38 34.49 +23 40 22.3 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 3.34 0.48 HHJ97_DH091 GCS9 Cl* Melotte 22 HHJ 97 +03 38 43.30 +25 22 26.9 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 1.32 0.79 HCG11_HHJ378_DH092 GCS9 Cl* Melotte 22 HCG 378 +03 38 45.75 +24 28 03.7 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 2.42 0.84 HHJ91_DH094 GCS9 Cl* Melotte 22 HHJ 91 +03 38 53.89 +24 25 07.7 0 13.542 13.293 12.817 12.269 11.999 11.985 12.60 2.48 -33.86 2.48 1.14 0.03 HCG15 GCS9 Cl* Melotte 22 HCG 15 +03 38 54.16 +24 42 15.6 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 1.14 0.50 HHJ63 GCS9 Cl* Melotte 22 HHJ 63 +03 39 03.31 +26 29 30.9 0 13.943 13.592 13.088 12.528 12.249 12.283 35.52 3.00 -41.84 3.00 0.41 0.00 HHJ317 GCS9 Cl* Melotte 22 HHJ 317 +03 39 04.06 +27 00 04.7 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 0.70 0.22 DH098 GCS9 Cl* Melotte 22 DH 098 +03 39 08.13 +24 46 14.4 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.74 0.91 HCG16_HHJ398_DH099 GCS9 Cl* Melotte 22 HCG 16 +03 39 13.33 +25 43 49.5 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.43 0.90 HHJ349_DH100 GCS9 Cl* Melotte 22 HHJ 349 +03 39 15.56 +26 48 03.6 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.54 0.74 HHJ66_DH102 GCS9 Cl* Melotte 22 HHJ 66 +03 39 16.75 +24 57 38.5 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 3.81 0.85 DH103 GCS9 Cl* Melotte 22 DH 103 +03 39 17.05 +22 27 10.9 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 2.56 0.69 IPMBD43 GCS9 Cl* Melotte 22 IPMBD 43 +03 39 22.07 +24 02 39.1 0 15.165 14.761 14.179 13.633 13.370 13.330 9.68 2.60 -31.09 2.60 1.34 0.00 BPL1 GCS9 Cl* Melotte 22 BPL 1 +03 39 22.42 +26 11 36.0 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 1.11 0.93 HHJ224_DH104_Moraux2003_51 GCS9 Cl* Melotte 22 HHJ 224 +03 39 28.99 +25 34 55.8 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 1.61 0.41 HHJ359 GCS9 Cl* Melotte 22 HHJ 359 +03 39 31.49 +20 04 40.4 0 13.372 13.019 12.545 11.954 11.659 11.704 32.73 3.42 -49.02 3.42 0.24 0.00 DH106 GCS9 Cl* Melotte 22 DH 106 +03 39 32.32 +24 16 01.1 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.97 0.91 BPL2_DH107 GCS9 Cl* Melotte 22 BPL 2 +03 39 35.46 +24 07 06.1 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 2.91 0.87 HHJ407_DH108 GCS9 Cl* Melotte 22 HHJ 407 +03 39 41.12 +23 28 23.3 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.78 0.70 HHJ83 GCS9 Cl* Melotte 22 HHJ 83 +03 39 42.72 +23 54 27.6 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 1.07 0.88 HCG22_T2B_BPL3_HHJ230_DH109 GCS9 Cl* Melotte 22 HCG 22 +03 39 43.32 +23 12 25.3 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.27 0.67 DH110 GCS9 Cl* Melotte 22 DH 110 +03 39 44.19 +22 07 45.5 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.96 0.92 HHJ141_DH111 GCS9 Cl* Melotte 22 HHJ 141 +03 39 44.37 +26 18 18.8 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 0.49 0.59 Moraux2003_82 GCS9 Cl* Melotte 22 MBSC 82 +03 39 44.79 +22 39 15.3 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.93 0.94 DH2004_112 GCS9 Cl* Melotte 22 DH 112 +03 39 46.35 +23 58 52.9 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 3.77 0.69 HHJ324_BPL4_DH113 GCS9 Cl* Melotte 22 HHJ 324 +03 39 47.02 +26 21 14.3 0 14.554 14.117 13.546 12.997 12.694 12.688 28.81 3.00 -44.81 3.00 0.48 0.10 HHJ178_DH114_Moraux2003_57 GCS9 Cl* Melotte 22 HHJ 178 +03 39 47.99 +23 50 56.6 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 1.60 0.92 HHJ291_BPL5_DH115 GCS9 Cl* Melotte 22 HHJ 291 +03 39 48.51 +23 46 03.8 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 1.27 0.83 HHJ74_BPL6 GCS9 Cl* Melotte 22 HHJ 74 +03 39 49.72 +23 03 26.2 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.30 0.89 HHJ138_DH117 GCS9 Cl* Melotte 22 HHJ 138 +03 39 50.68 +23 45 52.3 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 1.25 0.89 BPL7_DH118 GCS9 Cl* Melotte 22 BPL 7 +03 39 53.53 +25 46 46.2 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.24 0.92 DH2004_119 GCS9 Cl* Melotte 22 DH 119 +03 39 55.21 +24 12 54.1 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 1.49 0.86 BPL8 GCS9 Cl* Melotte 22 BPL 8 +03 39 57.15 +26 07 00.1 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.32 0.82 Moraux2003_94 GCS9 Cl* Melotte 22 MBSC 94 +03 39 57.35 +23 19 42.2 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.39 0.61 HHJ103 GCS9 Cl* Melotte 22 HHJ 103 +03 39 57.85 +25 55 29.7 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 0.41 0.40 DH120_Moraux2003_92 GCS9 Cl* Melotte 22 DH 120 +03 40 00.19 +23 26 05.7 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 1.85 0.85 HCG26_SK802_HHJ403_DH121 GCS9 Cl* Melotte 22 HCG 26 +03 40 01.82 +22 59 20.2 0 12.400 12.141 11.716 11.468 10.921 11.098 22.71 2.97 -65.69 2.97 2.24 0.00 HHJ426 GCS9 Cl* Melotte 22 HHJ 426 +03 40 01.84 +25 04 19.5 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 3.13 0.93 HHJ102 GCS9 Cl* Melotte 22 HHJ 102 +03 40 01.87 +24 46 25.9 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.70 0.94 HCG24_T35B_HHJ226_DH122 GCS9 Cl* Melotte 22 HCG 24 +03 40 03.61 +24 30 02.7 0 13.674 13.270 12.776 12.243 11.963 11.937 2.13 2.48 -31.06 2.48 34.28 0.00 BPL9 GCS9 Cl* Melotte 22 BPL 9 +03 40 05.05 +25 31 36.6 0 13.648 13.372 12.896 12.232 12.056 12.038 42.65 2.95 -35.50 2.95 0.66 0.00 HHJ356 GCS9 Cl* Melotte 22 HHJ 356 +03 40 05.98 +25 40 20.8 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.51 0.89 DH124 GCS9 Cl* Melotte 22 DH 124 +03 40 06.21 +28 08 32.0 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.84 0.62 DH125 GCS9 Cl* Melotte 22 DH 125 +03 40 07.01 +22 38 47.5 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.63 0.89 HHJ114 GCS9 Cl* Melotte 22 HHJ 114 +03 40 07.11 +24 13 03.3 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.93 0.90 HHJ38_BPL10_DH126 GCS9 Cl* Melotte 22 HHJ 38 +03 40 09.69 +23 10 32.4 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 0.76 0.41 HCG31_DH127 GCS9 Cl* Melotte 22 HCG 31 +03 40 10.93 +26 06 40.8 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.32 0.67 HCG28_HHJ177_DH128 GCS9 Cl* Melotte 22 HCG 28 +03 40 11.04 +25 23 26.6 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.26 0.87 HHJ147_DH129 GCS9 Cl* Melotte 22 HHJ 147 +03 40 11.93 +25 52 32.3 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 0.57 0.39 HHJ29 GCS9 Cl* Melotte 22 HHJ 29 +03 40 12.74 +23 09 35.6 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 0.50 0.08 HCG32_SK794_HHJ280_DH130 GCS9 Cl* Melotte 22 HCG 32 +03 40 14.80 +25 50 05.5 0 13.923 13.506 12.914 12.433 12.114 12.129 8.07 2.95 -22.54 2.95 0.30 0.00 SK781 GCS9 Cl* Melotte 22 SK 781 +03 40 14.91 +25 19 18.7 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.67 0.87 SK785_HHJ397_DH131 GCS9 Cl* Melotte 22 SK 785 +03 40 15.93 +24 22 31.3 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 4.56 0.34 BPL11 GCS9 Cl* Melotte 22 BPL 11 +03 40 23.06 +25 29 47.6 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.50 0.93 HCG33_HHJ343_DH134 GCS9 Cl* Melotte 22 HCG 33 +03 40 23.84 +23 04 09.0 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.58 0.90 HHJ162_DH135 GCS9 Cl* Melotte 22 HHJ 162 +03 40 24.20 +24 35 04.0 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.86 0.91 HCG34_SK777_BPL12_DH136 GCS9 Cl* Melotte 22 HCG 34 +03 40 25.62 +24 06 00.0 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 1.37 0.94 HHJ135_BPL13_DH138 GCS9 Cl* Melotte 22 HHJ 135 +03 40 26.27 +23 21 29.1 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.57 0.71 HHJ167 GCS9 Cl* Melotte 22 HHJ 167 +03 40 26.41 +24 05 23.5 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 1.79 0.84 SK778_HHJ338_BPL14_DH139 GCS9 Cl* Melotte 22 SK 778 +03 40 26.95 +24 14 14.2 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 1.17 0.94 HHJ169_BPL15_DH140 GCS9 Cl* Melotte 22 HHJ 169 +03 40 27.93 +24 12 09.3 0 19.730 18.501 17.352 16.700 16.088 0.100 15.91 3.22 -42.54 3.22 5.78 0.62 int-pl-IZ-84;IPLJ0340279+241209_Y GCS9 Cl* Melotte 22 IPL 84 +03 40 31.17 +25 08 52.8 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 2.05 0.93 HCG36_T102_HHJ329_DH142 GCS9 Cl* Melotte 22 HCG 36 +03 40 31.50 +23 33 02.0 0 12.655 12.386 11.899 11.459 11.074 11.141 21.40 2.12 -35.26 2.12 8.58 0.58 SK773_DH143 GCS9 Cl* Melotte 22 SK 773 +03 40 32.59 +25 28 40.6 0 15.026 14.501 13.946 13.420 13.114 13.099 10.36 2.23 -47.38 2.23 1.93 0.21 HHJ101_DH144 GCS9 Cl* Melotte 22 HHJ 101 +03 40 35.33 +20 57 56.6 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 0.48 0.01 DH146 GCS9 Cl* Melotte 22 DH 146 +03 40 35.50 +23 13 07.4 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 5.06 0.44 L07_A1_1 GCS9 +03 40 39.46 +23 26 34.8 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 2.27 0.69 DH147_L07_A1_2 GCS9 Cl* Melotte 22 DH 147 +03 40 40.32 +25 50 48.1 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 1.34 0.94 DH148 GCS9 Cl* Melotte 22 DH 148 +03 40 43.20 +22 49 53.8 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 2.33 0.90 DH151_L07_193 GCS9 Cl* Melotte 22 DH 151 +03 40 43.27 +25 11 55.4 0 12.877 12.623 12.124 11.589 11.329 41.44 2.97 -27.65 2.97 3.03 0.00 SK754 GCS9 Cl* Melotte 22 SK 754 +03 40 49.40 +21 12 55.3 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 0.41 0.39 DH152 GCS9 Cl* Melotte 22 DH 152 +03 40 51.08 +20 41 17.2 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.28 0.75 DH155 GCS9 Cl* Melotte 22 DH 155 +03 40 51.46 +19 32 45.8 0 14.044 13.716 13.240 12.620 12.349 12.346 27.56 5.01 -46.86 5.01 1.30 0.17 DH157 GCS9 Cl* Melotte 22 DH 157 +03 40 51.82 +23 13 50.5 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 1.21 0.93 HCG43_HHJ234_DH158_L07_169 GCS9 Cl* Melotte 22 HCG 43 +03 40 54.48 +22 54 25.5 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 1.48 0.93 HHJ123_DH159_L07_182 GCS9 Cl* Melotte 22 HHJ 123 +03 40 55.05 +22 20 58.7 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 1.06 0.40 HCG44_SK758_HHJ355_DH160 GCS9 Cl* Melotte 22 HCG 44 +03 40 55.30 +25 34 57.4 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 7.86 0.69 L07_A1_4 GCS9 +03 40 56.06 +28 43 39.0 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 0.85 0.21 DH161 GCS9 Cl* Melotte 22 DH 161 +03 40 59.26 +25 11 55.2 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.63 0.86 HHJ41_DH162 GCS9 Cl* Melotte 22 HHJ 41 +03 41 00.39 +24 13 35.0 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.81 0.89 BPL17 GCS9 Cl* Melotte 22 BPL 17 +03 41 01.99 +24 55 21.2 0 14.847 14.451 13.855 13.287 13.009 14.19 2.97 -32.57 2.97 1.14 0.03 HHJ126 GCS9 Cl* Melotte 22 HHJ 126 +03 41 02.99 +23 43 21.4 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.61 0.71 HCG45_HHJ290_BPL18_DH163 GCS9 Cl* Melotte 22 HCG 45 +03 41 05.23 +23 50 14.9 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 1.49 0.85 BPL19 GCS9 Cl* Melotte 22 BPL 19 +03 41 10.27 +25 45 55.9 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 1.32 0.94 SK733_DH164 GCS9 Cl* Melotte 22 SK 733 +03 41 13.21 +24 05 25.6 0 20.248 19.168 17.791 16.898 16.325 0.166 14.11 3.48 -46.41 3.48 1.51 0.61 int-pl-IZ-81;IPLJ0341131+240525_Y GCS9 Cl* Melotte 22 IPL 81 +03 41 19.35 +23 51 41.7 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 1.15 0.90 HCG48_DH167 GCS9 Cl* Melotte 22 HCG 48 +03 41 19.86 +25 06 49.0 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.45 0.87 HHJ191 GCS9 Cl* Melotte 22 HHJ 191 +03 41 20.00 +22 37 53.7 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 6.59 0.30 SK739_DH168 GCS9 Cl* Melotte 22 SK 739 +03 41 22.45 +24 23 51.6 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 1.05 0.90 DH169 GCS9 Cl* Melotte 22 DH 169 +03 41 22.88 +23 55 48.1 0 14.135 13.702 13.150 12.615 12.323 12.335 24.42 2.12 -38.30 2.12 3.58 0.57 HCG49_HHJ233_BPL20_DH170 GCS9 Cl* Melotte 22 HCG 49 +03 41 23.63 +27 24 16.9 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 0.33 0.40 DH171 GCS9 Cl* Melotte 22 DH 171 +03 41 24.65 +24 15 13.9 0 16.242 15.768 15.135 14.504 14.192 14.176 32.40 2.16 -41.17 2.16 1.95 0.00 DH172 GCS9 Cl* Melotte 22 DH 172 +03 41 24.69 +25 23 05.8 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 3.60 0.78 HHJ117_L07_48 GCS9 Cl* Melotte 22 HHJ 117 +03 41 25.33 +22 32 55.8 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.81 0.89 HCG51_SK732_HHJ345_DH173 GCS9 Cl* Melotte 22 HCG 51 +03 41 25.97 +23 15 35.8 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 2.57 0.94 L07_170 GCS9 +03 41 26.36 +23 08 02.7 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 11.80 0.92 DH174_L07_168 GCS9 Cl* Melotte 22 DH 174 +03 41 26.90 +24 01 02.3 0 13.364 12.912 12.305 11.845 11.430 11.454 25.17 2.12 -47.48 2.12 4.23 0.38 SK729_HHJ340_BPL21_DH175 GCS9 Cl* Melotte 22 SK 729 +03 41 29.39 +24 53 40.1 0 14.735 14.330 13.741 13.182 12.863 33.87 2.97 -32.81 2.97 1.30 0.00 Moraux2003_63 GCS9 Cl* Melotte 22 MBSC 63 +03 41 29.70 +24 32 26.5 0 13.241 12.967 12.496 11.906 11.680 23.06 2.97 -37.00 2.97 0.84 0.46 BPL22 GCS9 Cl* Melotte 22 BPL 22 +03 41 30.35 +25 17 05.9 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 104.59 0.60 L07_A1_5_L07_214 GCS9 +03 41 31.21 +24 03 24.7 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 2.29 0.89 HHJ90_BPL23_DH177 GCS9 Cl* Melotte 22 HHJ 90 +03 41 33.58 +27 09 50.1 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.47 0.89 DH178 GCS9 Cl* Melotte 22 DH 178 +03 41 33.90 +23 11 44.9 0 16.186 15.643 15.029 14.509 14.159 14.137 30.84 2.27 -37.70 2.27 1.56 0.00 HHJ12_L07_A1_6 GCS9 Cl* Melotte 22 HHJ 12 +03 41 34.80 +23 38 15.6 0 15.153 14.666 14.080 13.522 13.242 13.186 15.91 2.13 -49.58 2.13 0.83 0.48 DH180 GCS9 Cl* Melotte 22 DH 180 +03 41 36.55 +22 41 01.7 0 16.280 15.724 15.122 14.591 14.266 14.232 32.02 2.27 -36.94 2.27 19.82 0.00 L07_photNM_21 GCS9 +03 41 37.26 +25 08 32.6 0 14.630 14.170 13.596 13.084 12.736 28.66 2.97 -44.60 2.97 1.75 0.12 HHJ148_DH182 GCS9 Cl* Melotte 22 HHJ 148 +03 41 38.81 +24 23 09.2 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2.29 0.59 BPL25 GCS9 Cl* Melotte 22 BPL 25 +03 41 38.87 +22 16 40.3 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 1.28 0.00 HHJ176_DH183 GCS9 Cl* Melotte 22 HHJ 176 +03 41 39.84 +24 52 30.0 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 1.71 0.88 HHJ69_Moraux2003_84 GCS9 Cl* Melotte 22 HHJ 69 +03 41 40.91 +25 54 24.1 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 4.70 0.74 L07_A1_8_L07_248 GCS9 +03 41 41.46 +22 58 10.0 0 15.253 14.805 14.250 13.646 13.328 13.331 2.08 2.26 -35.29 2.26 3.25 0.00 L07_184 GCS9 +03 41 42.41 +23 54 57.1 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 0.56 0.41 HHJ6_BPL26 GCS9 Cl* Melotte 22 HHJ 6 +03 41 43.72 +23 07 59.7 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 5.75 0.60 L07_A1_9 GCS9 +03 41 45.07 +22 28 01.8 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2.91 0.59 DH185 GCS9 Cl* Melotte 22 DH 185 +03 41 46.44 +24 55 33.1 0 15.937 15.504 14.923 14.371 14.019 10.89 2.99 -39.19 2.99 1.45 0.34 DH186 GCS9 Cl* Melotte 22 DH 186 +03 41 47.09 +25 00 21.9 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.91 0.92 HHJ125 GCS9 Cl* Melotte 22 HHJ 125 +03 41 48.05 +26 47 20.7 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 0.69 0.53 HHJ357_DH188 GCS9 Cl* Melotte 22 HHJ 357 +03 41 49.60 +22 29 36.7 0 15.602 14.992 14.388 13.890 13.505 13.492 29.63 2.51 -46.82 2.51 3.79 0.01 DH189 GCS9 Cl* Melotte 22 DH 189 +03 41 51.50 +24 19 27.3 0 16.617 15.976 15.244 14.667 14.283 0.009 16.53 2.33 -37.48 2.33 3.69 0.46 int-pl-IZ-88;IPLJ0341515+241927_BPL27_Y GCS9 Cl* Melotte 22 IPL 88 +03 41 52.32 +24 41 57.6 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 0.69 0.50 HHJ120_BPL28_DH190 GCS9 Cl* Melotte 22 HHJ 120 +03 41 52.94 +24 07 24.7 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 2.22 0.82 HHJ108_BPL29_DH191 GCS9 Cl* Melotte 22 HHJ 108 +03 41 53.05 +27 04 42.9 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.25 0.92 DH192 GCS9 Cl* Melotte 22 DH 192 +03 41 54.16 +23 05 04.7 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 4.74 0.69 L07_A1_10 GCS9 +03 41 54.21 +25 43 47.1 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 1.80 0.89 HCG55_B346_HHJ342_DH194 GCS9 Cl* Melotte 22 HCG 55 +03 41 56.49 +27 02 57.9 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.17 0.86 DH195 GCS9 Cl* Melotte 22 DH 195 +03 41 56.72 +23 58 43.2 0 15.234 14.770 14.109 13.584 13.195 14.88 2.31 -33.12 2.31 0.99 0.07 BPL30 GCS9 Cl* Melotte 22 BPL 30 +03 41 58.66 +22 57 01.7 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 10.25 0.90 HCG66_HHJ312_DH196_L07_16 GCS9 Cl* Melotte 22 HCG 66 +03 41 58.86 +26 12 21.1 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 0.27 0.08 HCG56_B117_HHJ351_DH197_Moraux2003_16 GCS9 Cl* Melotte 22 HCG 56 +03 41 59.67 +24 42 18.3 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 1.19 0.65 BPL31_Moraux2003_102 GCS9 Cl* Melotte 22 BPL 31 +03 41 59.75 +26 27 39.6 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.32 0.64 HHJ213_DH200 GCS9 Cl* Melotte 22 HHJ 213 +03 42 00.02 +25 01 47.1 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 1.15 0.88 SK702_HHJ277_DH201 GCS9 Cl* Melotte 22 SK 702 +03 42 00.05 +26 01 12.1 0 14.722 14.226 13.612 13.052 12.712 12.728 13.09 2.23 -31.95 2.23 3.88 0.01 L07_18 GCS9 +03 42 00.37 +24 10 12.6 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 1.87 0.73 BPL32 GCS9 Cl* Melotte 22 BPL 32 +03 42 01.68 +22 23 26.6 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 0.89 0.00 HCG67_HHJ201_DH202 GCS9 Cl* Melotte 22 HCG 67 +03 42 02.87 +24 12 36.0 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.93 0.61 HCG63_HHJ350_BPL33_DH203 GCS9 Cl* Melotte 22 HCG 63 +03 42 02.93 +23 55 53.7 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.46 0.87 HCG64_HHJ406_BPL34_DH204 GCS9 Cl* Melotte 22 HCG 64 +03 42 03.30 +24 32 13.3 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 1.39 0.79 SK701_HHJ354_BPL35_DH205_Moraux2003_11 GCS9 Cl* Melotte 22 SK 701 +03 42 03.42 +25 22 39.1 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 5.58 0.68 HHJ212_DH206_L07_41 GCS9 Cl* Melotte 22 HHJ 212 +03 42 04.16 +22 16 51.0 0 14.270 13.988 13.501 12.878 12.617 40.26 7.97 -17.56 7.97 1.17 0.00 SK708 GCS9 Cl* Melotte 22 SK 708 +03 42 05.74 +23 07 14.4 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2.73 0.41 L07_A1_11 GCS9 +03 42 08.29 +25 36 59.9 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 2.38 0.77 HHJ270_DH211_L07_36 GCS9 Cl* Melotte 22 HHJ 270 +03 42 08.84 +23 35 16.8 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 1.62 0.72 SK699_HHJ384_DH212 GCS9 Cl* Melotte 22 SK 699 +03 42 09.86 +27 57 25.2 0 13.631 13.340 12.849 12.206 11.986 11.978 33.43 3.68 -33.78 3.68 0.13 0.00 DH213 GCS9 Cl* Melotte 22 DH 213 +03 42 10.60 +22 18 05.5 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 0.41 0.00 HHJ55 GCS9 Cl* Melotte 22 HHJ 55 +03 42 10.93 +24 05 08.4 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 1.14 0.85 HCG68_T36_HHJ396_DH214 GCS9 Cl* Melotte 22 HCG 68 +03 42 10.99 +25 44 35.0 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 3.83 0.83 L07_25 GCS9 +03 42 12.62 +26 49 44.9 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.84 0.91 HHJ236_DH215 GCS9 Cl* Melotte 22 HHJ 236 +03 42 13.49 +24 18 49.6 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 0.83 0.48 HHJ34_BPL36 GCS9 Cl* Melotte 22 HHJ 34 +03 42 13.90 +20 21 43.6 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.53 0.76 DH216 GCS9 Cl* Melotte 22 DH 216 +03 42 15.37 +23 11 30.9 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 1.60 0.81 HHJ109_L07_167 GCS9 Cl* Melotte 22 HHJ 109 +03 42 17.90 +24 06 57.6 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.77 0.94 HHJ129_BPL37_DH217 GCS9 Cl* Melotte 22 HHJ 129 +03 42 18.88 +23 59 22.0 0 12.658 12.428 11.944 11.452 11.259 25.66 2.30 -50.92 2.30 2.16 0.01 SK687_HHJ437 GCS9 Cl* Melotte 22 SK 687 +03 42 22.13 +25 03 56.3 0 15.564 15.206 14.659 14.050 13.781 24.49 2.98 -48.94 2.98 8.93 0.19 DH219 GCS9 Cl* Melotte 22 DH 219 +03 42 26.28 +23 51 38.5 0 15.456 14.895 14.283 13.744 13.347 13.357 10.50 2.13 -43.07 2.13 2.55 0.42 HHJ49_BPL38_DH221 GCS9 Cl* Melotte 22 HHJ 49 +03 42 26.29 +24 14 07.7 0 14.155 13.741 13.173 12.659 12.325 12.320 10.47 2.12 -47.50 2.12 2.98 0.21 HCG73_SK680_HHJ262_BPL39_DH223 GCS9 Cl* Melotte 22 HCG 73 +03 42 27.31 +22 34 24.6 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 1.87 0.90 HCG76_HHJ294_DH224 GCS9 Cl* Melotte 22 HCG 76 +03 42 28.43 +27 12 58.1 0 16.484 16.050 15.500 14.978 14.679 14.670 9.87 3.37 -42.08 3.37 0.41 0.18 DH226 GCS9 Cl* Melotte 22 DH 226 +03 42 28.66 +25 01 00.2 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 1.06 0.93 SK676_HHJ362_DH227 GCS9 Cl* Melotte 22 SK 676 +03 42 29.43 +22 47 25.9 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 7.62 0.90 HCG77_A80_DH228 GCS9 Cl* Melotte 22 HCG 77 +03 42 29.51 +22 23 45.7 0 12.961 12.706 12.271 11.802 11.535 25.41 7.95 -63.14 7.95 0.89 0.00 SK682 GCS9 Cl* Melotte 22 SK 682 +03 42 29.71 +20 48 39.6 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.39 0.82 DH229 GCS9 Cl* Melotte 22 DH 229 +03 42 31.21 +24 49 21.2 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 8.89 0.56 HCG74_HHJ82_DH230_Moraux2003_76 GCS9 Cl* Melotte 22 HCG 74 +03 42 33.97 +24 11 00.5 0 15.453 14.926 14.319 13.774 13.408 13.422 11.59 2.13 -52.78 2.13 8.09 0.02 BPL40_DH231 GCS9 Cl* Melotte 22 BPL 40 +03 42 36.27 +23 22 04.7 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 1.08 0.73 HCG79_HHJ241_HHJ241_DH232 GCS9 Cl* Melotte 22 HCG 79 +03 42 36.95 +25 13 57.5 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 2.57 0.70 DH233 GCS9 Cl* Melotte 22 DH 233 +03 42 40.23 +23 59 21.5 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 1.30 0.59 HCG80_T3_HHJ409_DH234 GCS9 Cl* Melotte 22 HCG 80 +03 42 41.18 +24 01 42.7 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.54 0.93 HCG82_HHJ115_BPL41_DH235 GCS9 Cl* Melotte 22 HCG 82 +03 42 41.85 +24 00 15.5 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.31 0.94 BPL42_DH236 GCS9 Cl* Melotte 22 BPL 42 +03 42 42.12 +25 11 48.7 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 1.90 0.82 HHJ45_DH237 GCS9 Cl* Melotte 22 HHJ 45 +03 42 42.41 +23 20 21.5 0 13.380 12.965 12.389 11.853 11.534 11.549 20.26 2.25 -20.48 2.25 1.34 0.00 HCG86_DH238 GCS9 Cl* Melotte 22 HCG 86 +03 42 43.81 +25 32 06.2 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 2.28 0.93 SK665_HHJ341_DH239 GCS9 Cl* Melotte 22 SK 665 +03 42 44.37 +23 06 16.1 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 5.30 0.75 HHJ119_DH240_L07_176 GCS9 Cl* Melotte 22 HHJ 119 +03 42 44.46 +23 58 15.2 0 15.186 14.686 14.062 13.536 13.187 13.188 15.12 2.13 -48.66 2.13 4.84 0.57 BPL43_DH241 GCS9 Cl* Melotte 22 BPL 43 +03 42 45.65 +23 49 22.6 0 15.473 14.898 14.286 13.714 13.361 13.361 22.64 2.13 -48.82 2.13 2.57 0.38 HHJ52_BPL44 GCS9 Cl* Melotte 22 HHJ 52 +03 42 47.30 +23 00 40.3 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 5.33 0.59 L07_A1_13 GCS9 +03 42 48.17 +24 04 01.2 0 17.739 16.847 16.038 15.466 14.936 0.020 19.17 2.24 -33.98 2.24 3.95 0.16 int-pl-IZ-29;IPLJ0342481+240401_BPL45_Y GCS9 Cl* Melotte 22 IPL 29 +03 42 48.91 +23 34 48.4 0 14.623 14.157 13.593 13.067 12.741 12.742 24.86 2.13 -47.62 2.13 1.71 0.49 HHJ153_DH242 GCS9 Cl* Melotte 22 HHJ 153 +03 42 49.11 +24 10 15.4 0 13.648 13.221 12.670 12.134 11.801 11.828 31.63 2.12 -28.60 2.12 1.47 0.00 SK663_BPL46_DH243 GCS9 Cl* Melotte 22 SK 663 +03 42 51.68 +23 08 43.7 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 6.70 0.92 L07_177 GCS9 +03 42 54.00 +26 08 16.1 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 4.13 0.94 HHJ146_DH244_L07_22 GCS9 Cl* Melotte 22 HHJ 146 +03 42 55.88 +22 38 00.4 0 16.581 16.069 15.450 14.845 14.501 0.009 25.97 2.55 -3.49 2.55 1.79 0.00 int-pl-IZ-78;2MASSJ0342558+223800_Y GCS9 Cl* Melotte 22 IPL 78 +03 42 56.55 +22 51 17.9 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 4.45 0.78 L07_191 GCS9 +03 42 56.55 +24 04 57.8 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 1.08 0.82 HCG93_SK658_HHJ401_BPL48_DH245 GCS9 Cl* Melotte 22 HCG 93 +03 42 56.57 +24 13 45.4 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.76 0.92 HCG92_BPL47 GCS9 Cl* Melotte 22 HCG 92 +03 42 58.60 +20 12 45.0 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 0.30 0.10 DH247 GCS9 Cl* Melotte 22 DH 247 +03 42 59.92 +22 42 51.5 0 16.085 15.349 14.650 14.118 13.712 13.703 25.82 2.27 -43.19 2.27 33.85 0.17 L07_A1_14_L07_245 GCS9 +03 43 00.17 +24 43 52.3 0 17.791 16.841 16.022 15.425 14.969 32.72 3.09 -51.43 3.09 12.65 0.00 BPL49_CFHT-Pl-17_M7.9 GCS9 Cl* Melotte 22 BPL 49 +03 43 01.27 +24 14 52.2 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 2.12 0.68 BPL50 GCS9 Cl* Melotte 22 BPL 50 +03 43 01.40 +23 29 30.4 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 1.47 0.94 L07_158 GCS9 +03 43 03.83 +23 54 19.6 0 18.852 17.703 16.751 16.052 15.562 0.046 18.37 2.48 -38.49 2.48 2.92 0.66 int-pl-IZ-25;2MASSJ0343038+235420_Y GCS9 Cl* Melotte 22 IPL 25 +03 43 04.20 +22 48 03.3 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 6.22 0.90 HCG101_B173_HHJ428_DH251 GCS9 Cl* Melotte 22 HCG 101 +03 43 04.37 +25 26 12.0 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 8.13 0.56 HHJ107 GCS9 Cl* Melotte 22 HHJ 107 +03 43 05.54 +24 49 28.3 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 2.77 0.86 HCG97_HHJ432_DH252 GCS9 Cl* Melotte 22 HCG 97 +03 43 06.50 +22 17 49.2 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 4.64 0.67 HHJ18 GCS9 Cl* Melotte 22 HHJ 18 +03 43 07.58 +25 34 29.0 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 2.69 0.92 HCG96_DH253_L07_34 GCS9 Cl* Melotte 22 HCG 96 +03 43 09.75 +24 41 32.7 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 2.88 0.67 HCG100_SK654_T40_HHJ383_BPL51_DH254_Moraux2003_2 GCS9 Cl* Melotte 22 HCG 100 +03 43 11.57 +25 25 22.9 0 13.868 13.412 12.861 12.355 12.020 12.040 6.10 2.23 -53.18 2.23 6.99 0.00 SK650 GCS9 Cl* Melotte 22 SK 650 +03 43 11.65 +24 06 52.4 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 3.13 0.74 HHJ71_BPL52 GCS9 Cl* Melotte 22 HHJ 71 +03 43 11.77 +25 31 31.9 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 12.60 0.72 L07_A1_15 GCS9 +03 43 12.12 +24 44 45.1 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 1.92 0.61 HCG102_SK653_HHJ328_BPL53_DH255 GCS9 Cl* Melotte 22 HCG 102 +03 43 13.07 +24 39 19.3 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 1.16 0.91 HCG103_SK652_HHJ395_BPL54_DH256 GCS9 Cl* Melotte 22 HCG 103 +03 43 15.74 +25 20 29.5 0 13.094 12.779 12.277 11.779 11.507 11.524 15.79 2.23 -24.92 2.23 0.89 0.00 SK646 GCS9 Cl* Melotte 22 SK 646 +03 43 16.61 +23 50 01.5 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.89 0.92 HHJ206_BPL56_DH258 GCS9 Cl* Melotte 22 HHJ 206 +03 43 16.88 +23 59 56.4 0 18.673 17.896 17.104 16.502 16.126 0.042 31.62 2.69 -8.78 2.69 3.68 0.00 int-pl-IZ-24;IPLJ0343168+235956_N GCS9 Cl* Melotte 22 IPL 24 +03 43 18.47 +26 40 24.3 0 13.001 12.604 12.085 11.533 11.239 11.287 49.10 2.94 -45.26 2.94 0.27 0.00 HHJ413 GCS9 Cl* Melotte 22 HHJ 413 +03 43 18.56 +26 30 18.9 0 15.438 14.940 14.415 13.971 13.634 13.654 24.39 2.95 -50.80 2.95 0.64 0.07 HHJ89 GCS9 Cl* Melotte 22 HHJ 89 +03 43 19.01 +22 47 10.4 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 2.58 0.89 HCG110_HHJ143_DH259 GCS9 Cl* Melotte 22 HCG 110 +03 43 19.06 +26 04 43.9 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.67 0.77 SK642_HHJ279_DH260 GCS9 Cl* Melotte 22 SK 642 +03 43 20.58 +24 26 34.9 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.91 0.88 HCG106_HHJ88_BPL57_DH261_Moraux2003_79 GCS9 Cl* Melotte 22 HCG 106 +03 43 21.12 +24 11 09.0 0 15.279 14.803 14.182 13.683 13.346 13.361 11.26 2.13 -42.71 2.13 2.07 0.55 HHJ65 GCS9 Cl* Melotte 22 HHJ 65 +03 43 22.55 +23 00 56.5 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 2.24 0.70 L07_A1_16 GCS9 +03 43 24.99 +23 52 01.2 0 16.867 16.116 15.368 14.820 14.365 0.011 14.16 2.34 -46.02 2.34 1.59 0.53 int-pl-IZ-6;2MASSJ0343249+235201_BPL58_Y GCS9 Cl* Melotte 22 IPL 6 +03 43 25.16 +22 53 44.3 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 1.39 0.85 HHJ134_DH263_L07_179 GCS9 Cl* Melotte 22 HHJ 134 +03 43 26.20 +26 02 30.6 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 3.41 0.92 HCG105_SK633_HHJ327_DH264 GCS9 Cl* Melotte 22 HCG 105 +03 43 26.44 +22 42 42.5 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 1.69 0.56 HCG114_SK644_HHJ261_DH265_L07_189 GCS9 Cl* Melotte 22 HCG 114 +03 43 26.98 +24 27 09.6 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.43 0.92 SK638_HHJ368_BPL59_DH267_Moraux2003_6 GCS9 Cl* Melotte 22 SK 638 +03 43 27.44 +22 37 41.0 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 1.02 0.67 HHJ70 GCS9 Cl* Melotte 22 HHJ 70 +03 43 28.21 +24 53 30.9 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.59 0.87 HCG109_SK635_DH268 GCS9 Cl* Melotte 22 HCG 109 +03 43 28.74 +24 09 06.1 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.61 0.90 HHJ76 GCS9 Cl* Melotte 22 HHJ 76 +03 43 29.90 +24 39 23.4 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 4.29 0.52 HHJ50_DH269_Moraux2003_87 GCS9 Cl* Melotte 22 HHJ 50 +03 43 34.14 +25 35 25.8 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 1.22 0.93 HCG112_DH270_Moraux2003_19 GCS9 Cl* Melotte 22 HCG 112 +03 43 34.49 +25 57 30.6 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 16.35 0.40 L07_A1_17 GCS9 +03 43 35.22 +25 24 30.8 0 13.655 13.247 12.728 12.188 11.914 11.926 10.07 2.23 -45.01 2.23 1.09 0.30 SK622_HHJ337_DH272 GCS9 Cl* Melotte 22 SK 622 +03 43 36.57 +23 12 34.1 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 6.15 0.79 HCG122_T6_HHJ205_DH274_L07_174 GCS9 Cl* Melotte 22 HCG 122 +03 43 36.68 +25 47 00.5 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 1.31 0.85 SK619_DH276_Moraux2003_22 GCS9 Cl* Melotte 22 SK 619 +03 43 37.11 +23 38 31.9 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.60 0.91 SK630_DH278 GCS9 Cl* Melotte 22 SK 630 +03 43 37.32 +25 24 32.0 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 1.32 0.69 HCG115_SK620_HHJ377_DH279 GCS9 Cl* Melotte 22 HCG 115 +03 43 39.05 +23 44 05.2 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 5.74 0.93 HCG124_SK624_BPL61_DH281 GCS9 Cl* Melotte 22 HCG 124 +03 43 39.72 +23 41 32.4 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.60 0.86 HHJ40 GCS9 Cl* Melotte 22 HHJ 40 +03 43 40.31 +24 30 11.2 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2.96 0.36 BPL62_Roque7_CFHT-Pl-24_M8.3 GCS9 Cl* Melotte 22 BPL 62 +03 43 42.14 +24 34 23.1 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 3.04 0.87 HCG123_SK616_MT41_DH282 GCS9 Cl* Melotte 22 HCG 123 +03 43 42.90 +25 51 37.0 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 7.04 0.78 HHJ77_Moraux2003_81_L07_27 GCS9 Cl* Melotte 22 HHJ 77 +03 43 43.15 +24 32 56.0 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.40 0.69 DH283_Moraux2003_77 GCS9 Cl* Melotte 22 DH 283 +03 43 43.53 +24 12 50.0 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 1.15 0.89 HHJ43_DH284 GCS9 Cl* Melotte 22 HHJ 43 +03 43 43.71 +24 29 15.5 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2.25 0.55 HHJ374_BPL63_DH285_Moraux2003_5 GCS9 Cl* Melotte 22 HHJ 374 +03 43 44.09 +25 39 49.5 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 2.38 0.90 Moraux2003_72_L07_32 GCS9 Cl* Melotte 22 MBSC 72 +03 43 44.97 +23 03 21.1 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 2.21 0.84 SK618_HHJ321_DH286 GCS9 Cl* Melotte 22 SK 618 +03 43 46.29 +23 58 37.9 0 19.124 17.930 16.876 16.248 15.628 0.057 20.13 2.62 -45.01 2.62 6.23 0.69 int-pl-IZ-20;IPLJ0343462+235838_Y GCS9 Cl* Melotte 22 IPL 20 +03 43 47.08 +26 04 35.3 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 9.19 0.93 SK607_HHJ311_DH287 GCS9 Cl* Melotte 22 SK 607 +03 43 47.71 +28 15 59.3 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.74 0.90 DH2004_289 GCS9 Cl* Melotte 22 DH 289 +03 43 48.45 +25 02 36.7 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.33 0.93 HCG125_HHJ298_DH292 GCS9 Cl* Melotte 22 HCG 125 +03 43 50.56 +23 05 47.7 0 16.501 15.963 15.297 14.681 14.285 0.009 24.32 2.28 -51.66 2.28 5.95 0.02 int-pl-IZ-53_2MASSJ0343505+230548_Y_L07_241 GCS9 Cl* Melotte 22 IPL 53 +03 43 51.38 +21 09 03.1 0 14.541 14.138 13.586 13.020 12.740 12.734 30.15 2.65 -36.85 2.65 1.50 0.01 DH295 GCS9 Cl* Melotte 22 DH 295 +03 43 51.76 +24 14 15.9 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 1.45 0.90 HCG128_BPL65_DH296 GCS9 Cl* Melotte 22 HCG 128 +03 43 52.15 +24 50 29.5 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 4.37 0.60 HII191_HCG127_SK606_DH297 GCS9 HII191 +03 43 52.79 +25 29 30.3 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 4.71 0.83 HHJ218_DH298Moraux2003_37 GCS9 Cl* Melotte 22 HHJ 218 +03 43 53.55 +24 31 11.4 0 18.970 17.709 16.649 15.942 15.298 13.04 3.29 -53.30 3.29 6.40 0.02 BPL66_Roque4 GCS9 Cl* Melotte 22 BPL 66 +03 43 53.88 +25 28 30.0 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 3.97 0.76 HCG126_SK600_T69_HHJ391_DH299 GCS9 Cl* Melotte 22 HCG 126 +03 43 56.00 +25 36 25.2 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 11.61 0.36 PLZJ50_L07_A1_18_L07_249 GCS9 Cl* Melotte 22 PlZJ 50 +03 43 56.70 +25 15 43.9 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.51 0.86 SK596_DH300 GCS9 Cl* Melotte 22 SK 596 +03 43 56.71 +24 59 36.5 0 13.167 12.845 12.319 11.756 11.484 27.73 2.97 -45.73 2.97 1.35 0.13 HCG129_SK598_HHJ382_DH301 GCS9 Cl* Melotte 22 HCG 129 +03 43 57.00 +23 57 05.7 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 1.37 0.90 HCG135_HHJ229_DH302 GCS9 Cl* Melotte 22 HCG 135 +03 43 57.29 +24 13 20.3 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 1.14 0.72 HCG133_SK601_BPL67_DH303 GCS9 Cl* Melotte 22 HCG 133 +03 44 02.29 +25 03 53.5 0 12.928 12.605 12.098 11.550 11.827 17.46 2.97 -49.03 2.97 1.22 0.26 HCG134_SK591_T150_DH306 GCS9 Cl* Melotte 22 HCG 134 +03 44 02.50 +21 13 15.8 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.41 0.85 DH307 GCS9 Cl* Melotte 22 DH 307 +03 44 05.25 +22 50 13.5 0 20.066 18.839 17.666 16.895 16.134 0.147 19.70 3.13 -45.22 3.13 3.08 0.57 int-pl-IZ-57;IPLJ0344052+225014_N GCS9 Cl* Melotte 22 IPL 57 +03 44 05.63 +23 03 42.4 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 1.94 0.90 L07_180 GCS9 +03 44 08.81 +23 04 47.5 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 1.05 0.39 HCG141_SK590_T71_DH312 GCS9 Cl* Melotte 22 HCG 141 +03 44 09.32 +23 08 46.8 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 4.90 0.91 HHJ186_DH313_L07_172 GCS9 Cl* Melotte 22 HHJ 186 +03 44 09.61 +24 35 22.1 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.60 0.89 SK586_DH314 GCS9 Cl* Melotte 22 SK 586 +03 44 09.92 +24 16 03.9 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 0.27 0.57 HCG140_DH315 GCS9 Cl* Melotte 22 HCG 140 +03 44 10.76 +25 37 38.2 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 6.39 0.62 HHJ235_DH316_Moraux2003_36 GCS9 Cl* Melotte 22 HHJ 235 +03 44 11.28 +24 52 34.2 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 3.05 0.09 HHJ59 GCS9 Cl* Melotte 22 HHJ 59 +03 44 11.90 +22 53 36.9 0 15.170 14.818 14.272 13.668 13.416 13.423 12.90 2.26 -60.48 2.26 6.74 0.00 L07_178 GCS9 +03 44 11.92 +19 18 19.1 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 2.20 0.77 DH318 GCS9 Cl* Melotte 22 DH 318 +03 44 12.14 +23 52 37.3 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 1.87 0.87 HHJ217_DH319 GCS9 Cl* Melotte 22 HHJ 217 +03 44 13.97 +25 32 15.3 0 13.648 13.147 12.531 11.991 11.643 11.691 -5.25 2.23 -36.15 2.23 5.79 0.00 HCG138_SK579_DH320_Moraux2003_25 GCS9 Cl* Melotte 22 HCG 138 +03 44 16.46 +23 37 04.0 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 1.22 0.93 HCG144_SK580_HHJ301_DH322 GCS9 Cl* Melotte 22 HCG 144 +03 44 17.76 +24 26 46.7 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.14 0.88 HCG145_SK576_HHJ352_DH323 GCS9 Cl* Melotte 22 HCG 145 +03 44 19.06 +24 35 18.2 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.54 0.90 HCG146_T18B_HHJ228_DH324 GCS9 Cl* Melotte 22 HCG 146 +03 44 20.66 +24 15 10.7 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 0.51 0.59 HHJ57 GCS9 Cl* Melotte 22 HHJ 57 +03 44 20.87 +23 33 39.8 0 13.825 13.404 12.863 12.282 11.993 3.71 2.30 -36.55 2.30 6.76 0.00 HCG155_SK575_HHJ300_DH327 GCS9 Cl* Melotte 22 HCG 155 +03 44 22.14 +23 10 54.8 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 1.84 0.88 DH329_L07_173 GCS9 Cl* Melotte 22 DH 329 +03 44 22.45 +23 39 01.3 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 3.20 0.61 Roque5_L07_A1_19_L07_261 GCS9 Cl* Melotte 22 Roque 5 +03 44 23.24 +25 38 44.9 0 16.319 15.457 14.698 14.154 13.731 13.721 18.31 2.25 -50.40 2.25 6.34 0.20 BRB4_PLZJ29_L07_A1_78 GCS9 Cl* Melotte 22 BRB 4 +03 44 23.40 +25 21 29.9 0 13.413 12.983 12.404 11.920 11.628 11.662 8.92 2.23 -42.15 2.23 9.13 0.15 HCG143_SK568_T19B_HHJ348_DH331 GCS9 Cl* Melotte 22 HCG 143 +03 44 24.00 +21 24 20.8 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 3.40 0.89 DH332 GCS9 Cl* Melotte 22 DH 332 +03 44 24.68 +24 51 53.2 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 1.21 0.86 HCG148_SK569_HHJ307_DH333 GCS9 Cl* Melotte 22 HCG 148 +03 44 24.82 +24 46 06.0 0 12.688 12.372 11.878 11.507 11.202 17.32 2.97 -50.88 2.97 2.49 0.07 HCG149_SK570_T42B_DH334 GCS9 Cl* Melotte 22 HCG 149 +03 44 25.08 +25 34 03.9 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 8.95 0.88 HHJ100_Moraux2003_75_L07_31 GCS9 Cl* Melotte 22 HHJ 100 +03 44 25.51 +22 27 49.8 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 1.49 0.35 DH335_int-pl-IZ-72;2MASSJ0344255+222749_Y GCS9 Cl* Melotte 22 DH 335 +03 44 25.58 +22 40 07.9 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 34.57 0.28 L07_A1_21_L07_246 GCS9 +03 44 25.60 +24 40 52.6 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 1.86 0.62 HCG150_SK567_B179_HHJ364_DH336 GCS9 Cl* Melotte 22 HCG 150 +03 44 26.54 +24 29 11.3 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 1.10 0.91 HHJ243_DH338 GCS9 Cl* Melotte 22 HHJ 243 +03 44 26.89 +24 24 31.5 0 13.155 12.822 12.306 11.723 11.468 3.74 2.97 -30.56 2.97 12.86 0.00 HCG156_HHJ400_DH339 GCS9 Cl* Melotte 22 HCG 156 +03 44 27.50 +24 14 17.0 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.91 0.92 HHJ190_DH341_L07_97 GCS9 Cl* Melotte 22 HHJ 190 +03 44 27.92 +23 59 59.6 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 2.29 0.89 DH342_L07_113 GCS9 Cl* Melotte 22 DH 342 +03 44 30.08 +25 35 46.8 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 7.54 0.81 HCG152 GCS9 Cl* Melotte 22 HCG 152 +03 44 31.04 +22 15 14.9 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2.16 0.26 SK571_DH344 GCS9 Cl* Melotte 22 SK 571 +03 44 31.72 +23 35 26.0 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 1.12 0.94 SK564_HHJ265_DH345 GCS9 Cl* Melotte 22 SK 564 +03 44 32.17 +25 08 12.3 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 7.50 0.90 HHJ68_Moraux2003_85_L07_51 GCS9 Cl* Melotte 22 HHJ 68 +03 44 32.33 +25 25 17.9 0 16.994 16.209 15.450 14.883 14.476 14.883 14.24 2.27 -43.51 2.27 5.78 0.64 CFHT-Pl-10_M6.5_PLZJ60_L07_A1_22_L07_250 GCS9 Cl* Melotte 22 CFHT 10 +03 44 33.08 +25 45 09.5 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.96 0.71 HCG157_DH346_Moraux2003_40 GCS9 Cl* Melotte 22 HCG 157 +03 44 33.49 +26 14 53.1 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.25 0.93 HHJ334_DH347 GCS9 Cl* Melotte 22 HHJ 334 +03 44 34.30 +23 51 24.6 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 3.77 0.74 L07_A1_23 GCS9 +03 44 35.16 +25 13 42.8 1 17.656 16.584 15.662 14.985 14.448 14.985 19.33 2.26 -44.97 2.26 16.37 0.68 CFHT-Pl-16_M9.3_L07_A1_24_L07_251 GCS9 Cl* Melotte 22 CFHT 16 +03 44 35.90 +23 34 41.9 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 1.71 0.72 HHJ5_L07_A1_25_L07_236 GCS9 Cl* Melotte 22 HHJ 5 +03 44 35.92 +22 50 42.9 0 14.238 13.940 13.439 12.803 12.544 12.552 34.09 2.24 -47.63 2.24 0.63 0.00 L07_197 GCS9 +03 44 36.28 +23 30 10.9 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 7.84 0.93 HCG161_SK559_A23_HHJ344_DH348 GCS9 Cl* Melotte 22 HCG 161 +03 44 37.78 +22 55 15.5 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.64 0.88 HCG164 GCS9 Cl* Melotte 22 HCG 164 +03 44 38.95 +23 02 25.3 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 2.32 0.92 HHJ189_DH351 GCS9 Cl* Melotte 22 HHJ 189 +03 44 40.31 +25 19 24.6 0 15.831 15.228 14.599 14.042 13.700 13.703 -10.25 2.24 -47.03 2.24 7.09 0.00 L07_45 GCS9 +03 44 46.14 +24 23 02.8 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 6.36 0.86 HHJ24_L07_99 GCS9 Cl* Melotte 22 HHJ 24 +03 44 47.31 +19 55 41.4 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.63 0.85 DH354 GCS9 Cl* Melotte 22 DH 354 +03 44 47.33 +24 00 37.7 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 4.41 0.94 HHJ276_HCG167_DH355_L07_9 GCS9 Cl* Melotte 22 HHJ 276 +03 44 47.84 +24 12 52.5 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 2.19 0.94 HCG166_HHJ239_DH357_L07_96 GCS9 Cl* Melotte 22 HCG 166 +03 44 51.51 +25 05 16.5 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 1.69 0.91 L07_49 GCS9 +03 44 53.12 +23 34 22.8 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 3.81 0.67 L07_A1_26_L07_235 GCS9 +03 44 53.21 +24 01 06.5 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 3.95 0.80 DH2004_359 GCS9 Cl* Melotte 22 DH 359 +03 44 53.53 +25 36 19.2 0 16.354 15.837 15.249 14.663 14.358 14.353 25.93 2.25 -40.81 2.25 4.48 0.14 PLZJ56_None GCS9 Cl* Melotte 22 PlZJ 56 +03 44 56.01 +23 55 53.4 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 1.00 0.91 HCG171_HHJ304 GCS9 Cl* Melotte 22 HCG 171 +03 44 56.69 +23 36 23.5 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 8.34 0.85 HHJ269_DH361 GCS9 Cl* Melotte 22 HHJ 269 +03 44 58.02 +23 24 31.0 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 2.38 0.79 HHJ223_DH362 GCS9 Cl* Melotte 22 HHJ 223 +03 44 58.59 +23 55 40.9 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 2.02 0.87 HHJ274_DH363 GCS9 Cl* Melotte 22 HHJ 274 +03 44 59.48 +23 21 18.1 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 1.03 0.77 HHJ54_DH365_L07_156 GCS9 Cl* Melotte 22 HHJ 54 +03 45 01.14 +24 46 40.9 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 3.65 0.71 HCG172_SK538_HHJ216_DH366_L07_74 GCS9 Cl* Melotte 22 HCG 172 +03 45 01.21 +25 21 05.6 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 5.45 0.89 DH367_Moraux2003_74_L07_46 GCS9 Cl* Melotte 22 DH 367 +03 45 02.88 +25 05 19.6 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 1.84 0.75 HHJ139_DH368_Moraux2003_65_L07_50 GCS9 Cl* Melotte 22 HHJ 139 +03 45 03.18 +23 06 58.4 0 16.937 15.492 14.946 14.495 0.012 20.81 2.32 -40.36 2.32 5.52 0.62 int-pl-IZ-60;2MASSJ0345031+230658_Y GCS9 Cl* Melotte 22 IPL 60 +03 45 04.41 +24 15 16.6 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 0.80 0.45 L07_A1_27 GCS9 +03 45 04.99 +23 46 06.4 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 2.75 0.91 HHJ113_DH372 GCS9 Cl* Melotte 22 HHJ 113 +03 45 05.32 +25 29 10.9 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 4.32 0.92 SK534_HHJ381_DH373 GCS9 Cl* Melotte 22 SK 534 +03 45 06.25 +28 42 19.1 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.12 0.85 DH374 GCS9 Cl* Melotte 22 DH 374 +03 45 06.56 +24 40 42.7 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 6.44 0.71 HHJ48_L07_73 GCS9 Cl* Melotte 22 HHJ 48 +03 45 06.79 +23 36 51.4 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 3.68 0.92 HCG176_HHJ136 GCS9 Cl* Melotte 22 HCG 176 +03 45 08.41 +23 25 00.9 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 3.78 0.79 L07_157 GCS9 +03 45 08.69 +22 38 30.3 0 14.314 13.895 13.322 12.787 12.483 12.496 27.40 2.51 -36.47 2.51 19.46 0.06 HHJ204_BPL70_DH376_L07_194 GCS9 Cl* Melotte 22 HHJ 204 +03 45 08.69 +24 24 09.3 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 1.73 0.74 L07_A1_28 GCS9 +03 45 09.04 +25 22 29.7 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 3.96 0.86 HHJ81_L07_47 GCS9 Cl* Melotte 22 HHJ 81 +03 45 09.04 +25 32 49.0 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 1.78 0.94 HHJ164_DH377_Moraux2003_61 GCS9 Cl* Melotte 22 HHJ 164 +03 45 09.46 +23 58 44.7 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 13.16 0.72 L07_A1_29 GCS9 +03 45 10.82 +23 02 58.0 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 4.56 0.92 HCG186_SK535 GCS9 Cl* Melotte 22 HCG 186 +03 45 12.16 +23 21 52.9 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 5.28 0.93 HCG185_SK532_HHJ255_DH379 GCS9 Cl* Melotte 22 HCG 185 +03 45 12.44 +22 41 50.8 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 1.91 0.87 BPL71_SK533_HHJ254_DH380_L07_196 GCS9 Cl* Melotte 22 BPL 71 +03 45 12.62 +23 53 45.0 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 3.02 0.71 HHJ14_PPL7_L07_133 GCS9 Cl* Melotte 22 HHJ 14 +03 45 13.14 +24 15 23.6 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 1.18 0.86 HCG180_HHJ118_DH381_L07_98 GCS9 Cl* Melotte 22 HCG 180 +03 45 14.21 +25 05 19.4 0 12.232 11.987 11.572 11.411 10.770 11.068 -49.24 2.21 -120.03 2.21 2.79 0.00 HII566_HCG174 GCS9 HII566 +03 45 15.82 +25 06 36.5 0 12.587 12.238 11.785 11.593 10.986 11.109 16.46 2.21 -62.54 2.21 1.44 0.00 SK526_HHJ425 GCS9 Cl* Melotte 22 SK 526 +03 45 16.13 +24 07 16.0 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 1.79 0.91 HCG183_HHJ394_DH383 GCS9 Cl* Melotte 22 HCG 183 +03 45 16.42 +23 34 01.6 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 1.55 0.89 DH384 GCS9 Cl* Melotte 22 DH 384 +03 45 16.99 +25 15 47.5 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 2.50 0.89 HCG178_SK525_A28_HHJ410_DH386 GCS9 Cl* Melotte 22 HCG 178 +03 45 18.15 +25 05 58.1 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 2.36 0.74 HII590_HCG179_SK524_T45_DH387 GCS9 HII590 +03 45 21.12 +21 46 17.6 0 16.257 15.741 15.178 14.532 14.239 14.299 8.25 2.54 -32.01 2.54 32.48 0.00 L07_PM_NM_49 GCS9 +03 45 21.91 +26 28 41.9 0 14.370 14.041 13.523 12.837 12.615 12.636 32.97 2.94 -40.02 2.94 0.85 0.00 DH389 GCS9 Cl* Melotte 22 DH 389 +03 45 22.14 +21 52 40.0 0 16.265 15.746 15.102 14.554 14.203 14.180 -1.65 2.29 -61.68 2.29 5.14 0.00 L07_PM_NM_50 GCS9 +03 45 24.70 +24 38 46.4 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 1.24 0.89 L07_72 GCS9 +03 45 24.79 +24 20 45.3 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 8.47 0.85 DH392 GCS9 Cl* Melotte 22 DH 392 +03 45 26.56 +22 31 32.0 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 5.52 0.67 HHJ267_BPL73_DH393_L07_199 GCS9 Cl* Melotte 22 HHJ 267 +03 45 26.99 +24 13 26.4 0 15.192 14.757 14.205 13.605 13.284 13.278 23.59 2.22 -49.95 2.22 2.92 0.17 HHJ99 GCS9 Cl* Melotte 22 HHJ 99 +03 45 27.52 +23 37 56.9 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 1.56 0.90 HHJ106_L07_147 GCS9 Cl* Melotte 22 HHJ 106 +03 45 28.90 +27 28 07.2 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.75 0.88 DH394 GCS9 Cl* Melotte 22 DH 394 +03 45 30.23 +24 18 45.3 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 1.47 0.77 HII673 GCS9 HII673 +03 45 31.25 +25 46 33.3 0 14.192 13.836 13.341 12.732 12.466 12.457 29.01 2.23 -50.04 2.23 1.37 0.01 HHJ293 GCS9 Cl* Melotte 22 HHJ 293 +03 45 31.37 +24 52 47.4 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 7.98 0.66 IPMBD29_L07_A1_30 GCS9 Cl* Melotte 22 IPMBD 29 +03 45 35.69 +24 24 34.1 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 51.10 0.74 L07_A1_31 GCS9 +03 45 36.72 +24 39 06.5 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 19.04 0.85 HCG194_HHJ380_DH397 GCS9 Cl* Melotte 22 HCG 194 +03 45 37.76 +23 43 50.1 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2.10 0.59 L07_A1_32_L07_238 GCS9 +03 45 37.79 +24 20 08.1 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 11.15 0.93 HII717_HD23387_Tr215 GCS9 HII717 +03 45 38.99 +23 57 00.9 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 13.30 0.69 L07_130 GCS9 +03 45 39.04 +25 13 27.6 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 5.94 0.82 HCG196_SK510 GCS9 Cl* Melotte 22 HCG 196 +03 45 39.12 +22 04 23.1 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 3.36 0.67 BPL75_L07_210 GCS9 Cl* Melotte 22 BPL 75 +03 45 39.29 +24 08 20.4 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 1.38 0.93 HHJ130_DH398_L07_117 GCS9 Cl* Melotte 22 HHJ 130 +03 45 40.77 +28 32 05.8 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.16 0.75 DH399 GCS9 Cl* Melotte 22 DH 399 +03 45 41.27 +23 54 09.7 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 3.49 0.69 Roque15_L07_A1_33_L07_234 GCS9 Cl* Melotte 22 Roque 15 +03 45 42.33 +24 04 11.1 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 11.53 0.70 L07_A1_34_L07_227 GCS9 +03 45 43.18 +26 02 26.6 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 6.76 0.94 HHJ264_DH401 GCS9 Cl* Melotte 22 HHJ 264 +03 45 44.08 +24 04 26.6 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 10.09 0.78 HII762_Tr228a_HCG200 GCS9 HII762 +03 45 45.41 +22 33 21.5 0 15.557 15.172 14.632 14.051 13.736 13.741 -0.07 2.27 -31.02 2.27 8.08 0.00 L07_200 GCS9 +03 45 46.48 +23 47 43.1 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 2.51 0.89 HHJ421 GCS9 Cl* Melotte 22 HHJ 421 +03 45 46.90 +23 53 00.3 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 3.12 0.88 L07_129 GCS9 +03 45 46.94 +21 44 49.0 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 1.97 0.54 HHJ128 GCS9 Cl* Melotte 22 HHJ 128 +03 45 49.26 +25 24 46.1 0 16.825 16.314 15.704 15.073 14.758 14.754 26.80 2.29 -31.90 2.29 3.20 0.00 DH403 GCS9 Cl* Melotte 22 DH 403 +03 45 49.37 +24 25 07.1 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 5.99 0.93 BPL77_L07_7 GCS9 Cl* Melotte 22 BPL 77 +03 45 49.94 +23 19 44.7 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 2.39 0.66 L07_155 GCS9 +03 45 50.42 +22 36 05.6 0 17.653 16.839 16.051 15.524 15.034 0.021 14.53 2.39 -32.89 2.39 8.07 0.05 int-pl-IZ-44;2MASSJ0345504+223606_BPL78_Y_L07_A1_35_L07_247 GCS9 Cl* Melotte 22 IPL 44 +03 45 50.66 +24 09 03.5 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 1.10 0.65 BPL79_Roque13_L07_A1_36 GCS9 Cl* Melotte 22 BPL 79 +03 45 51.09 +24 26 10.9 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 1.18 0.84 HHJ25_L07_86 GCS9 Cl* Melotte 22 HHJ 25 +03 45 51.33 +24 17 44.3 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 8.20 0.75 L07_92 GCS9 +03 45 51.64 +24 02 19.7 0 12.518 12.529 11.248 11.845 10.016 30.83 2.22 -25.84 2.22 9.31 0.00 HII804_HD23409_Tr235 GCS9 HII804 +03 45 51.95 +25 10 01.7 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 7.90 0.92 HHJ166_BPL80_DH404_Moraux2003_54_L07_54 GCS9 Cl* Melotte 22 HHJ 166 +03 45 52.60 +25 54 59.8 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 1.59 0.92 SK499_DH405 GCS9 Cl* Melotte 22 SK 499 +03 45 52.76 +23 27 54.0 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 4.63 0.49 HCG202_SK504_HHJ227_DH407 GCS9 Cl* Melotte 22 HCG 202 +03 45 54.96 +23 33 57.8 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 5.98 0.72 L07_A1_38_L07_237 GCS9 +03 45 54.99 +24 13 26.0 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.61 0.94 BPL82 GCS9 Cl* Melotte 22 BPL 82 +03 45 56.97 +23 01 29.0 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 1.23 0.90 HCG205_HHJ199_DH410 GCS9 Cl* Melotte 22 HCG 205 +03 45 57.09 +24 21 00.9 0 15.058 14.437 13.779 13.215 12.854 12.842 8.36 2.22 -35.88 2.22 11.01 0.01 L07_94 GCS9 +03 45 57.28 +25 11 12.7 0 13.246 12.837 12.272 12.049 11.431 11.482 11.26 2.21 -43.67 2.21 7.13 0.55 SK497_HHJ379_BPL83_DH411_Moraux2003_7 GCS9 Cl* Melotte 22 SK 497 +03 45 57.71 +24 03 04.9 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 4.80 0.59 HHJ27_L07_116 GCS9 Cl* Melotte 22 HHJ 27 +03 45 57.78 +26 18 44.4 0 13.541 13.217 12.726 12.101 11.875 11.872 36.22 2.94 -32.09 2.94 0.34 0.00 Moraux2003_14 GCS9 Cl* Melotte 22 MBSC 14 +03 45 57.91 +24 08 40.9 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 2.70 0.90 DH412_L07_118 GCS9 Cl* Melotte 22 DH 412 +03 45 58.80 +26 20 01.7 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.40 0.81 HHJ67_DH413_Moraux2003_86 GCS9 Cl* Melotte 22 HHJ 67 +03 45 59.20 +23 48 46.8 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 4.75 0.92 HHJ127 GCS9 Cl* Melotte 22 HHJ 127 +03 46 00.93 +22 12 29.4 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 9.09 0.68 HHJ11_BPL84 GCS9 Cl* Melotte 22 HHJ 11 +03 46 02.26 +25 36 40.4 0 12.980 12.632 12.114 11.635 11.296 11.366 31.82 2.23 -37.17 2.23 6.80 0.00 SK491_HHJ399 GCS9 Cl* Melotte 22 SK 491 +03 46 02.53 +22 28 27.5 0 16.837 16.315 15.700 15.098 14.791 0.012 7.00 2.33 -22.57 2.33 3.08 0.00 int-pl-IZ-64;2MASSJ0346025+222827_N GCS9 Cl* Melotte 22 IPL 64 +03 46 02.96 +24 40 55.6 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 7.46 0.83 BPL85_DH414 GCS9 Cl* Melotte 22 BPL 85 +03 46 03.45 +24 20 57.0 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 3.54 0.46 HCG206_BPL86_DH415_L07_93 GCS9 Cl* Melotte 22 HCG 206 +03 46 03.67 +25 52 28.8 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 3.39 0.94 HHJ182_DH416_L07_26 GCS9 Cl* Melotte 22 HHJ 182 +03 46 03.96 +24 23 08.9 0 15.655 15.066 14.448 13.848 13.511 13.513 31.07 2.23 0.78 2.23 0.19 0.00 BPL87 GCS9 Cl* Melotte 22 BPL 87 +03 46 04.30 +23 55 40.6 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 3.63 0.84 HHJ363 GCS9 Cl* Melotte 22 HHJ 363 +03 46 04.57 +24 09 55.8 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 2.54 0.84 BPL88 GCS9 Cl* Melotte 22 BPL 88 +03 46 05.64 +24 36 44.3 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 3.04 0.93 SK488 GCS9 Cl* Melotte 22 SK 488 +03 46 05.69 +24 36 49.7 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 4.06 0.87 L07_88 GCS9 +03 46 06.05 +23 58 19.1 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 21.77 0.73 L07_114 GCS9 +03 46 06.21 +25 06 45.8 0 14.228 14.006 13.550 12.936 12.838 12.837 12.78 2.21 -34.39 2.21 1.09 0.08 L07_53 GCS9 +03 46 06.46 +25 25 11.5 0 14.674 14.289 13.759 13.117 12.837 12.862 18.32 2.23 -26.35 2.23 1.79 0.00 HHJ194_Moraux2003_59 GCS9 Cl* Melotte 22 HHJ 194 +03 46 06.52 +23 50 20.2 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 3.27 0.81 HHJ435 GCS9 Cl* Melotte 22 HHJ 435 +03 46 06.67 +22 23 33.7 0 12.888 12.512 12.011 11.499 11.168 11.310 42.71 2.26 -18.94 2.26 1.18 0.00 BPL89 GCS9 Cl* Melotte 22 BPL 89 +03 46 07.51 +24 22 27.6 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.96 0.89 HII890_HCG210 GCS9 HII890 +03 46 07.56 +23 44 42.6 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 1.77 0.70 L07_239 GCS9 +03 46 08.70 +24 40 33.1 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 5.02 0.93 HCG209_BPL90_DH422 GCS9 Cl* Melotte 22 HCG 209 +03 46 09.88 +21 05 52.4 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 5.12 0.46 DH425 GCS9 Cl* Melotte 22 DH 425 +03 46 10.10 +26 00 09.0 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.60 0.82 HHJ80_L07_17 GCS9 Cl* Melotte 22 HHJ 80 +03 46 10.23 +21 52 55.8 0 16.174 15.346 14.578 13.994 13.570 13.564 43.56 2.27 -44.28 2.27 1.50 0.00 L07_A1_42 GCS9 +03 46 12.67 +23 35 13.6 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 4.56 0.94 L07_146 GCS9 +03 46 12.88 +24 03 15.6 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 6.72 0.81 HII930_DH429 GCS9 Cl* Melotte 22 DH 429 +03 46 13.00 +27 02 11.7 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 1.60 0.82 DH430 GCS9 Cl* Melotte 22 DH 430 +03 46 14.06 +23 21 56.4 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 7.96 0.68 L07_A1_43 GCS9 +03 46 15.84 +22 02 41.0 0 13.656 13.264 12.746 12.128 11.854 11.872 24.87 2.26 -14.34 2.26 2.45 0.00 BPL93 GCS9 Cl* Melotte 22 BPL 93 +03 46 16.85 +24 26 27.3 0 14.565 14.151 13.599 13.055 12.761 12.781 35.34 2.21 -3.40 2.21 3.16 0.00 BPL94 GCS9 Cl* Melotte 22 BPL 94 +03 46 17.94 +24 41 09.3 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 7.95 0.93 HHJ367_BPL95_DH433 GCS9 Cl* Melotte 22 HHJ 367 +03 46 18.54 +23 59 02.5 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 36.19 0.88 L07_115 GCS9 +03 46 19.41 +23 00 55.7 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 3.01 0.80 HHJ47_BPL96_DH435 GCS9 Cl* Melotte 22 HHJ 47 +03 46 19.43 +26 02 35.5 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 4.18 0.74 SK474_DH436 GCS9 Cl* Melotte 22 SK 474 +03 46 19.86 +24 59 01.3 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 2.73 0.88 HHJ303_BPL97_DH437_Moraux2003_27 GCS9 Cl* Melotte 22 HHJ 303 +03 46 20.65 +23 53 31.7 0 15.607 15.199 14.655 13.992 13.738 13.729 10.57 2.23 -42.96 2.23 9.25 0.44 L07_125 GCS9 +03 46 21.36 +24 33 52.2 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 3.50 0.83 HHJ105_BPL98_DH439 GCS9 Cl* Melotte 22 HHJ 105 +03 46 21.40 +23 05 08.9 0 15.684 15.157 14.548 14.032 13.690 13.664 11.08 2.24 -47.42 2.24 27.35 0.29 HHJ33 GCS9 Cl* Melotte 22 HHJ 33 +03 46 21.62 +23 04 00.9 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 22.02 0.93 DH440 GCS9 Cl* Melotte 22 DH 440 +03 46 22.20 +23 52 41.0 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 4.55 0.91 DH441 GCS9 Cl* Melotte 22 DH 441 +03 46 22.25 +23 52 26.6 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 4.78 0.56 L07_A1_44_L07_232 GCS9 +03 46 23.03 +24 36 17.9 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 19.52 0.93 BPL99_DH442 GCS9 Cl* Melotte 22 BPL 99 +03 46 23.12 +24 20 36.0 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 3.71 0.66 BPL100_Roque9_L07_A1_45 GCS9 Cl* Melotte 22 BPL 100 +03 46 23.47 +24 01 51.2 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 5.91 0.94 HCG218_HHJ195_DH443 GCS9 Cl* Melotte 22 HCG 218 +03 46 23.72 +22 50 16.4 0 17.310 15.710 15.189 14.722 0.015 20.26 2.35 -45.42 2.35 5.66 0.64 int-pl-IZ-43;2MASSJ0346237+225016_Y GCS9 Cl* Melotte 22 IPL 43 +03 46 23.74 +26 34 23.0 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.60 0.73 HHJ175_DH444_Moraux2003_62 GCS9 Cl* Melotte 22 HHJ 175 +03 46 24.12 +24 30 12.7 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 3.56 0.90 BPL101 GCS9 Cl* Melotte 22 BPL 101 +03 46 24.64 +24 28 46.2 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 2.85 0.93 HHJ249_BPL102_DH445_L07_87 GCS9 Cl* Melotte 22 HHJ 249 +03 46 25.18 +21 26 17.4 0 13.441 12.930 12.360 11.842 11.508 11.529 3.09 2.65 -44.47 2.65 1.82 0.00 DH446 GCS9 Cl* Melotte 22 DH 446 +03 46 25.39 +24 09 36.2 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.43 0.64 HCG219_A2_HHJ429 GCS9 Cl* Melotte 22 HCG 219 +03 46 26.09 +24 05 09.5 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 3.37 0.57 IPMBD25_L07_A1_46_L07_230 GCS9 Cl* Melotte 22 IPMBD 25 +03 46 27.01 +24 27 13.9 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 2.66 0.82 HHJ326_BPL103_DH447 GCS9 Cl* Melotte 22 HHJ 326 +03 46 27.10 +21 48 22.6 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 1.27 0.65 L07_A1_47 GCS9 +03 46 27.69 +23 48 45.5 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 1.69 0.94 HHJ161 GCS9 Cl* Melotte 22 HHJ 161 +03 46 28.63 +24 45 32.1 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 8.09 0.81 HII1029_HCG222_SK472_B212_DH451 GCS9 HII1029 +03 46 29.73 +21 02 16.7 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.73 0.92 DH452 GCS9 Cl* Melotte 22 DH 452 +03 46 30.96 +23 00 15.1 0 14.562 14.242 13.760 13.105 12.885 12.870 29.31 2.24 -51.76 2.24 0.62 0.00 L07_181 GCS9 +03 46 31.02 +23 01 34.6 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 5.54 0.83 HHJ30_DH453 GCS9 Cl* Melotte 22 HHJ 30 +03 46 31.32 +22 18 19.6 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 4.04 0.92 HHJ160_BPL105_L07_204 GCS9 Cl* Melotte 22 HHJ 160 +03 46 32.13 +24 23 14.6 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 3.18 0.57 L07_A1_48 GCS9 +03 46 32.69 +22 20 17.7 0 14.509 14.126 13.585 12.902 12.626 12.625 30.13 2.26 -27.71 2.26 1.50 0.00 SK471 GCS9 Cl* Melotte 22 SK 471 +03 46 32.79 +19 17 30.2 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 1.46 0.90 DH455 GCS9 Cl* Melotte 22 DH 455 +03 46 34.16 +23 25 12.5 0 16.303 15.778 15.088 14.398 14.055 14.041 11.49 2.25 -25.44 2.25 5.43 0.00 HHJ9_L07_PM_NM_20 GCS9 Cl* Melotte 22 HHJ 9 +03 46 34.25 +23 50 03.6 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 3.43 0.66 L07_A1_49_L07_260 GCS9 +03 46 34.99 +23 31 14.4 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 4.06 0.70 L07_A1_50 GCS9 +03 46 35.36 +23 57 07.4 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 11.08 0.75 L07_A1_51_L07_233 GCS9 +03 46 35.54 +24 01 35.4 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.77 0.89 HHJ140_DH458 GCS9 Cl* Melotte 22 HHJ 140 +03 46 36.07 +23 04 17.2 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 5.66 0.94 SK465_HHJ282_DH459 GCS9 Cl* Melotte 22 SK 465 +03 46 39.33 +24 06 11.3 0 12.740 12.808 11.163 12.306 9.936 10.900 30.68 2.22 -50.62 2.22 33.59 0.00 HII1122_HD23511_Tr327 GCS9 HII1122 +03 46 40.27 +25 43 53.4 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 10.61 0.91 SK461_HHJ316_DH464_L07_3 GCS9 Cl* Melotte 22 SK 461 +03 46 40.41 +22 50 39.6 0 15.138 14.065 13.514 13.194 13.182 21.74 2.28 -48.62 2.28 4.41 0.50 BPL107 GCS9 Cl* Melotte 22 BPL 107 +03 46 40.60 +22 22 03.5 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 4.92 0.88 L07_206 GCS9 +03 46 43.19 +23 37 21.9 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 6.11 0.86 HHJ104_DH466_L07_138 GCS9 Cl* Melotte 22 HHJ 104 +03 46 43.60 +23 59 42.3 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.97 0.91 DH467 GCS9 Cl* Melotte 22 DH 467 +03 46 44.79 +24 44 58.2 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 4.59 0.90 HHJ75_BPL109_L07_67 GCS9 Cl* Melotte 22 HHJ 75 +03 46 44.88 +23 37 07.3 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 5.80 0.83 L07_137 GCS9 +03 46 45.78 +25 27 30.2 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 2.85 0.88 HCG233_SK458_T25B_HHJ319_Moraux2003_20_DH2004_468 GCS9 Cl* Melotte 22 HCG 233 +03 46 46.48 +23 24 02.8 0 14.716 14.273 13.660 13.004 12.658 12.671 33.44 2.24 -34.98 2.24 7.11 0.00 HHJ159 GCS9 Cl* Melotte 22 HHJ 159 +03 46 47.19 +25 20 53.1 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 8.06 0.66 HHJ208_DH469_Moraux2003_45_L07_39 GCS9 Cl* Melotte 22 HHJ 208 +03 46 48.79 +23 04 07.3 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 3.00 0.90 HCG245_HHJ370_DH470 GCS9 Cl* Melotte 22 HCG 245 +03 46 50.03 +24 00 23.6 0 17.416 16.456 15.617 15.025 14.512 14.503 16.75 2.25 -34.16 2.25 3.06 0.16 L07_A1_53_L07_229 GCS9 +03 46 50.09 +23 31 56.1 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 4.16 0.94 HCG240_HHJ253_DH472_L07_134 GCS9 Cl* Melotte 22 HCG 240 +03 46 50.19 +22 12 42.3 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 3.93 0.87 BPL110_L07_203 GCS9 Cl* Melotte 22 BPL 110 +03 46 51.83 +23 23 09.4 0 19.654 18.417 17.182 16.429 15.651 15.690 19.04 2.55 -23.47 2.55 10.80 0.00 L07_A1_54 GCS9 +03 46 52.29 +21 47 43.0 0 16.720 16.080 15.402 14.797 14.405 14.418 24.38 2.30 -20.65 2.30 1.45 0.00 L07_PM_NM_24 GCS9 +03 46 52.61 +23 38 43.0 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 5.33 0.92 HHJ247_DH473 GCS9 Cl* Melotte 22 HHJ 247 +03 46 52.97 +24 15 07.8 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 5.64 0.33 BPL112_L07_A1_55 GCS9 Cl* Melotte 22 BPL 112 +03 46 53.61 +24 17 14.8 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 6.95 0.85 HCG244_SK454_BPL113_DH474 GCS9 Cl* Melotte 22 HCG 244 +03 46 53.96 +24 07 57.1 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 1.08 0.78 15.76 GCS9 Cl* Melotte 22 MHO 8 +03 46 54.03 +25 14 44.8 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 1.73 0.92 HCG241_SK453_B267_BPL114_Moraux2003_4_DH475 GCS9 Cl* Melotte 22 HCG 241 +03 46 54.39 +22 45 11.9 0 18.716 16.652 16.025 15.444 0.043 17.39 2.54 -35.13 2.54 10.55 0.43 int-pl-IZ-48;IPLJ0346543+224512_Y GCS9 Cl* Melotte 22 IPL 48 +03 46 55.32 +23 22 49.3 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 6.26 0.89 HHJ95_DH479_L07_163 GCS9 Cl* Melotte 22 HHJ 95 +03 46 55.32 +24 11 16.6 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 3.10 0.93 BPL116_DH478_L07_103 GCS9 Cl* Melotte 22 BPL 116 +03 46 55.36 +25 51 29.4 0 15.808 15.294 14.643 14.056 13.716 13.713 9.44 2.24 -34.52 2.24 8.63 0.01 HHJ31_Moraux2003_97_L07_28 GCS9 Cl* Melotte 22 HHJ 31 +03 46 55.49 +23 11 16.0 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 3.33 0.33 L07_A1_56 GCS9 +03 46 55.78 +23 56 24.1 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 2.24 0.91 HHJ257_DH480 GCS9 Cl* Melotte 22 HHJ 257 +03 46 57.10 +23 15 02.2 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 2.12 0.83 HCG247_SK452_HHJ415_DH481 GCS9 Cl* Melotte 22 HCG 247 +03 46 57.83 +23 12 46.2 0 14.826 14.503 14.022 13.469 13.198 13.219 28.31 2.24 -40.75 2.24 3.54 0.15 L07_175 GCS9 +03 46 58.17 +23 33 38.8 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.71 0.94 HHJ174_DH482_L07_136 GCS9 Cl* Melotte 22 HHJ 174 +03 46 58.26 +24 01 41.3 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 4.06 0.79 L07_121 GCS9 +03 46 59.32 +24 01 42.8 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 2.81 0.85 HHJ299_DH484 GCS9 Cl* Melotte 22 HHJ 299 +03 47 01.85 +24 13 28.1 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 3.78 0.52 BPL122_L07_A1_57 GCS9 Cl* Melotte 22 BPL 122 +03 47 02.35 +23 32 36.0 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 10.63 0.89 DH487_L07_135 GCS9 Cl* Melotte 22 DH 487 +03 47 03.78 +23 36 58.6 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 1.39 0.80 HII1286_HCG251_SK444_DH489 GCS9 HII1286 +03 47 04.41 +24 47 27.3 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2.70 0.49 L07_A1_58 GCS9 +03 47 04.75 +25 22 50.0 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 2.99 0.87 HCG248_SK443_B180_HHJ390_DH491 GCS9 Cl* Melotte 22 HCG 248 +03 47 05.71 +24 40 03.6 0 16.900 16.127 15.416 14.847 14.428 14.447 10.79 2.25 -41.09 2.25 2.09 0.26 BPL124_L07_A1_59 GCS9 Cl* Melotte 22 BPL 124 +03 47 05.79 +23 45 34.7 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 18.37 0.33 L07_A1_60_L07_231 GCS9 +03 47 07.88 +24 23 37.8 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 35.17 0.80 BPL125_DH494_Festin98_003 GCS9 Cl* Melotte 22 BPL 125 +03 47 08.15 +24 18 24.5 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 1.55 0.90 HCG253_SK440_BPL126_DH495 GCS9 Cl* Melotte 22 HCG 253 +03 47 09.42 +24 15 34.7 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 3.08 0.68 HHJ37_BPL128_DH496_L07_105 GCS9 Cl* Melotte 22 HHJ 37 +03 47 09.52 +23 25 56.3 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.90 0.93 DH498 GCS9 Cl* Melotte 22 DH 498 +03 47 10.21 +24 43 35.3 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 12.34 0.90 L07_65 GCS9 +03 47 10.65 +23 58 16.4 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 2.39 0.72 L07_A1_61_L07_228 GCS9 +03 47 11.03 +24 13 51.5 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 1.30 0.90 HCG254_BPL129_L07_104 GCS9 Cl* Melotte 22 HCG 254 +03 47 11.79 +24 13 31.3 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.62 0.73 BPL130_L07_A1_62 GCS9 Cl* Melotte 22 BPL 130 +03 47 11.86 +24 13 53.8 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 1.06 0.80 HHJ92_BPL131_DH499 GCS9 Cl* Melotte 22 HHJ 92 +03 47 13.67 +23 49 53.2 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 5.11 0.89 HCG258_SK437_B363_DH500 GCS9 Cl* Melotte 22 HCG 258 +03 47 15.20 +25 24 19.0 0 17.217 16.524 15.790 15.226 14.834 14.844 17.61 2.31 -19.56 2.31 3.87 0.00 IPMBD26 GCS9 Cl* Melotte 22 IPMBD 26 +03 47 15.29 +25 06 55.3 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 2.31 0.89 SK432_HHJ376_BPL133_DH502_Moraux2003_9 GCS9 Cl* Melotte 22 SK 432 +03 47 15.38 +23 26 05.8 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 2.38 0.92 HHJ203_DH503_L07_161 GCS9 Cl* Melotte 22 HHJ 203 +03 47 15.46 +24 23 31.0 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 85.06 0.58 BPL134_Festin98_002 GCS9 Cl* Melotte 22 BPL 134 +03 47 15.75 +22 21 16.8 0 14.199 13.704 13.140 12.641 12.297 12.323 20.22 2.26 -54.43 2.26 2.70 0.03 HCG267_BPL135_HHJ211_L07_205 GCS9 Cl* Melotte 22 HCG 267 +03 47 16.45 +24 44 50.1 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 12.55 0.93 BPL136_L07_66 GCS9 Cl* Melotte 22 BPL 136 +03 47 17.92 +24 22 31.6 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 6.15 0.68 BPL137_Teide1_M8_L07_A1_63 GCS9 Cl* Melotte 22 BPL 137 +03 47 18.10 +24 45 14.6 0 19.068 18.067 17.095 16.314 15.678 15.676 8.16 2.57 -37.97 2.57 4.23 0.15 L07_A1_64 GCS9 +03 47 19.34 +24 08 20.7 0 13.736 13.765 11.395 12.325 9.950 10.831 21.11 2.22 -61.40 2.22 7.54 0.00 HII1362_HD23607_Tr390 GCS9 HII1362 +03 47 20.43 +22 21 56.3 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 3.34 0.80 L07_207 GCS9 +03 47 20.84 +25 05 12.1 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 2.92 0.77 SK428_HHJ417_DH506 GCS9 Cl* Melotte 22 SK 428 +03 47 20.97 +23 48 11.9 0 13.896 13.945 11.841 12.647 10.307 11.499 2.96 2.22 -46.62 2.22 25.98 0.00 HII1380_HD23632_Tr397 GCS9 HII1380 +03 47 21.94 +26 22 47.2 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 1.49 0.71 HHJ219_DH508_Moraux2003_44 GCS9 Cl* Melotte 22 HHJ 219 +03 47 22.03 +23 21 36.3 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 3.13 0.93 L07_159 GCS9 +03 47 22.38 +24 14 18.8 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 3.72 0.81 BPL139 GCS9 Cl* Melotte 22 BPL 139 +03 47 22.46 +22 31 10.8 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 3.97 0.83 BPL140_L07_201 GCS9 Cl* Melotte 22 BPL 140 +03 47 22.68 +23 44 06.7 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 30.63 0.92 HCG266_DH509 GCS9 Cl* Melotte 22 HCG 266 +03 47 22.76 +23 01 58.0 0 15.929 15.403 14.815 14.282 13.961 13.934 36.81 2.25 -28.10 2.25 3.24 0.00 HHJ23 GCS9 Cl* Melotte 22 HHJ 23 +03 47 22.91 +22 55 19.4 0 12.476 12.365 11.050 11.776 9.922 10.773 30.63 2.23 -35.96 2.23 35.83 0.01 HII1407_HD23610_TrS108 GCS9 HII1407 +03 47 25.11 +24 15 17.2 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 3.29 0.93 HHJ198_BPL143 GCS9 Cl* Melotte 22 HHJ 198 +03 47 25.35 +24 02 56.8 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 3.92 0.88 HHJ427_DH512 GCS9 Cl* Melotte 22 HHJ 427 +03 47 25.80 +25 08 32.8 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 4.52 0.93 HCG263_SK423_T6B_HHJ361_BPL144_DH513_Moraux2003_10 GCS9 Cl* Melotte 22 HCG 263 +03 47 25.90 +25 26 26.4 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 3.55 0.91 Moraux2003_49_L07_38 GCS9 Cl* Melotte 22 MBSC 49 +03 47 26.77 +23 38 02.4 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 4.38 0.93 HCG269_HHJ263_DH514 GCS9 Cl* Melotte 22 HCG 269 +03 47 26.84 +23 40 41.8 0 13.124 13.039 11.380 12.020 10.138 13.738 12.70 2.22 -30.72 2.22 12.76 0.00 HII1425_HD23643_Tr410 GCS9 HII1425 +03 47 27.28 +24 49 16.3 0 14.616 14.190 13.656 12.958 12.729 12.717 31.79 2.21 -32.38 2.21 2.21 0.00 15.07 GCS9 Cl* Melotte 22 MHO 13 +03 47 27.72 +22 09 38.5 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 6.30 0.72 L07_A1_65 GCS9 +03 47 28.12 +23 26 53.4 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 3.65 0.92 SK417_HHJ315_DH515 GCS9 Cl* Melotte 22 SK 417 +03 47 28.41 +26 32 05.5 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.39 0.93 DH516_Moraux2003_33 GCS9 Cl* Melotte 22 DH 516 +03 47 28.43 +24 40 33.0 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 4.76 0.87 BPL145_DH517_L07_69 GCS9 Cl* Melotte 22 BPL 145 +03 47 29.59 +23 52 49.3 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 1.74 0.74 L07_A1_66 GCS9 +03 47 29.93 +23 33 15.0 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 2.95 0.74 HHJ16 GCS9 Cl* Melotte 22 HHJ 16 +03 47 30.59 +26 16 44.5 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.21 0.93 HCG260_HHJ310_DH518_Moraux2003_26 GCS9 Cl* Melotte 22 HCG 260 +03 47 30.60 +24 22 13.8 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 2.33 0.94 HCG273_HHJ408 GCS9 Cl* Melotte 22 HCG 273 +03 47 31.13 +21 10 51.1 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 0.64 0.31 DH519 GCS9 Cl* Melotte 22 DH 519 +03 47 31.37 +25 25 11.0 0 15.908 15.366 14.702 14.048 13.679 13.650 15.53 2.24 -17.06 2.24 1.02 0.00 Moraux2003_104 GCS9 Cl* Melotte 22 MBSC 104 +03 47 31.65 +23 52 19.1 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 2.44 0.82 L07_124 GCS9 +03 47 32.00 +24 10 24.7 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 1.69 0.94 BPL147 GCS9 Cl* Melotte 22 BPL 147 +03 47 33.06 +25 38 18.4 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 6.41 0.90 L07_29 GCS9 +03 47 33.46 +23 41 32.8 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.71 0.88 HCG277_SK413_T105_HHJ424_DH520 GCS9 Cl* Melotte 22 HCG 277 +03 47 34.17 +25 43 05.9 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 17.36 0.86 HHJ238_DH522_L07_30 GCS9 Cl* Melotte 22 HHJ 238 +03 47 34.52 +24 02 23.0 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 2.40 0.72 DH523 GCS9 Cl* Melotte 22 DH 523 +03 47 35.86 +24 52 26.7 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 10.09 0.91 HCG279_HHJ157_BPL149 GCS9 Cl* Melotte 22 HCG 279 +03 47 36.04 +23 28 26.7 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 3.74 0.83 HHJ73_DH526_L07_162 GCS9 Cl* Melotte 22 HHJ 73 +03 47 37.35 +25 20 02.3 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 1.96 0.93 Moraux2003_43 GCS9 Cl* Melotte 22 MBSC 43 +03 47 37.66 +24 24 23.1 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 2.31 0.84 BPL150_L07_76 GCS9 Cl* Melotte 22 BPL 150 +03 47 38.05 +24 49 10.8 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 7.15 0.91 SK409_HHJ360_BPL151_DH528 GCS9 Cl* Melotte 22 SK 409 +03 47 38.37 +24 35 59.7 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 8.01 0.85 L07_80 GCS9 +03 47 39.02 +24 36 22.2 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 12.06 0.59 BPL152_Roque16_CFHT-Pl-11_M6.0_BRB12_CFHT-Pl-11_L07_A1_67_L07_219 GCS9 Cl* Melotte 22 BPL 152 +03 47 39.36 +24 27 31.9 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 4.21 0.94 HCG282_HHJ272_BPL153_DH530 GCS9 Cl* Melotte 22 HCG 282 +03 47 39.80 +23 00 04.4 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 13.32 0.88 HHJ94 GCS9 Cl* Melotte 22 HHJ 94 +03 47 40.96 +21 49 05.0 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 5.36 0.80 L07_213 GCS9 +03 47 42.86 +28 18 59.1 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 0.05 0.45 DH533 GCS9 Cl* Melotte 22 DH 533 +03 47 43.88 +26 13 26.8 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.35 0.86 DH534_Moraux2003_66 GCS9 Cl* Melotte 22 DH 534 +03 47 44.05 +24 03 56.3 0 16.061 15.644 15.077 14.512 14.197 14.197 23.29 2.23 -45.96 2.23 10.22 0.35 HHJ26_DH535 GCS9 Cl* Melotte 22 HHJ 26 +03 47 44.66 +23 42 03.1 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 1.38 0.91 HHJ152_DH536_L07_139 GCS9 Cl* Melotte 22 HHJ 152 +03 47 44.67 +22 12 43.9 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 5.26 0.74 HHJ15_BPL154 GCS9 Cl* Melotte 22 HHJ 15 +03 47 44.68 +22 23 53.0 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.33 0.90 HCG299_HHJ163_BPL155_L07_208 GCS9 Cl* Melotte 22 HCG 299 +03 47 45.92 +24 38 01.3 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 0.62 HCG287_HHJ168_BPL156_L07_68 GCS9 Cl* Melotte 22 HCG 287 +03 47 46.40 +24 03 02.3 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 7.93 0.84 HHJ438 GCS9 Cl* Melotte 22 HHJ 438 +03 47 46.78 +25 35 16.6 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 1.25 0.44 L07_A1_68 GCS9 +03 47 47.87 +25 13 34.3 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 2.41 0.94 SK398_HHJ266_BPL157_DH537_Moraux2003_34 GCS9 Cl* Melotte 22 SK 398 +03 47 48.91 +24 17 06.5 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 3.93 0.43 L07_A1_69 GCS9 +03 47 49.45 +23 31 52.8 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 7.37 0.65 L07_A1_70 GCS9 +03 47 49.79 +24 25 43.1 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 2.91 0.87 HCG292_HHJ202_BPL158_DH538_L07_77 GCS9 Cl* Melotte 22 HCG 292 +03 47 50.41 +23 54 47.8 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 5.41 0.63 Festin98_007_L07_A1_71 GCS9 Cl* Melotte 22 NPL 007 +03 47 50.95 +24 30 18.6 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 7.73 0.92 HCG295_HHJ389_BPL159_DH539 GCS9 Cl* Melotte 22 HCG 295 +03 47 51.97 +23 39 48.0 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 6.79 0.94 HHJ122_DH540 GCS9 Cl* Melotte 22 HHJ 122 +03 47 52.88 +22 59 33.8 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 11.92 0.89 SK394_HHJ258_BPL160_DH541 GCS9 Cl* Melotte 22 SK 394 +03 47 55.28 +23 19 05.8 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2.43 0.30 HCG302_SK392_HHJ289_DH542 GCS9 Cl* Melotte 22 HCG 302 +03 47 56.64 +24 15 31.7 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 3.78 0.47 BPL162_L07_100 GCS9 Cl* Melotte 22 BPL 162 +03 47 56.66 +26 31 50.9 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 2.10 0.76 HHJ423_DH543 GCS9 Cl* Melotte 22 HHJ 423 +03 47 58.04 +22 06 50.8 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 4.22 0.74 BPL163_L07_A1_72 GCS9 Cl* Melotte 22 BPL 163 +03 47 59.38 +24 35 37.0 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 3.92 0.87 BPL164_DH544_L07_79 GCS9 Cl* Melotte 22 BPL 164 +03 47 59.74 +22 36 01.8 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 6.65 0.69 int-pl-IZ-33_2MASSJ0347597+223601_Y_L07_A1_73 GCS9 Cl* Melotte 22 IPL 33 +03 47 59.77 +22 38 30.1 0 12.945 12.429 11.820 11.418 11.025 11.072 30.05 2.26 -48.45 2.26 26.02 0.00 HHJ365_BPL165 GCS9 Cl* Melotte 22 HHJ 365 +03 48 04.67 +23 39 30.1 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 5.65 0.66 PPl15_IPMBD23_L07_A1_74 GCS9 PPl15 +03 48 04.98 +23 24 13.4 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 1.67 0.89 HHJ21_DH546_L07_160 GCS9 Cl* Melotte 22 HHJ 21 +03 48 05.72 +22 38 09.3 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 31.26 0.82 HCG314_HHJ210_DH547_L07_202 GCS9 Cl* Melotte 22 HCG 314 +03 48 05.83 +23 02 02.7 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 2.60 0.93 HCG309_SK385_T154_HHJ375_DH548_BPL166 GCS9 Cl* Melotte 22 HCG 309 +03 48 06.41 +24 06 51.7 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.58 0.94 L07_122 GCS9 +03 48 06.64 +24 00 06.7 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 1.28 0.90 HHJ240_DH549 GCS9 Cl* Melotte 22 HHJ 240 +03 48 07.96 +23 44 23.5 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.64 0.92 HCG307_HHJ288_DH551 GCS9 Cl* Melotte 22 HCG 307 +03 48 08.96 +23 42 23.3 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 1.13 0.90 HHJ156_DH552 GCS9 Cl* Melotte 22 HHJ 156 +03 48 09.22 +23 58 40.5 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.91 0.90 HHJ225_DH553_L07_107 GCS9 Cl* Melotte 22 HHJ 225 +03 48 10.17 +23 00 03.9 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 5.94 0.66 DH554 GCS9 Cl* Melotte 22 DH 554 +03 48 10.18 +23 59 20.1 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.99 0.88 DH555_PPL12_L07_109 GCS9 Cl* Melotte 22 DH 555 +03 48 13.31 +23 58 46.8 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 4.05 0.93 HCG311_T155_DH560_Festin98_001_L07_108 GCS9 Cl* Melotte 22 HCG 311 +03 48 13.78 +23 37 59.3 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 2.65 0.94 HCG315_SK374_HHJ281_DH561_L07_10 GCS9 Cl* Melotte 22 HCG 315 +03 48 14.30 +24 15 50.5 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 4.06 0.69 BPL169_L07_A1_75 GCS9 Cl* Melotte 22 BPL 169 +03 48 15.25 +23 26 05.4 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 1.54 0.89 HHJ232 GCS9 Cl* Melotte 22 HHJ 232 +03 48 15.27 +23 42 03.4 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 1.36 0.92 HHJ336_DH562 GCS9 Cl* Melotte 22 HHJ 336 +03 48 15.49 +25 14 36.4 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 3.89 0.74 BPL170_DH563_Moraux2003_78_L07_58 GCS9 Cl* Melotte 22 BPL 170 +03 48 16.09 +23 35 15.2 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.55 0.94 HHJ151_DH564_L07_144 GCS9 Cl* Melotte 22 HHJ 151 +03 48 16.38 +26 20 05.8 0 14.225 13.741 13.161 12.587 12.309 12.297 17.16 2.93 -24.32 2.93 0.43 0.00 HCG305 GCS9 Cl* Melotte 22 HCG 305 +03 48 16.58 +21 57 44.4 0 15.162 14.808 14.229 13.602 13.361 13.347 19.66 2.27 -27.09 2.27 22.21 0.00 L07_212 GCS9 +03 48 16.64 +26 30 11.6 0 14.415 13.953 13.400 12.855 12.541 12.576 17.23 2.93 -51.67 2.93 0.24 0.29 HHJ215_DH565 GCS9 Cl* Melotte 22 HHJ 215 +03 48 17.30 +24 30 15.7 0 12.184 11.939 11.515 11.450 10.723 10.898 26.02 2.21 -46.97 2.21 2.32 0.15 HII1785_HCG312_DH566 GCS9 HII1785 +03 48 17.37 +23 48 23.5 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.63 0.94 HHJ188_L07_132 GCS9 Cl* Melotte 22 HHJ 188 +03 48 17.61 +22 04 00.9 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 2.96 0.94 HHJ187_BPL171_L07_209 GCS9 Cl* Melotte 22 HHJ 187 +03 48 19.02 +24 25 12.7 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 13.03 0.49 BPL172_Festin98_005_Roque12_L07_photNM_22 GCS9 Cl* Melotte 22 BPL 172 +03 48 19.84 +23 36 11.8 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 1.90 0.91 DH568 GCS9 Cl* Melotte 22 DH 568 +03 48 20.29 +24 54 54.9 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 2.97 0.94 HCG313_SK371_T23_BPL173 GCS9 Cl* Melotte 22 HCG 313 +03 48 20.58 +23 31 01.3 0 15.523 14.983 14.358 13.817 13.510 13.517 24.03 2.14 -49.73 2.14 6.53 0.15 HCG321_DH570 GCS9 Cl* Melotte 22 HCG 321 +03 48 21.54 +24 34 43.4 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 3.06 0.73 BPL174_DH571_L07_78 GCS9 Cl* Melotte 22 BPL 174 +03 48 22.66 +22 52 21.3 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 4.73 0.91 HCG323_SK370_A91_BPL175_DH572 GCS9 Cl* Melotte 22 HCG 323 +03 48 22.71 +23 27 42.8 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.89 0.94 HHJ171_DH573 GCS9 Cl* Melotte 22 HHJ 171 +03 48 22.81 +24 48 53.4 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 2.53 0.78 SK368_HHJ309_BPL176_DH574 GCS9 Cl* Melotte 22 SK 368 +03 48 23.92 +23 08 08.1 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 1.68 0.83 DH576 GCS9 Cl* Melotte 22 DH 576 +03 48 25.24 +24 14 25.8 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 5.84 0.94 HCG322_SK363_HHJ314_BPL178 GCS9 Cl* Melotte 22 HCG 322 +03 48 26.05 +25 14 40.8 0 12.786 12.471 11.969 11.411 11.122 11.211 18.96 2.21 -54.45 2.21 33.24 0.00 HCG317_DH578 GCS9 Cl* Melotte 22 HCG 317 +03 48 26.60 +23 11 29.5 0 13.307 12.741 12.122 11.691 11.279 11.326 25.82 2.13 -37.29 2.13 0.76 0.17 HCG332_SK362_HHJ339 GCS9 Cl* Melotte 22 HCG 332 +03 48 27.36 +23 46 16.2 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 1.05 0.57 L07_A1_76 GCS9 +03 48 29.76 +23 58 05.8 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 3.01 0.92 HCG328_WILL3_HHJ132 GCS9 Cl* Melotte 22 HCG 328 +03 48 30.29 +24 18 00.2 0 20.225 19.201 17.793 16.971 16.382 16.374 21.38 2.76 -51.81 2.76 2.78 0.54 L07_A1_77 GCS9 +03 48 30.74 +22 44 50.2 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 1.90 0.47 Roque25 GCS9 Cl* Melotte 22 Roque 25 +03 48 31.06 +24 16 53.1 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 10.39 0.79 HCG324_VM46_HHJ404_BPL180_DH580 GCS9 Cl* Melotte 22 HCG 324 +03 48 31.53 +24 34 37.2 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 3.41 0.49 BRB16_PIZ1_L07_A1_78_L07_258 GCS9 Cl* Melotte 22 BRB 16 +03 48 31.84 +24 01 58.6 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 6.09 0.93 HCG327_HHJ197_DH581_L07_110 GCS9 Cl* Melotte 22 HCG 327 +03 48 33.78 +24 01 58.8 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 4.68 0.87 HHJ184_DH582_L07_111 GCS9 Cl* Melotte 22 HHJ 184 +03 48 35.20 +22 53 42.1 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 1.57 0.62 HHJ10_BPL181_PPL10 GCS9 Cl* Melotte 22 HHJ 10 +03 48 35.49 +24 12 03.0 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 2.04 0.85 HCG335_HHJ96_BPL182_DH583_L07_95 GCS9 Cl* Melotte 22 HCG 335 +03 48 36.34 +25 15 41.2 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 13.10 0.54 BPL183_Moraux2003_105_L07_56 GCS9 Cl* Melotte 22 BPL 183 +03 48 38.37 +22 33 51.8 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 7.70 0.60 L07_A1_79 GCS9 +03 48 39.31 +24 50 19.9 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 1.67 0.84 DH585_L07_75 GCS9 Cl* Melotte 22 DH 585 +03 48 39.91 +24 12 42.7 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 1.22 0.87 HCG337_SK353_HHJ347_BPL184 GCS9 Cl* Melotte 22 HCG 337 +03 48 40.43 +24 36 34.0 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 2.52 0.89 HCG333_SK351_BPL185_DH586 GCS9 Cl* Melotte 22 HCG 333 +03 48 40.60 +25 01 19.8 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 1.98 0.90 BPL186_L07_63 GCS9 Cl* Melotte 22 BPL 186 +03 48 40.99 +23 14 17.2 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 4.91 0.92 HCG343_SK352 GCS9 Cl* Melotte 22 HCG 343 +03 48 42.15 +25 00 28.2 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.66 0.92 HCG339_SK349_B207_BPL187 GCS9 Cl* Melotte 22 HCG 339 +03 48 42.69 +24 27 19.4 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.44 0.85 HHJ44_WILL6_BPL188_DH587_L07_89 GCS9 Cl* Melotte 22 HHJ 44 +03 48 43.14 +26 32 20.9 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 0.52 0.39 HHJ246_DH588 GCS9 Cl* Melotte 22 HHJ 246 +03 48 44.05 +25 06 22.4 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 3.48 0.92 BPL189_DH589_Moraux2003_48_L07_55 GCS9 Cl* Melotte 22 BPL 189 +03 48 44.69 +24 37 23.5 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 1.14 0.75 DH590_CFHT-Pl-5_M5.5_BRB7_CFHT-Pl-5_L07_A1_80_L07_223 GCS9 Cl* Melotte 22 DH 590 +03 48 45.35 +24 37 26.3 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 2.78 0.83 HCG346_HHJ111_BPL190 GCS9 Cl* Melotte 22 HCG 346 +03 48 46.02 +24 10 12.5 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 1.14 0.89 HHJ207_DH59_L07_112 GCS9 Cl* Melotte 22 HHJ 207 +03 48 48.62 +24 30 15.4 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 6.82 0.70 L07_90 GCS9 +03 48 48.79 +23 24 48.0 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 1.51 0.64 HHJ86 GCS9 Cl* Melotte 22 HHJ 86 +03 48 50.45 +22 44 29.8 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 2.49 0.71 HHJ3_BPL191 GCS9 Cl* Melotte 22 HHJ 3 +03 48 50.45 +25 17 54.7 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 2.79 0.67 BPL192_Moraux2003_109 GCS9 Cl* Melotte 22 BPL 192 +03 48 50.50 +24 08 27.2 0 16.669 16.113 15.512 14.949 14.599 14.574 7.25 2.25 -27.47 2.25 3.93 0.00 DH593 GCS9 Cl* Melotte 22 DH 593 +03 48 50.67 +23 04 30.0 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 1.74 0.90 HHJ116 GCS9 Cl* Melotte 22 HHJ 116 +03 48 51.56 +24 12 17.3 0 12.963 12.601 12.088 11.632 11.240 11.331 19.13 2.22 -24.55 2.22 13.96 0.00 BPL193_ALR728 GCS9 Cl* Melotte 22 BPL 193 +03 48 52.29 +25 22 32.3 0 14.846 14.364 13.770 13.224 12.890 12.904 16.02 2.23 -33.87 2.23 4.39 0.17 L07_43 GCS9 +03 48 55.65 +24 21 40.1 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 2.50 0.70 HHJ8_L07_A1_81 GCS9 Cl* Melotte 22 HHJ 8 +03 48 57.36 +24 19 43.6 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 3.88 0.89 HCG349_SK340 GCS9 Cl* Melotte 22 HCG 349 +03 48 58.29 +23 05 39.0 0 13.329 12.999 12.460 11.907 11.630 11.649 20.85 2.13 -49.85 2.13 3.36 0.54 HHJ353_DH595 GCS9 Cl* Melotte 22 HHJ 353 +03 49 00.99 +24 54 10.0 0 13.608 13.216 12.673 12.188 11.812 11.833 24.02 2.20 -54.18 2.20 4.15 0.01 HCG351_SK335_HHJ332_BPL195 GCS9 Cl* Melotte 22 HCG 351 +03 49 01.02 +22 58 49.2 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 2.32 0.84 HCG353_SK336_A55_HHJ414_DH597 GCS9 Cl* Melotte 22 HCG 353 +03 49 01.16 +23 38 15.5 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 6.87 0.86 DH598_L07_145 GCS9 Cl* Melotte 22 DH 598 +03 49 01.50 +24 11 38.3 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 49.18 0.81 HHJ231_BPL196_DH599 GCS9 Cl* Melotte 22 HHJ 231 +03 49 02.35 +25 43 24.1 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 7.61 0.66 DH2004_600 GCS9 Cl* Melotte 22 DH 600 +03 49 02.97 +21 54 46.9 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 3.03 0.75 BPL197_L07_211 GCS9 Cl* Melotte 22 BPL 197 +03 49 04.86 +23 33 39.3 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 23.53 0.70 Roque47_IPMBD20_L07_A1_82 GCS9 Cl* Melotte 22 Roque 47 +03 49 05.18 +22 04 52.7 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 5.97 0.59 L07_A1_83 GCS9 +03 49 05.93 +23 06 21.9 0 13.996 13.617 13.008 12.510 12.202 12.212 21.86 2.13 -55.37 2.13 8.61 0.00 HCG357_SK332_DH602 GCS9 Cl* Melotte 22 HCG 357 +03 49 06.76 +25 24 21.5 0 15.881 15.480 14.942 14.330 14.052 14.083 10.08 2.25 -28.77 2.25 1.80 0.00 L07_44 GCS9 +03 49 08.84 +25 53 48.6 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 2.17 0.89 SK327_HHJ387_DH603_Moraux2003_8 GCS9 Cl* Melotte 22 SK 327 +03 49 11.95 +24 39 26.5 0 19.975 18.858 18.018 17.618 17.140 17.153 33.87 3.66 -38.06 3.66 1.67 0.11 L07_256 GCS9 +03 49 12.18 +25 20 31.7 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 1.32 0.89 L07_42 GCS9 +03 49 15.12 +24 36 22.5 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 9.93 0.64 BPL202_CFHT-Pl-9_M6.5_BRB10_CFHT-Pl-9_L07_A1_85_L07_222 GCS9 Cl* Melotte 22 BPL 202 +03 49 15.63 +23 22 49.1 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 3.40 0.88 HHJ36_L07_152_DH610 GCS9 Cl* Melotte 22 HHJ 36 +03 49 16.18 +26 49 02.9 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 1.06 0.37 IPMBD21 GCS9 Cl* Melotte 22 IPMBD 21 +03 49 16.42 +24 03 49.0 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 7.58 0.71 HCG362 GCS9 Cl* Melotte 22 HCG 362 +03 49 20.31 +25 25 42.4 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 5.46 0.66 HCG366_HHJ251_DH611_Moraux2003_31 GCS9 Cl* Melotte 22 HCG 366 +03 49 20.70 +24 52 35.2 0 15.612 15.094 14.529 14.021 13.731 13.738 44.56 2.21 -41.45 2.21 12.56 0.00 BPL203 GCS9 Cl* Melotte 22 BPL 203 +03 49 20.96 +26 11 33.5 0 16.986 16.399 15.818 15.206 14.866 14.910 12.65 3.05 -35.63 3.05 1.07 0.10 DH614 GCS9 Cl* Melotte 22 DH 614 +03 49 21.17 +23 34 02.0 0 18.421 17.444 16.621 16.028 15.535 15.548 15.81 2.36 -29.43 2.36 3.20 0.03 Roque8_L07_photNM_11 GCS9 Cl* Melotte 22 Roque 8 +03 49 21.25 +24 41 40.8 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 4.32 0.84 HHJ51_BPL204_DH615 GCS9 Cl* Melotte 22 HHJ 51 +03 49 21.50 +23 39 06.3 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 5.34 0.82 HCG363_SK319_HHJ306_DH616 GCS9 Cl* Melotte 22 HCG 363 +03 49 21.93 +24 54 43.2 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 4.87 0.94 SK316_BPL205_DH617_L07_62 GCS9 Cl* Melotte 22 SK 316 +03 49 22.15 +25 47 37.7 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 2.58 0.80 HHJ196_DH618_Moraux2003_52 GCS9 Cl* Melotte 22 HHJ 196 +03 49 22.52 +21 37 56.0 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 1.35 0.15 DH2004_619 GCS9 Cl* Melotte 22 DH 619 +03 49 25.55 +22 18 44.4 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 2.43 0.79 BPL206 GCS9 Cl* Melotte 22 BPL 206 +03 49 25.61 +23 02 49.9 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 3.06 0.87 HHJ79_BPL207_DH620_L07_187 GCS9 Cl* Melotte 22 HHJ 79 +03 49 26.64 +22 50 54.5 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 1.72 0.91 SK315_BPL208_L07_198 GCS9 Cl* Melotte 22 SK 315 +03 49 26.65 +22 52 20.5 0 14.721 14.312 13.776 13.161 12.926 12.924 34.03 2.25 -25.62 2.25 11.59 0.00 L07_185 GCS9 +03 49 27.58 +24 24 13.7 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 90.55 0.54 HHJ192_DH621 GCS9 Cl* Melotte 22 HHJ 192 +03 49 27.65 +24 31 54.1 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 4.32 0.40 HCG370_DH622 GCS9 Cl* Melotte 22 HCG 370 +03 49 28.80 +26 50 51.4 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 0.50 0.37 Moraux2003_95 GCS9 Cl* Melotte 22 MBSC 95 +03 49 29.81 +23 38 55.2 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 7.60 0.86 HHJ158_DH624_L07_149 GCS9 Cl* Melotte 22 HHJ 158 +03 49 31.22 +23 41 19.6 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 5.61 0.93 HHJ142_DH625 GCS9 Cl* Melotte 22 HHJ 142 +03 49 32.16 +23 16 17.9 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 1.24 0.86 DH626 GCS9 Cl* Melotte 22 DH 626 +03 49 32.57 +23 24 41.0 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 6.19 0.91 HHJ245_L07_153 GCS9 Cl* Melotte 22 HHJ 245 +03 49 32.81 +25 47 46.8 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 4.11 0.93 HHJ209_DH628_Moraux2003_60_L07_23 GCS9 Cl* Melotte 22 HHJ 209 +03 49 33.04 +24 32 02.4 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 3.25 0.79 HCG372_SK309_T90_HHJ346_BPL209 GCS9 Cl* Melotte 22 HCG 372 +03 49 35.29 +25 59 34.6 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 3.27 0.86 HHJ155_DH630 GCS9 Cl* Melotte 22 HHJ 155 +03 49 35.46 +22 23 26.1 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.89 0.93 HHJ112_BPL211 GCS9 Cl* Melotte 22 HHJ 112 +03 49 36.32 +26 02 16.3 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 4.90 0.53 HHJ42_DH633 GCS9 Cl* Melotte 22 HHJ 42 +03 49 38.24 +26 51 05.6 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.33 0.94 DH635_Moraux2003_29 GCS9 Cl* Melotte 22 DH 635 +03 49 39.32 +23 34 54.9 0 17.647 17.116 16.453 15.939 15.609 15.592 -2.29 2.32 -44.61 2.32 7.80 0.00 Roque42 GCS9 Cl* Melotte 22 Roque 42 +03 49 41.14 +23 14 59.3 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 3.53 0.83 L07_171 GCS9 +03 49 41.21 +22 56 40.5 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 6.90 0.69 BPL213_L07_A1_86_L07_242 GCS9 Cl* Melotte 22 BPL 213 +03 49 41.34 +26 08 53.2 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 5.88 0.89 HHJ305_DH636 GCS9 Cl* Melotte 22 HHJ 305 +03 49 41.71 +21 56 19.2 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.65 0.72 BPL214 GCS9 Cl* Melotte 22 BPL 214 +03 49 43.17 +24 39 46.4 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 1.42 0.57 BPL215_L07_A1_87_L07_218 GCS9 Cl* Melotte 22 BPL 215 +03 49 47.04 +25 42 36.8 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 2.88 0.94 DH638_L07_5 GCS9 Cl* Melotte 22 DH 638 +03 49 48.16 +26 37 47.5 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.39 0.89 Moraux2003_98 GCS9 Cl* Melotte 22 MBSC 98 +03 49 48.44 +22 10 48.3 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 1.17 0.71 BPL216_DH639 GCS9 Cl* Melotte 22 BPL 216 +03 49 48.77 +23 42 59.3 0 19.186 18.198 17.242 16.647 16.119 16.132 -24.98 2.50 -59.62 2.50 3.68 0.00 L07_photNM_12 GCS9 +03 49 51.43 +25 15 30.4 0 20.207 19.539 18.563 17.881 17.415 17.463 21.41 4.65 -39.77 4.65 0.80 0.46 Roque2 GCS9 Cl* Melotte 22 Roque 2 +03 49 51.80 +21 18 26.1 0 12.546 12.207 11.705 11.428 10.854 11.164 28.74 3.05 -39.12 3.05 1.63 0.10 HCG378_DH640 GCS9 Cl* Melotte 22 HCG 378 +03 49 52.44 +24 03 43.0 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 3.73 0.70 L07_A1_88 GCS9 +03 49 55.49 +24 06 05.0 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 6.13 0.94 DH641 GCS9 Cl* Melotte 22 DH 641 +03 49 55.79 +24 44 31.3 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 7.36 0.92 HCG380_SK294_T92_BPL217_DH642 GCS9 Cl* Melotte 22 HCG 380 +03 49 56.77 +25 22 22.6 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 6.29 0.93 DH643 GCS9 Cl* Melotte 22 DH 643 +03 49 56.81 +24 59 07.1 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 7.78 0.61 BPL218_L07_A1_89_L07_217 GCS9 Cl* Melotte 22 BPL 218 +03 49 57.64 +23 43 28.2 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 32.32 0.87 DH644_L07_150 GCS9 Cl* Melotte 22 DH 644 +03 49 57.85 +23 41 49.6 0 18.821 17.908 17.116 16.554 16.119 16.122 12.13 2.44 -65.77 2.44 10.58 0.00 Roque6 GCS9 Cl* Melotte 22 Roque 6 +03 49 58.33 +25 06 20.9 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 2.86 0.88 BPL219_L07_52 GCS9 Cl* Melotte 22 BPL 219 +03 49 58.34 +23 42 33.7 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 24.46 0.93 SK293 GCS9 Cl* Melotte 22 SK 293 +03 49 58.61 +25 53 46.2 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 2.86 0.89 L07_24 GCS9 +03 49 59.54 +24 11 45.6 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 41.43 0.90 BPL220 GCS9 Cl* Melotte 22 BPL 220 +03 50 01.57 +25 24 01.5 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 3.60 0.83 SK291_DH646 GCS9 Cl* Melotte 22 SK 291 +03 50 01.87 +25 12 40.9 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 5.30 0.85 HCG385_SK289_BPL222_DH647 GCS9 Cl* Melotte 22 HCG 385 +03 50 02.18 +23 51 44.6 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 5.36 0.81 HCG382_DH648 GCS9 Cl* Melotte 22 HCG 382 +03 50 03.94 +24 56 02.8 0 16.234 15.728 15.114 14.515 14.147 14.145 1.53 2.22 -47.12 2.22 1.59 0.00 BPL223_L07_PM_NM_51 GCS9 Cl* Melotte 22 BPL 223 +03 50 04.25 +23 10 44.5 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 5.17 0.84 SK287_DH649 GCS9 Cl* Melotte 22 SK 287 +03 50 05.00 +23 18 17.2 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 48.45 0.77 DH650_L07_151 GCS9 Cl* Melotte 22 DH 650 +03 50 06.43 +26 58 18.7 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.15 0.92 DH652 GCS9 Cl* Melotte 22 DH 652 +03 50 06.56 +24 59 46.3 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 4.23 0.92 BPL224_DH653 GCS9 Cl* Melotte 22 BPL 224 +03 50 08.27 +25 30 51.7 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 0.96 0.58 L07_A1_90 GCS9 +03 50 08.42 +25 32 55.7 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 1.82 0.93 HCG386_DH654_L07_35 GCS9 Cl* Melotte 22 HCG 386 +03 50 08.62 +24 40 17.7 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 9.31 0.90 DH655 GCS9 Cl* Melotte 22 DH 655 +03 50 10.77 +24 28 41.4 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 8.77 0.90 BPL225_DH656_L07_84 GCS9 Cl* Melotte 22 BPL 225 +03 50 12.46 +23 55 35.9 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 2.36 0.92 SK279_DH659 GCS9 Cl* Melotte 22 SK 279 +03 50 12.60 +23 48 48.9 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 4.65 0.77 DH660 GCS9 Cl* Melotte 22 DH 660 +03 50 12.87 +24 21 06.2 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 8.13 0.75 HII2601_HCG390_SK278_DH661 GCS9 HII2601 +03 50 13.40 +23 59 29.8 0 20.475 19.183 17.880 16.935 16.219 16.193 42.39 2.66 -31.73 2.66 1.78 0.02 L07_A1_91 GCS9 +03 50 14.76 +25 25 29.8 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 4.67 0.91 DH662_L07_40 GCS9 Cl* Melotte 22 DH 662 +03 50 15.27 +24 13 36.0 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.48 0.94 HCG394_SK276_B214_BPL226_DH663 GCS9 Cl* Melotte 22 HCG 394 +03 50 15.33 +24 35 40.3 0 16.164 15.638 15.090 14.514 14.208 14.215 15.42 2.22 -15.83 2.22 5.62 0.00 L07_photNM_23 GCS9 +03 50 16.09 +24 08 34.7 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 2.35 0.67 Roque30_L07_A1_92_L07_259 GCS9 Cl* Melotte 22 Roque 30 +03 50 17.59 +22 55 58.8 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 2.13 0.89 DH664 GCS9 Cl* Melotte 22 DH 664 +03 50 18.22 +24 35 15.3 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 7.48 0.85 BPL227_DH665+L07_85 GCS9 Cl* Melotte 22 BPL 227 +03 50 19.15 +24 16 34.0 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 5.08 0.67 BPL228_L07_A1_93_L07_226 GCS9 Cl* Melotte 22 BPL 228 +03 50 22.01 +23 55 30.3 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 1.80 0.65 L07_A1_94 GCS9 +03 50 22.04 +22 37 32.3 0 14.088 13.717 13.128 12.578 12.281 12.280 13.50 2.51 -32.83 2.51 5.52 0.03 HCG399_DH666 GCS9 Cl* Melotte 22 HCG 399 +03 50 23.69 +24 25 43.6 0 17.469 16.705 16.019 15.399 15.045 14.969 9.85 2.30 -45.98 2.30 3.57 0.11 L07_221 GCS9 +03 50 24.96 +20 42 17.8 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 3.00 0.11 DH668 GCS9 Cl* Melotte 22 DH 668 +03 50 25.16 +23 55 41.8 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 1.44 0.90 HCG396_DH669 GCS9 Cl* Melotte 22 HCG 396 +03 50 27.35 +23 22 44.9 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 0.74 SK273_DH670 GCS9 Cl* Melotte 22 SK 273 +03 50 27.83 +23 03 54.9 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.78 0.94 DH671 GCS9 Cl* Melotte 22 DH 671 +03 50 29.30 +24 56 34.6 0 14.639 14.187 13.579 12.980 12.665 12.647 23.93 2.20 -30.32 2.20 2.43 0.00 BPL229_Moraux2003_58_L07_64 GCS9 Cl* Melotte 22 BPL 229 +03 50 29.96 +25 03 06.7 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.61 0.87 HCG403_SK272_B213_BPL230_DH672 GCS9 Cl* Melotte 22 HCG 403 +03 50 31.95 +22 08 47.5 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 1.62 0.75 HCG401_DH673 GCS9 Cl* Melotte 22 HCG 401 +03 50 32.58 +24 08 52.9 0 19.612 18.727 17.919 17.469 16.994 16.954 32.96 3.31 -13.95 3.31 1.26 0.00 Roque31 GCS9 Cl* Melotte 22 Roque 31 +03 50 33.08 +24 20 21.6 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 4.47 0.93 BPL231_DH674 GCS9 Cl* Melotte 22 BPL 231 +03 50 37.42 +22 28 08.0 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 1.02 0.87 HCG404_DH677 GCS9 Cl* Melotte 22 HCG 404 +03 50 38.92 +23 13 02.5 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 1.49 0.79 SK263_DH678 GCS9 Cl* Melotte 22 SK 263 +03 50 39.33 +26 16 03.7 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 0.34 0.09 DH680 GCS9 Cl* Melotte 22 DH 680 +03 50 40.83 +24 40 02.6 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 2.17 0.78 DH681_Moraux2003_83_L07_70 GCS9 Cl* Melotte 22 DH 681 +03 50 42.44 +24 12 55.4 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 2.68 0.85 DH683_Moraux2003_73_L07_101 GCS9 Cl* Melotte 22 DH 683 +03 50 44.35 +25 07 05.1 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 1.71 0.84 L07_60 GCS9 +03 50 45.38 +25 06 10.5 0 14.663 14.213 13.627 13.059 12.768 12.744 12.87 2.20 -29.87 2.20 1.06 0.00 L07_59 GCS9 +03 50 48.97 +22 40 11.8 0 13.084 12.693 12.183 11.610 11.323 11.328 23.79 2.13 -30.12 2.13 1.45 0.00 HCG409_DH684 GCS9 Cl* Melotte 22 HCG 409 +03 50 54.65 +24 21 55.7 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 26.89 0.87 DH687_Moraux2003_55_L07_102 GCS9 Cl* Melotte 22 DH 687 +03 50 54.99 +24 33 03.5 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 8.04 0.85 Moraux2003_69 GCS9 Cl* Melotte 22 MBSC 69 +03 50 57.41 +24 24 44.4 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 3.95 0.83 L07_81 GCS9 +03 50 57.42 +24 06 30.7 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 7.91 0.80 HCG415_SK251_DH689_Moraux2003_15 GCS9 Cl* Melotte 22 HCG 415 +03 51 03.62 +24 32 35.1 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 8.27 0.78 DH691 GCS9 Cl* Melotte 22 DH 691 +03 51 05.08 +26 36 51.2 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 0.51 0.41 DH692_Moraux2003_93 GCS9 Cl* Melotte 22 DH 692 +03 51 05.97 +24 36 16.9 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 5.62 0.34 L07_A1_95_L07_220 GCS9 +03 51 06.13 +22 38 00.6 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 2.59 0.82 DH694 GCS9 Cl* Melotte 22 DH 694 +03 51 07.11 +23 20 57.6 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 10.33 0.90 DH695 GCS9 Cl* Melotte 22 DH 695 +03 51 11.88 +23 44 43.3 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 19.86 0.89 BPL232_Moraux2003_90 GCS9 Cl* Melotte 22 BPL 232 +03 51 17.96 +26 01 22.1 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 7.12 0.91 DH701_L07_20 GCS9 Cl* Melotte 22 DH 701 +03 51 18.72 +26 03 15.4 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 3.57 0.90 L07_21 GCS9 +03 51 18.86 +26 03 08.7 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 6.61 0.92 SK237 GCS9 Cl* Melotte 22 SK 237 +03 51 19.07 +24 10 13.1 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 1.13 0.92 HCG424_SK235_BPL233_DH702_Moraux2003_3 GCS9 Cl* Melotte 22 HCG 424 +03 51 19.48 +23 09 49.4 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 1.60 0.90 DH703_L07_12 GCS9 Cl* Melotte 22 DH 703 +03 51 19.77 +23 04 04.1 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 7.69 0.88 DH704 GCS9 Cl* Melotte 22 DH 704 +03 51 20.68 +19 38 37.8 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.42 0.77 DH705 GCS9 Cl* Melotte 22 DH 705 +03 51 23.82 +22 50 29.1 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 3.94 0.88 DH706 GCS9 Cl* Melotte 22 DH 706 +03 51 23.89 +25 05 52.6 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 32.31 0.88 SK231_DH708 GCS9 Cl* Melotte 22 SK 231 +03 51 24.17 +26 03 11.3 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 2.49 0.93 HCG427_SK232_T29_DH709 GCS9 Cl* Melotte 22 HCG 427 +03 51 25.88 +24 47 38.7 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 11.49 0.46 HCG428_SK230_DH712 GCS9 Cl* Melotte 22 HCG 428 +03 51 30.60 +27 22 49.5 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.73 0.89 DH714 GCS9 Cl* Melotte 22 DH 714 +03 51 34.01 +24 34 08.9 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.67 0.90 DH715_Moraux2003_46 GCS9 Cl* Melotte 22 DH 715 +03 51 36.41 +25 13 40.6 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 2.11 0.91 DH717 GCS9 Cl* Melotte 22 DH 717 +03 51 38.96 +24 30 44.8 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 14.05 0.64 L07_A1_97_L07_253 GCS9 +03 51 39.08 +23 22 04.3 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 10.84 0.85 BPL237_L07_166 GCS9 Cl* Melotte 22 BPL 237 +03 51 42.34 +25 57 25.6 0 16.214 15.645 14.965 14.400 14.005 13.990 13.59 2.25 -34.32 2.25 4.49 0.06 L07_A1_98 GCS9 +03 51 44.95 +23 26 39.3 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 9.87 0.62 BPL240_L07_A1_99_L07_240 GCS9 Cl* Melotte 22 BPL 240 +03 51 46.56 +23 23 34.8 0 13.746 13.429 12.922 12.317 12.066 12.079 28.17 2.25 -4.26 2.25 10.21 0.00 BPL241 GCS9 Cl* Melotte 22 BPL 241 +03 51 47.66 +24 39 58.9 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 4.12 0.52 PLZJ21_PLIZ31 GCS9 Cl* Melotte 22 PlZJ 21 +03 51 50.60 +22 53 44.3 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 2.45 0.72 HCG437_SK212_DH724_L07_14 GCS9 Cl* Melotte 22 HCG 437 +03 51 51.15 +23 17 41.1 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 21.53 0.93 L07_13 GCS9 +03 51 51.55 +23 34 49.1 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 19.31 0.89 BPL242_CFHT-Pl-1_M4.9_Moraux2003_91_BRB1_CFHT-Pl-1_L07_140 GCS9 Cl* Melotte 22 BPL 242 +03 51 54.52 +23 33 31.3 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 31.11 0.70 HCG439_SK210_BPL244_DH727_Moraux2003_23 GCS9 Cl* Melotte 22 HCG 439 +03 51 55.05 +23 57 42.0 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 4.30 0.86 HCG440_BPL245_DH728_L07_119 GCS9 Cl* Melotte 22 HCG 440 +03 51 57.53 +25 48 31.2 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 7.40 0.94 HCG446_SK207_DH731 GCS9 Cl* Melotte 22 HCG 446 +03 51 58.35 +23 58 19.3 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 1.15 0.65 BPL246_DH732_L07_120 GCS9 Cl* Melotte 22 BPL 246 +03 51 59.32 +24 39 58.8 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.80 0.83 Moraux2003_28 GCS9 Cl* Melotte 22 MBSC 28 +03 51 59.93 +23 24 25.6 0 19.672 18.405 17.376 16.709 16.210 16.167 44.15 3.19 6.95 3.19 5.04 0.00 L07_photNM_15 GCS9 +03 52 01.65 +25 01 29.1 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 3.33 0.89 HCG447_BPL248_DH733 GCS9 Cl* Melotte 22 HCG 447 +03 52 02.10 +23 15 45.4 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 16.77 0.44 BPL249_L07_A1_100_L07_255 GCS9 Cl* Melotte 22 BPL 249 +03 52 02.28 +24 21 47.9 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 4.71 0.28 SK204_DH734 GCS9 Cl* Melotte 22 SK 204 +03 52 02.64 +25 06 14.9 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 2.28 0.91 BPL250_DH736_L07_57 GCS9 Cl* Melotte 22 BPL 250 +03 52 03.58 +25 01 13.6 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 3.16 0.90 BPL251_DH737 GCS9 Cl* Melotte 22 BPL 251 +03 52 03.62 +23 17 21.3 0 14.608 14.228 13.716 13.085 12.840 12.838 8.56 2.26 -19.96 2.26 73.82 0.00 SK202 GCS9 Cl* Melotte 22 SK 202 +03 52 04.48 +24 14 39.6 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 2.51 0.92 BPL252_DH739_L07_91 GCS9 Cl* Melotte 22 BPL 252 +03 52 05.58 +22 34 55.1 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 1.01 0.91 SK201_DH740 GCS9 Cl* Melotte 22 SK 201 +03 52 05.83 +24 17 31.0 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 2.70 0.67 BPL253_CFHT-Pl-7_M5.6_Moraux2003_108_BRB8_CFHT-Pl-7_L07_A1_101_L07_225 GCS9 Cl* Melotte 22 BPL 253 +03 52 06.67 +22 01 14.3 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 1.48 0.62 DH741 GCS9 Cl* Melotte 22 DH 741 +03 52 06.72 +24 16 00.4 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 6.89 0.68 BPL254_CFHT-Pl-13_M6.0_BRB11_CFHT-Pl-13,Teide2,CFHT-PLIZ-3_L07_A1_102_L07_224 GCS9 Cl* Melotte 22 BPL 254 +03 52 07.43 +25 53 02.7 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 1.02 0.88 L07_2 GCS9 +03 52 07.96 +25 27 54.6 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.42 0.67 HCG449_BPL255_DH742 GCS9 Cl* Melotte 22 HCG 449 +03 52 11.01 +24 09 21.6 0 14.675 14.287 13.746 13.155 12.875 12.891 19.68 2.24 -25.40 2.24 3.06 0.00 BPL256 GCS9 Cl* Melotte 22 BPL 256 +03 52 11.23 +20 52 33.4 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.93 0.91 DH743 GCS9 Cl* Melotte 22 DH 743 +03 52 12.19 +26 22 08.9 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 1.31 0.86 HCG452_DH744 GCS9 Cl* Melotte 22 HCG 452 +03 52 13.20 +26 11 45.3 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 1.13 0.88 DH745 GCS9 Cl* Melotte 22 DH 745 +03 52 13.32 +26 08 36.8 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.47 0.82 HCG451_SK197_DH746_L07_1 GCS9 Cl* Melotte 22 HCG 451 +03 52 15.01 +24 20 23.7 0 12.845 12.510 11.997 11.537 11.142 11.317 25.52 2.24 -17.36 2.24 2.89 0.00 BPL257 GCS9 Cl* Melotte 22 BPL 257 +03 52 15.20 +27 56 37.5 0 15.781 15.372 14.821 14.296 14.065 14.036 35.34 3.01 -42.14 3.01 0.77 0.00 DH747 GCS9 Cl* Melotte 22 DH 747 +03 52 17.44 +25 06 11.8 0 15.000 14.563 13.990 13.419 13.132 13.135 -0.61 2.20 -35.73 2.20 4.16 0.00 BPL258 GCS9 Cl* Melotte 22 BPL 258 +03 52 17.54 +24 27 19.7 0 14.481 13.947 13.400 12.888 12.573 12.615 5.97 2.20 -33.38 2.20 95.51 0.00 BPL259_DH748_Moraux2003_42 GCS9 Cl* Melotte 22 BPL 259 +03 52 18.46 +22 00 53.3 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.71 0.91 DH749 GCS9 Cl* Melotte 22 DH 749 +03 52 18.64 +24 04 28.1 0 18.327 17.280 16.395 15.738 15.202 15.738 15.37 2.40 -46.07 2.40 9.62 0.50 CFHT-Pl-23_XX GCS9 Cl* Melotte 22 CFHT 23 +03 52 18.72 +23 52 36.6 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 6.65 0.82 BPL260_L07_127 GCS9 Cl* Melotte 22 BPL 260 +03 52 20.66 +24 33 55.5 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 5.83 0.78 HCG454_SK188_T159_DH750 GCS9 Cl* Melotte 22 HCG 454 +03 52 25.93 +21 50 31.5 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 1.83 0.83 DH752 GCS9 Cl* Melotte 22 DH 752 +03 52 30.54 +19 40 39.6 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 0.89 0.55 DH753 GCS9 Cl* Melotte 22 DH 753 +03 52 30.91 +24 32 39.5 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.86 0.89 HCG456_SK178_BPL261_DH754_Moraux2003_12 GCS9 Cl* Melotte 22 HCG 456 +03 52 31.38 +25 15 07.5 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 7.95 0.92 SK179_BPL262_DH755 GCS9 Cl* Melotte 22 SK 179 +03 52 31.60 +25 19 49.5 0 16.266 15.764 15.163 14.645 14.313 14.280 33.73 2.98 -58.22 2.98 0.55 0.00 BPL263 GCS9 Cl* Melotte 22 BPL 263 +03 52 33.34 +23 51 06.7 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 5.90 0.93 BPL264_DH756_L07_126 GCS9 Cl* Melotte 22 BPL 264 +03 52 34.48 +22 30 07.5 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 1.65 0.63 HCG458_SK174_sk174_DH757 GCS9 Cl* Melotte 22 HCG 458 +03 52 35.32 +25 01 04.5 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 2.24 0.94 BPL265_DH758_L07_61 GCS9 Cl* Melotte 22 BPL 265 +03 52 38.91 +25 50 25.3 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.35 0.89 HCG461_HHJ244_DH760_Moraux2003_30 GCS9 Cl* Melotte 22 HCG 461 +03 52 41.82 +26 46 10.5 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.18 0.74 DH761 GCS9 Cl* Melotte 22 DH 761 +03 52 42.21 +25 10 42.2 0 15.719 15.130 14.525 13.956 13.610 13.606 7.18 2.24 -43.36 2.24 13.94 0.05 DH762 GCS9 Cl* Melotte 22 DH 762 +03 52 43.24 +24 27 58.5 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 1.73 0.93 BPL266_DH763_L07_82 GCS9 Cl* Melotte 22 BPL 266 +03 52 43.47 +27 28 20.7 0 15.621 15.237 14.702 14.167 13.881 13.877 19.52 3.32 -29.27 3.32 0.59 0.00 DH764 GCS9 Cl* Melotte 22 DH 764 +03 52 44.29 +23 54 14.9 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 4.54 0.86 BPL267_DH765_CFHT-Pl-2_M4.9_BRB2_CFHT-Pl-2_L07_128 GCS9 Cl* Melotte 22 BPL 267 +03 52 44.48 +24 20 59.3 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 2.44 0.87 BPL268_DH766_Moraux2003_70 GCS9 Cl* Melotte 22 BPL 268 +03 52 46.45 +24 33 41.0 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 1.87 0.76 BPL270_Moraux2003_47_L07_83 GCS9 Cl* Melotte 22 BPL 270 +03 52 51.72 +22 31 32.7 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 1.02 0.94 HCG462_SK150_HHJ302_DH770 GCS9 Cl* Melotte 22 HCG 462 +03 52 51.79 +23 33 47.9 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 4.57 0.88 HHJ22_BPL272_Moraux2003_99_BRB5_CFHT-Pl-3_MBSC99_L07_148 GCS9 Cl* Melotte 22 HHJ 22 +03 52 54.25 +25 17 43.3 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.44 0.89 HCG464_SK151_BPL274_DH771 GCS9 Cl* Melotte 22 HCG 464 +03 52 55.92 +24 57 41.8 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2.34 0.48 BPL275_L07_A1_103_L07_252 GCS9 Cl* Melotte 22 BPL 275 +03 52 56.97 +22 26 01.1 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.86 0.92 HCG463_SK143_HHJ358_DH772 GCS9 Cl* Melotte 22 HCG 463 +03 52 58.78 +25 26 19.0 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.26 0.90 BPL277 GCS9 Cl* Melotte 22 BPL 277 +03 52 59.48 +22 46 59.9 0 16.051 15.626 15.081 14.514 14.235 14.263 6.77 2.28 -42.20 2.28 15.58 0.02 L07_192 GCS9 +03 53 01.63 +22 58 48.2 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 16.24 0.94 HHJ181_DH776_L07_183 GCS9 Cl* Melotte 22 HHJ 181 +03 53 05.13 +25 04 15.6 0 15.954 15.369 14.759 14.236 13.861 13.829 7.81 2.24 -28.58 2.24 19.42 0.00 HHJ13_BPL278 GCS9 Cl* Melotte 22 HHJ 13 +03 53 07.29 +25 47 21.4 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 1.34 0.32 HHJ53_DH778_Moraux2003_89 GCS9 Cl* Melotte 22 HHJ 53 +03 53 07.48 +25 18 03.4 0 15.833 15.247 14.597 14.025 13.647 13.618 2.66 2.95 -37.03 2.95 0.59 0.00 BPL279 GCS9 Cl* Melotte 22 BPL 279 +03 53 09.63 +23 33 47.7 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.56 0.63 BPL280_CFHT-Pl-4_XX_Moraux2003_101_BRB6_CFHT-Pl-4,MBSC101 GCS9 Cl* Melotte 22 BPL 280 +03 53 10.16 +23 03 10.6 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.47 0.93 HHJ330_DH779 GCS9 Cl* Melotte 22 HHJ 330 +03 53 12.52 +25 48 17.1 0 16.111 15.665 15.069 14.499 14.163 14.185 20.43 2.98 -8.43 2.98 0.61 0.00 Moraux2003_106 GCS9 Cl* Melotte 22 MBSC 106 +03 53 14.00 +19 42 59.3 0 15.849 15.386 14.792 14.189 13.888 13.898 9.11 4.02 -47.37 4.02 0.59 0.10 DH780 GCS9 Cl* Melotte 22 DH 780 +03 53 15.71 +22 52 14.3 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 20.38 0.93 HCG467_SK134_B369_HHJ323_DH781_L07_15 GCS9 Cl* Melotte 22 HCG 467 +03 53 16.44 +23 20 58.1 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 2.72 0.80 BPL281_L07_154 GCS9 Cl* Melotte 22 BPL 281 +03 53 21.68 +24 03 26.6 0 15.326 14.768 14.128 13.552 13.218 7.55 2.31 -30.24 2.31 2.14 0.00 BPL282 GCS9 Cl* Melotte 22 BPL 282 +03 53 24.12 +23 47 58.4 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 4.05 0.89 HHJ173_BPL285_DH784_Moraux2003_39_L07_131 GCS9 Cl* Melotte 22 HHJ 173 +03 53 24.24 +25 14 37.7 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 18.74 0.71 L07_A1_105 GCS9 +03 53 26.54 +24 46 44.8 0 16.063 15.580 14.980 14.429 14.130 14.105 -15.85 2.25 -65.72 2.25 2.82 0.00 L07_photNM_24 GCS9 +03 53 30.76 +19 54 24.1 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.52 0.91 DH787 GCS9 Cl* Melotte 22 DH 787 +03 53 35.52 +26 07 08.0 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.36 0.87 HHJ124_DH790_Moraux2003_64 GCS9 Cl* Melotte 22 HHJ 124 +03 53 44.53 +24 00 42.4 0 16.464 15.928 15.311 14.743 14.396 21.83 2.33 -19.33 2.33 2.88 0.00 BPL289 GCS9 Cl* Melotte 22 BPL 289 +03 53 47.58 +23 44 30.9 0 14.156 13.695 13.118 12.583 12.291 25.83 2.30 -36.52 2.30 12.91 0.18 HCG474_SK115_HHJ237_BPL290_Moraux2003_88 GCS9 Cl* Melotte 22 HCG 474 +03 53 48.04 +23 49 09.6 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 7.95 0.81 HHJ28_BPL291 GCS9 Cl* Melotte 22 HHJ 28 +03 53 48.14 +23 58 12.3 0 16.128 15.626 15.014 14.480 14.122 45.32 2.32 -23.11 2.32 16.02 0.00 BPL292 GCS9 Cl* Melotte 22 BPL 292 +03 53 48.49 +23 29 09.3 0 13.958 13.639 13.144 12.476 12.276 12.276 22.11 2.25 -50.69 2.25 1.26 0.30 L07_11 GCS9 +03 53 49.10 +23 32 49.5 0 16.498 15.857 15.188 14.669 14.312 14.255 20.11 2.27 -19.91 2.27 1.92 0.00 BPL293 GCS9 Cl* Melotte 22 BPL 293 +03 53 51.53 +23 37 32.4 0 15.072 14.583 13.986 13.438 13.123 13.114 10.35 2.25 -31.49 2.25 0.90 0.00 L07_142 GCS9 +03 53 52.46 +22 37 34.1 0 13.916 13.447 12.840 12.297 11.984 11.987 21.25 2.50 -22.78 2.50 0.32 0.00 SK109 GCS9 Cl* Melotte 22 SK 109 +03 53 55.13 +23 23 36.1 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 8.66 0.70 BPL294_CFHT-Pl-12_M8.0_BRB9_CFHT-Pl-12,CFHT-PLIZ-6_PLZJ9_PLIZ6 GCS9 Cl* Melotte 22 BPL 294 +03 54 00.71 +23 58 59.8 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.03 2.24 7.85 0.73 HHJ322_BPL298 GCS9 Cl* Melotte 22 HHJ 322 +03 54 01.12 +23 19 41.0 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 12.64 0.83 HHJ20_BPL299 GCS9 Cl* Melotte 22 HHJ 20 +03 54 02.73 +23 35 00.9 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 6.97 0.80 BPL301_Moraux2003_68_L07_141 GCS9 Cl* Melotte 22 BPL 301 +03 54 04.89 +25 05 06.2 0 14.698 14.348 13.849 13.358 13.082 13.085 33.90 2.23 -56.38 2.23 10.70 0.00 BPL302 GCS9 Cl* Melotte 22 BPL 302 +03 54 05.35 +23 33 59.2 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 5.84 0.61 BPL303_CFHT-Pl-25_M9.0_BRB15_CFHT-PLIZ-20,PLZJ11_L07_A1_106_L07_254 GCS9 Cl* Melotte 22 BPL 303 +03 54 11.49 +25 18 42.7 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.56 0.88 HCG484_BPL304_DH796 GCS9 Cl* Melotte 22 HCG 484 +03 54 13.02 +23 20 50.8 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 1.02 0.94 HCG482_SK97_HHJ325_BPL305_DH797_Moraux2003_13 GCS9 Cl* Melotte 22 HCG 482 +03 54 14.07 +23 17 51.9 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 5.30 0.47 BRB18_L0.0_CFHT-PLIZ-28 GCS9 Cl* Melotte 22 BRB 18 +03 54 14.18 +25 37 57.2 0 13.687 13.351 12.846 12.276 12.008 11.988 15.24 2.93 -23.51 2.93 0.25 0.00 SK99_HHJ318 GCS9 Cl* Melotte 22 SK 99 +03 54 15.29 +25 09 52.2 0 17.907 17.026 16.179 15.579 15.102 15.061 14.10 2.34 -32.82 2.34 5.56 0.04 BPL306_L07_A1_107 GCS9 Cl* Melotte 22 BPL 306 +03 54 15.60 +24 20 45.7 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 4.55 0.33 BPL307_BPL308_Moraux2003_96 GCS9 Cl* Melotte 22 BPL 307 +03 54 16.80 +22 00 16.6 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 1.97 0.67 DH799 GCS9 Cl* Melotte 22 DH 799 +03 54 19.69 +24 41 52.0 0 13.851 13.644 13.223 12.660 12.539 12.515 24.74 2.23 -26.36 2.23 1.62 0.00 L07_6 GCS9 +03 54 21.23 +23 23 48.9 0 13.899 13.505 12.946 12.350 12.071 12.051 17.29 2.25 -14.89 2.25 10.35 0.00 BPL310 GCS9 Cl* Melotte 22 BPL 310 +03 54 22.49 +23 38 11.9 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 7.78 0.77 HHJ214_BPL311_DH801_Moraux2003_38_L07_143 GCS9 Cl* Melotte 22 HHJ 214 +03 54 24.00 +23 51 58.7 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 12.19 0.58 L07_123 GCS9 +03 54 24.39 +22 41 20.5 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 6.55 0.92 L07_190 GCS9 +03 54 25.04 +24 42 43.4 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 3.32 0.94 HCG487_HHJ131_BPL312_DH802_Moraux2003_67_L07_71 GCS9 Cl* Melotte 22 HCG 487 +03 54 28.11 +23 56 36.0 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 2.36 0.85 BPL313 GCS9 Cl* Melotte 22 BPL 313 +03 54 31.49 +22 39 01.5 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 3.40 0.53 L07_A1_108_L07_244 GCS9 +03 54 33.14 +23 33 39.5 0 13.404 13.138 12.649 12.026 11.829 11.837 29.86 2.24 -16.03 2.24 2.59 0.00 BPL314 GCS9 Cl* Melotte 22 BPL 314 +03 54 34.80 +21 53 01.7 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 5.77 0.87 HCG488_DH804 GCS9 Cl* Melotte 22 HCG 488 +03 54 37.36 +23 13 32.5 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 7.31 0.81 DH805 GCS9 Cl* Melotte 22 DH 805 +03 54 39.13 +24 35 54.5 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 3.51 0.88 BPL315_DH806_Moraux2003_103 GCS9 Cl* Melotte 22 BPL 315 +03 54 40.56 +23 42 23.7 0 16.133 15.654 15.075 14.441 14.147 14.128 21.00 2.26 -8.72 2.26 2.22 0.00 L07_PM_NM_45 GCS9 +03 54 46.39 +25 02 58.2 0 15.875 15.298 14.639 14.106 13.746 13.746 12.58 2.24 -33.12 2.24 3.87 0.03 BPL317 GCS9 Cl* Melotte 22 BPL 317 +03 54 46.53 +25 31 34.9 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.23 0.90 DH808 GCS9 Cl* Melotte 22 DH 808 +03 54 48.00 +25 12 30.2 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.13 2.23 9.47 0.87 SK82_HHJ371_BPL318_DH809 GCS9 Cl* Melotte 22 SK 82 +03 54 55.34 +22 59 40.1 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 3.01 0.78 HHJ62 GCS9 Cl* Melotte 22 HHJ 62 +03 54 58.03 +25 14 29.0 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 3.97 0.82 SK76_BPL321_DH810 GCS9 Cl* Melotte 22 SK 76 +03 54 59.43 +23 58 07.6 0 18.399 17.751 17.008 16.573 16.182 16.573 -15.56 2.74 -76.49 2.74 2.13 0.00 CFHT-Pl-22_XX GCS9 Cl* Melotte 22 CFHT 22 +03 55 03.69 +25 13 16.7 0 15.227 14.748 14.150 13.541 13.229 13.257 24.07 2.23 -25.82 2.23 2.86 0.00 BPL322 GCS9 Cl* Melotte 22 BPL 322 +03 55 05.75 +23 03 17.1 0 17.281 16.747 16.124 15.457 15.159 15.148 22.55 2.35 -23.42 2.35 7.78 0.00 DH811 GCS9 Cl* Melotte 22 DH 811 +03 55 07.09 +24 17 25.7 0 13.823 13.494 12.962 12.312 12.069 12.072 45.79 2.24 -23.35 2.24 9.80 0.00 Moraux2003_24 GCS9 Cl* Melotte 22 MBSC 24 +03 55 08.98 +24 05 02.5 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 2.15 0.90 SK65_HHJ313_BPL324_DH812_Moraux2003_18 GCS9 Cl* Melotte 22 SK 65 +03 55 10.57 +23 40 30.8 0 16.148 15.629 15.011 14.450 14.138 14.42 2.32 -24.94 2.32 4.22 0.00 Moraux2003_107 GCS9 Cl* Melotte 22 MBSC 107 +03 55 11.85 +22 58 02.5 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 8.86 0.83 HHJ61 GCS9 Cl* Melotte 22 HHJ 61 +03 55 17.17 +23 53 16.7 0 16.639 15.930 15.233 14.684 14.291 14.302 -3.54 2.27 -45.04 2.27 8.18 0.00 BPL325 GCS9 Cl* Melotte 22 BPL 325 +03 55 20.68 +22 32 08.6 0 14.849 14.456 13.903 13.290 13.023 13.017 25.35 2.51 -36.82 2.51 2.55 0.26 HHJ144_DH814 GCS9 Cl* Melotte 22 HHJ 144 +03 55 23.08 +24 49 04.9 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 7.67 0.72 BPL327_IPMBD11_PLZJ78_PLIZ2 GCS9 Cl* Melotte 22 BPL 327 +03 55 24.90 +23 27 21.2 0 14.218 13.952 13.438 12.772 12.577 12.555 17.19 2.25 -18.72 2.25 12.58 0.00 L07_165 GCS9 +03 55 27.06 +25 14 45.8 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 7.07 0.60 BPL328_L07_A1_111_L07_216 GCS9 Cl* Melotte 22 BPL 328 +03 55 30.90 +23 23 50.9 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 14.93 0.52 SK54_HHJ284_BPL329_DH815_Moraux2003_32 GCS9 Cl* Melotte 22 SK 54 +03 55 32.84 +23 19 08.0 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 7.73 0.61 DH2004_816 GCS9 Cl* Melotte 22 DH 816 +03 55 34.43 +23 58 28.3 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 6.79 0.55 HHJ78_BPL330_DH818_Moraux2003_80 GCS9 Cl* Melotte 22 HHJ 78 +03 55 44.34 +23 23 26.3 0 14.936 14.679 14.164 13.488 13.317 13.333 24.36 2.26 -31.05 2.26 5.80 0.00 L07_164 GCS9 +03 55 47.14 +25 14 39.5 0 17.252 16.517 15.773 15.223 14.793 14.775 11.42 2.29 -27.04 2.29 4.43 0.00 L07_A1_112 GCS9 +03 55 47.45 +22 50 50.1 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 3.85 0.63 L07_A1_113 GCS9 +03 55 49.92 +23 48 22.5 0 15.778 15.219 14.575 14.013 13.650 13.627 40.52 2.97 -15.60 2.97 0.28 0.00 BPL332 GCS9 Cl* Melotte 22 BPL 332 +03 55 50.11 +21 43 27.7 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 0.38 0.35 DH819 GCS9 Cl* Melotte 22 DH 819 +03 55 56.43 +25 17 59.8 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.63 0.94 HCG504_SK37_DH822 GCS9 Cl* Melotte 22 HCG 504 +03 55 57.55 +21 10 38.7 0 13.543 13.117 12.574 12.061 11.714 11.762 6.82 3.36 -22.12 3.36 1.12 0.00 DH823 GCS9 Cl* Melotte 22 DH 823 +03 56 09.81 +22 27 59.3 0 13.653 13.341 12.818 12.142 11.912 11.933 27.43 2.50 -42.46 2.50 0.16 0.22 HHJ385 GCS9 Cl* Melotte 22 HHJ 385 +03 56 18.60 +23 57 51.6 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.46 0.89 SK22_HHJ386_DH830 GCS9 Cl* Melotte 22 SK 22 +03 56 22.31 +21 07 16.9 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 0.54 0.40 DH831 GCS9 Cl* Melotte 22 DH 831 +03 56 22.39 +24 37 22.8 0 17.293 16.827 16.215 15.615 15.296 15.316 19.53 2.64 -22.35 2.64 1.45 0.00 DH832 GCS9 Cl* Melotte 22 DH 832 +03 56 24.99 +23 05 26.6 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 1.45 0.72 SK17_HHJ419 GCS9 Cl* Melotte 22 SK 17 +03 56 25.93 +24 16 51.3 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.24 0.83 SK18_HHJ416_DH834 GCS9 Cl* Melotte 22 SK 18 +03 56 28.91 +24 01 54.1 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.55 0.93 HHJ165_DH837_Moraux2003_41 GCS9 Cl* Melotte 22 HHJ 165 +03 56 36.56 +25 15 52.3 0 12.102 12.000 11.678 11.535 11.250 11.370 20.72 2.47 -47.20 2.47 9.47 0.56 DH2004_840 GCS9 Cl* Melotte 22 DH 840 +03 56 38.76 +21 35 08.3 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.16 0.93 DH841 GCS9 Cl* Melotte 22 DH 841 +03 56 39.34 +24 31 42.3 0 16.146 15.667 15.060 14.438 14.108 14.131 6.45 2.50 -37.23 2.50 0.69 0.00 BPL338 GCS9 Cl* Melotte 22 BPL 338 +03 56 39.67 +22 41 12.0 0 16.699 16.176 15.614 15.000 14.672 14.657 23.26 3.06 -34.21 3.06 0.25 0.04 DH842 GCS9 Cl* Melotte 22 DH 842 +03 56 46.60 +26 21 00.4 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 2.18 0.77 HHJ64_DH843 GCS9 Cl* Melotte 22 HHJ 64 +03 56 52.31 +25 10 05.1 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2.72 0.54 HHJ17 GCS9 Cl* Melotte 22 HHJ 17 +03 56 52.91 +23 25 43.7 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.16 0.89 HHJ271_DH846_Moraux2003_35 GCS9 Cl* Melotte 22 HHJ 271 +03 56 55.47 +22 08 24.3 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.38 0.80 DH847 GCS9 Cl* Melotte 22 DH 847 +03 56 57.08 +24 48 34.3 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 2.78 0.89 HCG511_HHJ393_DH848 GCS9 Cl* Melotte 22 HCG 511 +03 57 05.73 +22 49 14.9 0 15.172 14.200 13.601 13.300 13.290 41.88 3.06 -25.84 3.06 0.73 0.00 HHJ85 GCS9 Cl* Melotte 22 HHJ 85 +03 57 08.61 +20 07 42.7 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.70 0.90 DH849 GCS9 Cl* Melotte 22 DH 849 +03 57 09.81 +21 36 27.2 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 1.52 0.30 DH850 GCS9 Cl* Melotte 22 DH 850 +03 57 40.68 +25 16 04.1 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 0.52 0.37 DH856 GCS9 Cl* Melotte 22 DH 856 +03 57 42.98 +25 23 06.7 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 2.98 0.92 HCG516_HHJ180_DH857 GCS9 Cl* Melotte 22 HCG 516 +03 57 49.37 +22 08 30.9 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.50 0.72 HHJ7_DH858 GCS9 Cl* Melotte 22 HHJ 7 +03 57 49.44 +23 28 40.9 0 16.455 16.008 15.423 14.867 14.538 14.486 21.43 3.05 -44.61 3.05 1.11 0.60 HHJ4 GCS9 Cl* Melotte 22 HHJ 4 +03 57 52.35 +21 02 15.8 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 0.07 0.37 DH859 GCS9 Cl* Melotte 22 DH 859 +03 57 55.85 +21 16 10.8 0 15.354 14.971 14.436 13.805 13.526 13.536 16.87 3.37 -36.80 3.37 0.12 0.62 DH860 GCS9 Cl* Melotte 22 DH 860 +03 58 01.97 +23 53 54.5 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.06 0.88 HHJ84_DH862 GCS9 Cl* Melotte 22 HHJ 84 +03 58 13.93 +25 06 27.3 0 13.268 12.878 12.303 11.694 11.359 11.407 -0.09 2.47 -50.14 2.47 7.14 0.00 HHJ369 GCS9 Cl* Melotte 22 HHJ 369 +03 58 25.14 +24 00 58.5 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.84 0.92 HHJ220_DH865 GCS9 Cl* Melotte 22 HHJ 220 +03 58 30.99 +23 04 12.7 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.55 0.89 HHJ60_DH866 GCS9 Cl* Melotte 22 HHJ 60 +03 58 34.18 +22 40 11.1 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 1.36 0.81 HHJ93_DH867 GCS9 Cl* Melotte 22 HHJ 93 +03 58 56.15 +23 27 53.5 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.72 0.81 HHJ388_DH868 GCS9 Cl* Melotte 22 HHJ 388 +03 58 57.04 +23 42 31.1 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.94 0.82 HCG519_HHJ411_DH870 GCS9 Cl* Melotte 22 HCG 519 +03 59 05.73 +26 24 26.6 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.87 0.93 DH871 GCS9 Cl* Melotte 22 DH 871 +03 59 06.31 +25 03 20.0 0 13.383 12.988 12.334 11.705 11.382 11.486 4.31 2.47 -61.05 2.47 21.47 0.00 HHJ366_DH872 GCS9 Cl* Melotte 22 HHJ 366 +03 59 12.92 +24 17 18.4 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 0.41 0.22 DH873 GCS9 Cl* Melotte 22 DH 873 +03 59 15.87 +25 54 08.3 0 14.875 14.465 13.906 13.228 12.965 12.971 32.55 2.48 -38.84 2.48 0.41 0.00 HHJ133 GCS9 Cl* Melotte 22 HHJ 133 +03 59 18.99 +23 31 23.9 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.84 0.92 HHJ150_DH874 GCS9 Cl* Melotte 22 HHJ 150 +03 59 26.36 +22 21 22.2 0 15.719 15.289 14.695 14.117 13.819 13.820 31.13 2.88 -39.73 2.88 0.46 0.00 HHJ39 GCS9 Cl* Melotte 22 HHJ 39 +03 59 26.93 +21 48 18.7 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 1.42 0.94 DH876 GCS9 Cl* Melotte 22 DH 876 +03 59 29.33 +21 39 56.0 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.34 0.79 DH878 GCS9 Cl* Melotte 22 DH 878 +03 59 31.47 +21 16 18.3 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.74 0.87 DH879 GCS9 Cl* Melotte 22 DH 879 +03 59 52.43 +22 21 57.6 0 13.184 12.972 12.496 11.861 11.662 11.699 22.40 2.84 -35.16 2.84 0.08 0.23 DH880 GCS9 Cl* Melotte 22 DH 880 +03 59 56.49 +23 40 15.3 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.37 0.69 DH882 GCS9 Cl* Melotte 22 DH 882 +03 59 59.15 +23 41 04.6 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.30 0.83 DH883 GCS9 Cl* Melotte 22 DH 883 +03 59 59.86 +22 05 29.3 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 0.93 0.58 DH884 GCS9 Cl* Melotte 22 DH 884 +04 00 00.40 +23 47 07.2 0 15.850 15.374 14.799 14.250 13.943 27.42 3.57 -41.36 3.57 0.20 0.14 DH885 GCS9 Cl* Melotte 22 DH 885 +04 00 02.53 +26 36 02.0 0 14.847 14.477 13.972 13.308 13.038 13.047 4.33 2.48 -47.48 2.48 6.50 0.00 DH886 GCS9 Cl* Melotte 22 DH 886 +04 00 10.30 +22 02 17.0 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.29 0.94 DH888 GCS9 Cl* Melotte 22 DH 888 +04 00 14.11 +24 48 51.4 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.44 0.77 DH889 GCS9 Cl* Melotte 22 DH 889 +04 00 26.15 +23 26 17.3 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.11 0.89 DH891 GCS9 Cl* Melotte 22 DH 891 +04 00 28.19 +23 51 24.0 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.64 0.78 DH892 GCS9 Cl* Melotte 22 DH 892 +04 00 28.35 +27 20 54.5 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.16 0.81 DH893 GCS9 Cl* Melotte 22 DH 893 +04 00 39.24 +26 44 19.0 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 3.18 0.26 DH894 GCS9 Cl* Melotte 22 DH 894 +04 00 49.33 +25 12 10.6 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.15 0.89 DH895 GCS9 Cl* Melotte 22 DH 895 +04 01 08.61 +22 57 22.3 0 14.093 13.707 13.221 12.574 12.334 12.323 41.92 3.35 -24.05 3.35 38.80 0.00 DH897 GCS9 Cl* Melotte 22 DH 897 +04 01 12.08 +23 54 12.8 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.46 0.82 DH898 GCS9 Cl* Melotte 22 DH 898 +04 01 13.99 +23 10 29.9 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.17 0.77 DH899 GCS9 Cl* Melotte 22 DH 899 +04 01 26.06 +21 35 08.5 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.12 0.94 DH900 GCS9 Cl* Melotte 22 DH 900 +04 01 49.17 +22 10 05.0 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.13 0.83 DH901 GCS9 Cl* Melotte 22 DH 901 +04 01 59.58 +25 19 12.2 0 16.333 15.812 15.179 14.530 14.202 14.168 19.23 3.35 -37.71 3.35 0.41 0.48 DH902 GCS9 Cl* Melotte 22 DH 902 +04 02 22.62 +24 48 24.0 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.66 0.89 DH903 GCS9 Cl* Melotte 22 DH 903 +04 02 49.97 +23 30 38.4 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.12 0.81 DH904 GCS9 Cl* Melotte 22 DH 904 +04 03 01.22 +25 24 23.0 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 0.24 0.27 DH905 GCS9 Cl* Melotte 22 DH 905 +04 03 16.56 +24 35 19.6 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.10 0.83 DH906 GCS9 Cl* Melotte 22 DH 906 +04 03 24.94 +22 52 17.0 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.56 0.92 DH2004_907 GCS9 Cl* Melotte 22 DH 907 +04 03 40.67 +25 21 34.5 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 0.13 0.25 DH908 GCS9 Cl* Melotte 22 DH 908 +04 03 43.84 +23 53 47.0 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.36 0.93 DH909 GCS9 Cl* Melotte 22 DH 909 +04 03 49.57 +23 43 13.7 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.18 0.90 DH910 GCS9 Cl* Melotte 22 DH 910 +04 04 35.56 +25 07 14.7 0 13.458 13.161 12.678 12.087 11.856 11.863 18.96 2.94 -34.69 2.94 1.29 0.30 DH911 GCS9 Cl* Melotte 22 DH 911 +04 04 45.65 +24 41 19.1 0 14.008 13.433 12.771 12.193 11.842 11.858 15.03 2.94 -29.97 2.94 0.86 0.00 DH912 GCS9 Cl* Melotte 22 DH 912 +04 05 13.75 +24 08 42.7 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.54 0.94 DH915 GCS9 Cl* Melotte 22 DH 915 +04 06 29.99 +22 33 43.6 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 0.55 0.14 DH2004_916 GCS9 Cl* Melotte 22 DH 916 +03 27 35.60 +24 31 42.8 0 11.913 11.791 11.262 11.438 10.650 29.84 6.95 -51.81 6.95 5.58 NM DH001 GCS9 Cl* Melotte 22 DH 001 +03 27 37.78 +24 59 00.4 0 18.239 17.866 17.264 16.663 16.418 4.37 9.41 -21.38 9.41 0.68 NM DH2004_2 GCS9 Cl* Melotte 22 DH 2 +03 40 27.44 +24 20 42.0 0 19.876 19.366 18.295 17.674 17.142 0.114 19.62 5.81 -2.47 5.81 2.79 NM int-pl-IZ-83;IPLJ0340274+242042_N GCS9 Cl* Melotte 22 IPL 83 +03 40 40.02 +24 44 08.9 0 13.551 13.221 12.733 12.866 11.933 11.954 100.30 2.48 -40.25 2.48 710.82 NM HCG40_SK760_HHJ412 GCS9 Cl* Melotte 22 HCG 40 +03 40 42.48 +22 25 52.9 0 17.673 17.180 16.589 16.016 15.679 15.679 20.34 2.74 -38.19 2.74 3.77 NM DH149 GCS9 Cl* Melotte 22 DH 149 +03 40 47.27 +23 52 35.9 0 16.329 15.775 15.144 14.569 14.220 14.229 -14.15 2.16 -37.59 2.16 2.18 NM BPL16 GCS9 Cl* Melotte 22 BPL 16 +03 40 52.79 +25 24 43.5 0 19.211 18.106 17.097 16.437 15.871 15.971 11.63 2.75 -0.11 2.75 1.90 NM L07_A1_3 GCS9 +03 41 13.48 +26 01 18.1 0 14.297 14.031 13.545 13.781 12.723 12.729 -45.56 2.23 13.58 2.23 883.14 NM L07_19 GCS9 +03 41 20.82 +25 41 15.4 0 14.693 14.445 13.990 13.378 13.244 13.251 15.68 2.23 -33.00 2.23 4.42 NM L07_37 GCS9 +03 41 31.45 +23 05 12.5 0 17.388 17.172 16.798 16.334 16.205 16.153 -4.54 2.83 5.67 2.83 3.33 NM L07_photNM_1 GCS9 +03 41 33.33 +24 00 56.8 0 16.757 16.263 15.632 15.043 14.669 0.010 -0.93 2.35 28.95 2.35 0.35 NM int-pl-IZ-13;IPLJ0341333+240056_N GCS9 Cl* Melotte 22 IPL 13 +03 41 33.76 +24 11 18.7 0 16.689 16.102 15.481 14.947 14.519 0.009 -17.54 2.34 -3.76 2.34 0.87 NM int-pl-IZ-89;IPLJ0341337+241118_BPL24_Y GCS9 Cl* Melotte 22 IPL 89 +03 41 37.74 +23 04 32.7 0 17.983 17.097 16.312 15.674 15.230 15.224 55.66 2.38 -15.82 2.38 1.35 NM L07_A1_7 GCS9 +03 41 45.01 +24 02 00.5 0 20.471 21.487 18.728 18.097 17.603 0.192 -13.87 6.33 2.10 6.33 2.08 NM int-pl-IZ-12;IPLJ0341450+240200_N GCS9 Cl* Melotte 22 IPL 12 +03 41 45.82 +23 14 26.1 0 16.057 15.577 14.958 14.278 13.965 13.959 11.09 2.27 -13.29 2.27 1.09 NM L07_PM_NM_1 GCS9 +03 42 01.36 +24 07 42.8 0 20.269 19.509 18.937 18.788 18.036 0.158 44.55 7.28 -34.90 7.28 2.78 NM int-pl-IZ-79;IPLJ0342013+240742_N GCS9 Cl* Melotte 22 IPL 79 +03 42 04.72 +23 29 04.2 0 16.255 15.752 15.126 14.475 14.133 14.135 5.09 2.27 -8.96 2.27 1.00 NM L07_PM_NM_2 GCS9 +03 42 07.98 +22 39 33.4 0 18.739 17.474 16.501 15.831 15.206 15.225 -14.88 2.43 -2.56 2.43 5.87 NM int-pl-IZ-76_2MASSJ0342080+223933_Y_L07_A1_12 GCS9 Cl* Melotte 22 IPL 76 +03 42 10.17 +22 48 44.5 0 17.259 16.355 15.543 14.932 14.469 14.500 21.01 2.29 -90.42 2.29 1.52 NM L07_PM_NM_3 GCS9 +03 42 16.89 +23 23 26.2 0 14.399 14.006 13.488 12.830 12.556 12.534 4.44 2.25 -20.25 2.25 2.48 NM SK694 GCS9 Cl* Melotte 22 SK 694 +03 42 26.81 +24 50 20.5 0 16.617 15.878 15.170 14.570 14.175 14.570 5.87 3.00 7.70 3.00 11.87 NM SFHT-Pl-8_XX GCS9 Cl* Melotte 22 CFHT 8 +03 42 27.94 +25 25 20.0 0 18.768 18.348 17.661 17.160 16.956 16.830 -1.58 3.77 -14.43 3.77 1.80 NM L07_photNM_2 GCS9 +03 42 28.52 +24 03 40.9 0 17.374 16.756 16.090 15.493 15.055 0.015 14.53 2.24 4.35 2.24 2.29 NM int-pl-IZ-32;IPLJ0342285+240340_N GCS9 Cl* Melotte 22 IPL 32 +03 42 43.46 +22 38 31.3 0 19.335 18.795 18.306 17.850 17.254 17.390 6.96 6.04 -3.70 6.04 3.63 NM L07_faintJK_9 GCS9 +03 42 46.24 +23 54 50.4 0 18.364 17.566 16.865 16.291 15.869 0.031 -9.31 2.56 -4.06 2.56 2.52 NM int-pl-IZ-30;2MASSJ0342462+235450_N GCS9 Cl* Melotte 22 IPL 30 +03 42 49.78 +23 55 53.0 0 17.226 16.592 15.902 15.202 14.834 0.014 -20.00 2.22 -24.83 2.22 1.37 NM int-pl-IZ-28;2MASSJ0342497+235553_N GCS9 Cl* Melotte 22 IPL 28 +03 43 03.28 +23 41 30.7 0 18.320 17.643 16.965 16.316 15.973 0.032 0.97 2.58 -4.45 2.58 2.37 NM int-pl-IZ-10;IPLJ0343032+234130_N GCS9 Cl* Melotte 22 IPL 10 +03 43 12.21 +23 09 16.5 0 18.623 17.585 16.672 16.054 15.582 15.559 23.62 2.52 -78.70 2.52 5.17 NM L07_photNM_3 GCS9 +03 43 13.91 +24 08 20.2 0 14.902 14.430 13.830 13.297 12.985 13.008 7.02 2.13 -16.75 2.13 1.63 NM BPL55 GCS9 Cl* Melotte 22 BPL 55 +03 43 13.93 +23 47 46.0 0 16.658 16.080 15.498 15.003 14.645 0.010 80.42 2.19 -64.23 2.19 0.96 NM int-pl-IZ-8;2MASSJ0343138+234746_N GCS9 Cl* Melotte 22 IPL 8 +03 43 29.16 +24 02 06.4 0 16.723 16.206 15.569 14.907 14.555 0.010 3.90 2.33 -14.75 2.33 0.81 NM int-pl-IZ-22;IPLJ0343291+240206_N GCS9 Cl* Melotte 22 IPL 22 +03 43 29.87 +24 15 47.3 0 13.312 13.075 12.598 11.994 11.788 -2.96 2.30 -30.76 2.30 0.73 NM BPL60 GCS9 Cl* Melotte 22 BPL 60 +03 43 41.55 +23 38 56.9 0 13.507 13.136 11.471 13.159 10.941 68.56 2.30 127.64 2.30 9.62 NM HII157_HD23157_Tr050 GCS9 HII157 +03 43 47.80 +24 03 17.0 0 19.840 18.837 18.173 17.520 17.095 0.106 17.69 3.88 0.19 3.88 4.05 NM int-pl-IZ-18;IPLJ0343477+240316_N GCS9 Cl* Melotte 22 IPL 18 +03 43 48.10 +22 42 19.4 0 16.799 16.182 15.580 15.085 14.719 0.011 -12.71 2.31 -6.73 2.31 5.51 NM int-pl-IZ-77;2MASSJ0343481+224219_N GCS9 Cl* Melotte 22 IPL 77 +03 43 49.31 +24 07 42.2 0 13.277 13.106 12.742 12.360 12.239 10.92 2.30 -2.78 2.30 1.99 NM BPL64 GCS9 Cl* Melotte 22 BPL 64 +03 43 52.03 +22 55 24.5 0 18.877 17.799 16.954 16.281 15.790 15.790 8.59 2.63 -3.83 2.63 2.80 NM int-pl-IZ-55_IPLJ0343520+225524_Y_L07_photNM_4 GCS9 Cl* Melotte 22 IPL 55 +03 43 53.05 +23 21 50.2 0 16.148 15.662 15.035 14.372 14.027 14.014 -14.95 2.27 -18.64 2.27 6.97 NM L07_PM_NM_4 GCS9 +03 44 09.32 +23 02 18.5 0 18.527 17.911 17.141 16.542 16.135 0.040 -0.31 2.78 0.00 2.78 4.10 NM int-pl-IZ-54;IPLJ0344093+230218_N GCS9 Cl* Melotte 22 IPL 54 +03 44 09.39 +23 17 07.2 0 16.376 15.837 15.168 14.608 14.201 14.250 -32.76 2.28 2.92 2.28 11.23 NM L07_PM_NM_5 GCS9 +03 44 10.08 +22 43 57.8 0 16.296 15.777 15.173 14.582 14.243 14.252 4.33 2.28 -0.56 2.28 2.86 NM L07_PM_NM_6 GCS9 +03 44 10.90 +23 40 15.0 0 19.101 18.430 17.764 17.019 16.548 -2.43 3.36 -8.57 3.36 1.93 NM Roque3 GCS9 Cl* Melotte 22 Roque 3 +03 44 12.66 +23 43 17.6 0 20.244 19.037 18.123 17.534 17.170 15.97 4.26 -3.99 4.26 3.70 NM Roque18 GCS9 Cl* Melotte 22 Roque 18 +03 44 12.68 +25 24 35.1 0 18.091 17.245 16.455 15.959 15.528 15.959 -5.40 2.49 16.14 2.49 2.91 NM CFHT-Pl-20_L07_photNM_5 GCS9 Cl* Melotte 22 CFHT 20 +03 44 20.84 +24 39 02.8 0 19.245 18.126 17.127 16.400 15.949 3.92 3.63 7.91 3.63 6.22 NM Roque36 GCS9 Cl* Melotte 22 Roque 36 +03 44 26.36 +26 02 30.9 0 13.193 12.826 12.324 11.355 11.457 11.506 1.38 2.23 -17.15 2.23 540.35 NM HCG142_SK561_T10_DH337 GCS9 Cl* Melotte 22 HCG 142 +03 44 26.46 +22 40 00.5 0 15.389 15.105 14.594 13.943 13.721 13.732 23.92 2.25 -18.53 2.25 38.11 NM L07_195 GCS9 +03 44 33.80 +22 42 49.2 0 16.520 15.949 15.320 14.714 14.349 14.347 3.12 2.26 9.87 2.26 2.53 NM L07_PM_NM_7 GCS9 +03 44 44.62 +22 49 10.5 0 19.674 19.358 18.763 18.140 18.089 0.100 0.73 5.90 -8.60 5.90 2.55 NM int-pl-IZ-52;IPLJ0344446+224911_N GCS9 Cl* Melotte 22 IPL 52 +03 44 50.99 +25 21 43.5 0 19.424 18.545 17.899 17.322 16.856 16.853 4.78 3.88 -9.91 3.88 4.18 NM L07_262 GCS9 +03 44 58.97 +23 23 19.9 0 11.820 11.684 11.266 11.507 10.629 10.890 19.95 2.23 -34.31 2.23 2.42 NM HII513_DH364 GCS9 Cl* Melotte 22 HII 513 +03 45 12.28 +22 58 31.8 0 14.301 14.092 13.643 13.024 12.948 12.939 17.40 2.24 -40.22 2.24 3.39 NM L07_188 GCS9 +03 45 23.81 +21 52 38.7 0 15.066 14.597 14.011 13.441 13.158 13.108 12.19 2.27 -5.33 2.27 2.63 NM BPL72 GCS9 Cl* Melotte 22 BPL 72 +03 45 25.31 +22 22 07.8 0 19.833 18.841 18.030 17.345 16.867 0.125 0.66 4.90 -0.37 4.90 2.84 NM int-pl-IZ-68;IPLJ0345252+222208_N GCS9 Cl* Melotte 22 IPL 68 +03 45 28.34 +23 48 09.6 0 17.089 16.378 15.577 14.726 14.303 14.314 12.66 2.24 -4.80 2.24 4.43 NM L07_PM_NM_8 GCS9 +03 45 29.87 +22 24 14.5 0 16.385 15.797 15.183 14.585 14.229 14.242 60.29 2.30 -74.75 2.30 16.18 NM BPL74_L07_PM_NM_9 GCS9 Cl* Melotte 22 BPL 74 +03 45 33.16 +25 34 30.0 0 18.115 17.203 16.409 15.847 15.398 15.847 -6.60 2.43 -17.46 2.43 3.65 NM CFHT-Pl-19_L07_photNM_6 GCS9 Cl* Melotte 22 CFHT 19 +03 45 34.50 +23 41 43.5 0 17.069 16.371 15.611 14.865 14.455 14.451 -12.61 2.24 -23.80 2.24 3.02 NM L07_PM_NM_10 GCS9 +03 45 36.44 +24 18 15.4 0 16.225 15.689 15.049 14.432 14.104 14.091 9.33 2.24 -4.26 2.24 4.09 NM L07_PM_NM_11 GCS9 +03 45 36.50 +22 28 31.9 0 19.069 18.388 17.472 17.000 16.703 0.064 -24.55 3.48 -17.14 3.48 10.20 NM int-pl-IZ-66;IPLJ0345365+222832_N GCS9 Cl* Melotte 22 IPL 66 +03 45 36.85 +23 44 47.8 0 16.877 16.225 15.470 14.619 14.255 14.237 18.08 2.24 0.32 2.24 17.83 NM L07_PM_NM_12 GCS9 +03 45 37.25 +23 49 21.0 0 16.385 15.847 15.119 14.210 13.883 13.880 -6.65 2.23 -8.20 2.23 1.69 NM L07_PM_NM_13 GCS9 +03 45 43.11 +25 40 23.1 0 16.198 15.009 13.924 13.240 12.663 12.643 -96.92 2.23 -40.65 2.23 2.96 NM L07_PM_NM_14 GCS9 +03 45 45.25 +22 58 44.6 0 16.700 16.061 15.359 14.836 14.433 0.010 57.50 2.26 -53.09 2.26 10.93 NM int-pl-IZ-58;2MASSJ0345452+225845_BPL76_Y GCS9 Cl* Melotte 22 IPL 58 +03 45 49.90 +23 45 59.8 0 16.226 15.601 14.814 13.904 13.593 13.583 0.36 2.23 -2.57 2.23 0.62 NM L07_PM_NM_15 GCS9 +03 45 52.65 +25 51 42.0 0 12.679 12.343 11.821 11.385 10.925 11.124 122.72 2.23 -53.12 2.23 13.46 NM HCG199_SK500_MT61_DH406 GCS9 Cl* Melotte 22 HCG 199 +03 45 53.20 +25 12 55.8 0 17.551 16.776 16.002 15.368 14.932 14.952 -4.73 2.27 -12.06 2.27 5.27 NM BPL81_L07_A1_37 GCS9 Cl* Melotte 22 BPL 81 +03 46 02.52 +23 45 33.2 0 18.177 17.368 16.459 15.481 15.025 15.026 -1.56 2.31 1.54 2.31 6.68 NM L07_A1_39 GCS9 +03 46 03.75 +23 44 35.6 0 18.178 17.270 16.413 15.556 15.053 15.070 -10.12 2.30 -4.53 2.30 21.32 NM L07_A1_40 GCS9 +03 46 04.92 +22 15 40.2 0 17.472 16.679 15.925 15.296 15.057 15.166 0.19 2.39 -8.27 2.39 2.60 NM L07_photNM_7 GCS9 +03 46 08.02 +23 45 35.5 0 18.882 17.941 16.854 15.857 15.336 15.348 -12.84 2.37 2.06 2.37 2.22 NM L07_A1_41 GCS9 +03 46 08.22 +23 21 38.7 0 17.105 16.500 15.752 15.026 14.646 14.651 2.05 2.28 -10.76 2.28 3.97 NM L07_PM_NM_16 GCS9 +03 46 08.37 +23 20 50.8 0 11.765 11.589 11.200 11.417 10.482 10.755 22.70 2.23 -33.02 2.23 6.52 NM HII915_HCG215 GCS9 HII915 +03 46 14.34 +23 51 02.4 0 15.627 14.918 14.181 13.826 13.458 13.484 1.83 2.22 13.02 2.22 301.52 NM HHJ56_DH432 GCS9 Cl* Melotte 22 HHJ 56 +03 46 14.48 +22 20 51.0 0 16.717 16.105 15.473 15.002 14.615 0.011 66.01 2.32 -69.67 2.32 3.61 NM int-pl-IZ-63_2MASSJ0346144+222051_N_L07_PM_NM_17 GCS9 Cl* Melotte 22 IPL 63 +03 46 26.75 +24 49 18.1 0 16.284 15.730 15.129 14.552 14.213 14.224 -2.42 2.22 -18.50 2.22 17.98 NM L07_PM_NM_18 GCS9 +03 46 32.99 +23 38 00.9 0 16.598 16.068 15.371 14.454 14.143 14.105 4.67 2.24 2.45 2.24 3.95 NM L07_PM_NM_19 GCS9 +03 46 36.82 +23 33 01.8 0 16.694 16.058 15.333 14.498 14.128 14.125 -2.77 2.24 -15.45 2.24 1.91 NM L07_PM_NM_21 GCS9 +03 46 38.36 +25 33 18.6 0 15.621 15.312 14.788 14.129 13.931 13.905 19.87 2.24 -46.62 2.24 9.86 NM L07_33 GCS9 +03 46 40.95 +22 22 38.1 0 19.242 18.150 16.917 16.183 15.497 15.480 69.74 2.66 -50.74 2.66 3.71 NM L07_A1_52 GCS9 +03 46 44.04 +23 38 13.4 0 17.173 16.531 15.775 14.937 14.576 14.555 6.12 2.26 -11.45 2.26 9.02 NM L07_PM_NM_22 GCS9 +03 46 48.31 +24 18 06.2 0 13.266 13.009 12.529 11.953 11.752 11.731 -12.75 2.22 2.02 2.22 6.44 NM HCG236_J311 GCS9 Cl* Melotte 22 HCG 236 +03 46 50.99 +25 40 44.5 0 16.272 15.767 15.181 14.488 14.196 14.200 -5.63 2.26 -22.81 2.26 3.53 NM L07_PM_NM_23 GCS9 +03 46 52.58 +24 17 17.0 0 15.299 14.842 14.265 13.643 13.345 13.340 20.07 2.22 1.29 2.22 10.34 NM BPL111 GCS9 Cl* Melotte 22 BPL 111 +03 47 02.12 +25 54 36.6 0 13.774 13.591 13.184 12.619 12.512 12.525 10.77 2.23 -49.85 2.23 2.44 NM L07_4 GCS9 +03 47 02.54 +25 13 45.5 0 16.469 16.185 15.655 14.946 14.723 14.728 4.07 2.25 -9.76 2.25 1.92 NM BPL123 GCS9 Cl* Melotte 22 BPL 123 +03 47 03.56 +24 49 11.6 0 13.153 13.091 11.335 12.348 10.073 NM HII1266_HD23567_Tr359b GCS9 HII1266 +03 47 05.82 +23 24 52.5 0 19.481 19.105 18.389 17.632 17.262 17.393 -2.31 4.89 5.06 4.89 3.79 NM Roque32 GCS9 Cl* Melotte 22 Roque 32 +03 47 06.65 +24 45 47.4 0 16.069 15.513 14.959 14.428 14.100 14.105 59.57 2.23 27.45 2.23 12.46 NM L07_PM_NM_25 GCS9 +03 47 07.73 +24 21 40.5 0 15.473 15.016 14.402 13.829 13.534 13.532 -25.04 2.22 -2.14 2.22 11.43 NM JS9 GCS9 Cl* Melotte 22 JS 9 +03 47 08.31 +22 33 10.0 0 16.577 15.993 15.342 14.844 14.453 0.010 -15.97 2.31 -6.87 2.31 5.75 NM int-pl-IZ-38;2MASSJ0347082+223309_BPL127_Y_L07_PM_NM_26 GCS9 Cl* Melotte 22 IPL 38 +03 47 13.69 +23 46 28.4 0 16.410 15.813 15.184 14.658 14.309 14.311 -22.45 2.24 11.13 2.24 31.03 NM L07_PM_NM_27 GCS9 +03 47 24.41 +23 54 52.7 0 13.573 13.598 11.481 12.458 10.127 10.989 -61.87 2.22 -1.72 2.22 12.56 NM HII1397_HD23631_Tr402 GCS9 HII1397 +03 47 30.66 +25 13 30.6 0 16.074 15.584 14.998 14.473 14.158 14.126 48.31 2.22 -52.27 2.22 3.66 NM BPL146 GCS9 Cl* Melotte 22 BPL 146 +03 47 36.27 +24 28 50.1 0 16.052 15.566 14.943 14.294 13.957 13.957 -9.42 2.22 -2.90 2.22 3.25 NM L07_PM_NM_28 GCS9 +03 47 37.55 +24 28 59.0 0 19.855 19.463 18.773 18.225 18.131 17.930 -4.03 5.91 20.75 5.91 1.09 NM Roque34 GCS9 Cl* Melotte 22 Roque 34 +03 47 38.75 +22 38 40.3 0 17.442 16.864 16.220 15.689 15.285 15.282 -23.29 2.39 -31.80 2.39 4.85 NM Roque44 GCS9 Cl* Melotte 22 Roque 44 +03 47 41.19 +23 44 24.9 0 11.990 11.816 11.404 11.437 10.714 11.008 21.37 2.22 -39.05 2.22 12.59 NM HII1532_HCG286_SK405_B270_DH531 GCS9 HII1532 +03 47 46.16 +25 21 42.1 0 19.442 18.364 17.367 16.890 16.342 16.383 69.70 3.46 -102.93 3.46 5.96 NM L07_photNM_8 GCS9 +03 47 46.90 +24 03 40.5 0 20.021 20.270 19.206 18.506 18.007 18.106 -3.67 5.35 0.47 5.35 4.33 NM Roque27 GCS9 Cl* Melotte 22 Roque 27 +03 48 02.09 +24 00 02.5 0 18.921 18.465 17.869 17.284 16.982 17.006 1.27 3.24 -5.14 3.24 1.15 NM Roque10 GCS9 Cl* Melotte 22 Roque 10 +03 48 03.68 +23 44 10.4 0 15.989 15.099 14.329 13.709 13.277 13.278 32.62 2.22 -124.76 2.22 0.68 NM NOT1 GCS9 Cl* Melotte 22 NOT 1 +03 48 04.74 +23 51 01.8 0 20.362 19.516 19.084 18.521 17.969 18.210 11.09 6.08 5.08 6.08 1.36 NM Festin98_009 GCS9 Cl* Melotte 22 NPL 009 +03 48 13.80 +24 28 04.0 0 17.517 16.753 16.083 15.485 15.072 15.099 1.43 2.31 -3.33 2.31 2.70 NM Roque43 GCS9 Cl* Melotte 22 Roque 43 +03 48 13.93 +24 38 30.5 0 16.849 16.681 16.451 16.076 15.939 15.998 -2.88 2.65 1.72 2.65 5.01 NM BPL168 GCS9 Cl* Melotte 22 BPL 168 +03 48 23.62 +24 22 35.2 0 16.199 15.558 14.889 14.317 13.945 13.947 -10.86 2.23 -14.81 2.23 2.24 NM BPL177_Festin98_004_L07_PM_NM_29 GCS9 Cl* Melotte 22 BPL 177 +03 48 25.61 +22 52 13.0 0 15.518 15.386 15.395 14.708 14.646 14.641 11.27 2.17 -37.83 2.17 450.04 NM BPL179 GCS9 Cl* Melotte 22 BPL 179 +03 48 30.10 +24 20 43.9 0 14.608 14.578 12.604 14.861 11.050 12.051 -94.69 2.22 157.47 2.22 6.88 NM HII1876_HD23763_Tr518 GCS9 HII1876 +03 48 32.39 +24 13 18.4 0 20.066 19.095 18.425 17.638 17.303 17.351 2.98 3.77 -23.67 3.77 2.76 NM Festin98_010 GCS9 Cl* Melotte 22 NPL 010 +03 48 32.67 +23 52 40.6 0 14.722 14.278 13.711 13.109 12.832 12.842 0.96 2.22 -5.44 2.22 5.32 NM WILL1 GCS9 Cl* Melotte 22 WBM 1 +03 48 36.30 +23 33 25.3 0 16.077 15.589 15.028 14.489 14.152 14.167 -14.94 2.23 -33.20 2.23 1.87 NM L07_PM_NM_30 GCS9 +03 48 44.14 +24 21 18.7 0 17.816 17.195 16.443 15.823 15.448 15.431 2.52 2.33 -8.88 2.33 4.62 NM Roque37 GCS9 Cl* Melotte 22 Roque 37 +03 48 48.45 +21 59 00.3 0 16.559 15.982 15.348 14.816 14.470 14.458 -16.99 2.31 5.11 2.31 5.45 NM L07_PM_NM_31 GCS9 +03 48 49.03 +24 20 25.3 0 19.222 18.227 17.237 16.569 16.059 16.074 -33.18 2.59 -58.05 2.59 4.06 NM Roque33_Festin98_008_L07_photNM_9 GCS9 Cl* Melotte 22 Roque 33 +03 48 55.23 +22 50 42.2 0 14.915 14.474 13.893 13.361 13.070 13.047 4.68 2.13 -22.37 2.13 1.12 NM BPL194 GCS9 Cl* Melotte 22 BPL 194 +03 48 58.61 +23 37 03.9 0 19.604 18.306 17.390 16.771 16.233 16.221 6.50 2.60 -15.01 2.60 4.76 NM L07_photNM_10 GCS9 +03 48 58.96 +25 06 14.0 0 11.958 11.762 11.365 11.410 10.602 10.877 7.12 2.20 -45.83 2.20 1.85 NM HII2082 GCS9 HII2082 +03 49 11.59 +24 26 17.4 0 16.051 15.500 14.910 14.436 14.081 14.097 33.46 2.21 -90.64 2.21 2.53 NM L07_PM_NM_32 GCS9 +03 49 12.12 +23 12 55.9 0 16.875 16.165 15.429 14.824 14.414 14.401 53.85 2.28 -0.69 2.28 4.85 NM L07_PM_NM_33 GCS9 +03 49 33.05 +26 50 43.0 0 16.972 16.327 15.617 15.078 14.687 14.684 28.54 2.99 9.02 2.99 0.42 NM IPMBD22 GCS9 Cl* Melotte 22 IPMBD 22 +03 49 45.95 +22 53 43.7 0 15.830 15.510 15.011 14.332 14.144 14.153 13.11 2.27 -37.98 2.27 1.52 NM L07_186 GCS9 +03 49 51.23 +25 26 06.6 0 19.731 18.569 17.609 17.074 16.515 16.545 2.12 3.45 13.47 3.45 1.53 NM L07_photNM_13 GCS9 +03 49 53.76 +23 59 01.4 0 17.271 16.739 16.088 15.537 15.167 15.153 15.94 2.29 -9.47 2.29 31.41 NM Roque46 GCS9 Cl* Melotte 22 Roque 46 +03 50 00.31 +24 28 15.5 0 18.288 17.707 17.006 16.494 16.087 16.072 2.01 2.60 -7.24 2.60 4.36 NM Roque40 GCS9 Cl* Melotte 22 Roque 40 +03 50 01.67 +24 36 48.6 0 15.586 15.002 14.433 13.921 13.543 13.549 5.46 2.21 -6.13 2.21 12.12 NM BPL221 GCS9 Cl* Melotte 22 BPL 221 +03 50 03.90 +23 58 34.1 0 14.131 13.765 13.259 12.738 12.457 12.446 -8.17 2.22 -11.58 2.22 19.96 NM HHJ256 GCS9 Cl* Melotte 22 HHJ 256 +03 50 05.96 +23 42 14.1 0 19.805 19.357 18.931 18.260 17.904 17.940 -0.52 5.77 8.42 5.77 5.36 NM Roque29 GCS9 Cl* Melotte 22 Roque 29 +03 50 15.55 +26 06 30.0 0 16.909 16.185 15.415 14.832 14.403 14.396 40.58 2.28 -66.43 2.28 2.93 NM L07_PM_NM_34 GCS9 +03 50 20.77 +24 08 40.9 0 19.627 19.216 18.782 18.095 17.744 17.820 -0.38 5.11 4.55 5.11 1.62 NM Roque28 GCS9 Cl* Melotte 22 Roque 28 +03 50 41.10 +25 44 21.6 0 15.634 15.517 15.188 14.817 14.704 14.716 -8.19 2.27 -10.74 2.27 17.17 NM L07_faintJK_1 GCS9 +03 51 04.05 +24 32 57.8 0 16.030 15.535 14.962 14.396 14.049 14.060 76.40 2.22 -22.07 2.22 9.89 NM L07_PM_NM_35 GCS9 +03 51 05.58 +24 44 12.2 0 11.945 11.736 11.358 11.489 10.568 10.909 20.38 2.20 -47.92 2.20 5.38 NM HII2927_HCG418_DH693 GCS9 HII2927 +03 51 09.72 +25 18 52.4 0 16.714 16.037 15.393 14.875 14.493 14.534 -18.86 2.28 -30.02 2.28 14.71 NM L07_PM_NM_52 GCS9 +03 51 10.52 +22 48 14.4 0 16.768 16.141 15.471 14.799 14.434 14.413 -4.75 2.28 -21.83 2.28 1.24 NM L07_PM_NM_36 GCS9 +03 51 22.47 +23 42 19.2 0 13.909 13.760 13.410 12.975 12.869 12.863 2.24 2.22 -6.49 2.22 1.31 NM BPL234 GCS9 Cl* Melotte 22 BPL 234 +03 51 27.90 +22 48 12.9 0 16.113 15.431 14.734 14.118 13.705 13.722 11.13 2.26 -12.61 2.26 4.23 NM L07_PM_NM_37 GCS9 +03 51 35.03 +24 03 34.8 0 16.774 16.625 16.231 15.758 15.586 15.587 -6.66 2.32 -6.66 2.32 40.44 NM L07_faintJK_4 GCS9 +03 51 37.72 +25 42 46.5 0 19.137 17.784 16.352 15.461 14.660 14.657 55.25 2.37 -38.67 2.37 4.06 NM L07_photNM_14 GCS9 +03 51 38.18 +23 03 11.2 0 16.892 16.218 15.549 14.978 14.588 14.593 -5.14 2.31 40.41 2.31 5.15 NM L07_PM_NM_38 GCS9 +03 51 53.92 +24 02 51.4 0 13.571 13.114 13.213 12.035 11.680 11.721 -34.06 2.22 -22.59 2.22 165.02 NM HCG441_SK211_BPL243_DH726_Moraux2003_17 GCS9 Cl* Melotte 22 HCG 441 +03 51 57.12 +24 57 06.3 0 16.193 15.875 15.320 14.797 14.498 14.480 15.09 2.23 -31.04 2.23 6.23 NM DH729 GCS9 Cl* Melotte 22 DH 729 +03 52 01.58 +24 20 11.7 0 16.260 15.770 15.166 14.508 14.197 14.223 4.89 2.26 -3.61 2.26 3.40 NM BPL247 GCS9 Cl* Melotte 22 BPL 247 +03 52 07.88 +23 59 13.0 0 16.169 15.371 14.636 14.066 13.640 14.066 18.40 2.25 -115.67 2.25 1.33 NM CFHT-Pl-6_BRB3_L07_PM_NM_39 GCS9 Cl* Melotte 22 CFHT 6 +03 52 13.44 +24 28 52.3 0 19.996 18.601 17.754 17.227 16.636 16.681 7.13 3.47 -14.21 3.47 2.49 NM L07_photNM_16 GCS9 +03 52 17.49 +22 51 01.9 0 16.649 16.084 15.415 14.781 14.414 14.402 4.92 2.30 -6.97 2.30 1.46 NM L07_PM_NM_40 GCS9 +03 52 24.00 +23 55 15.7 0 14.588 14.438 14.048 13.571 13.477 13.478 14.07 2.25 -36.84 2.25 1.05 NM DH2004_751 GCS9 Cl* Melotte 22 DH 751 +03 52 44.31 +24 14 13.2 0 13.520 13.343 12.946 12.457 12.359 12.354 19.94 2.24 -11.03 2.24 7.05 NM L07_8 GCS9 +03 52 46.44 +24 24 17.1 0 19.772 18.591 17.569 16.949 16.281 16.359 -3.07 2.92 -13.02 2.92 18.30 NM L07_photNM_17 GCS9 +03 52 47.18 +22 38 44.1 0 17.260 16.818 16.156 15.566 15.252 15.282 -40.26 2.44 15.01 2.44 273.74 NM L07_faintJK_10 GCS9 +03 52 49.69 +25 00 50.7 0 13.101 12.909 12.566 12.095 11.969 11.985 -1.51 2.23 -0.95 2.23 2.51 NM BPL271 GCS9 Cl* Melotte 22 BPL 271 +03 52 52.11 +25 10 26.0 0 13.187 12.804 12.306 11.803 11.485 11.517 43.14 2.23 -67.75 2.23 5.68 NM BPL273_SK152 GCS9 Cl* Melotte 22 BPL 273 +03 52 58.25 +23 26 19.1 0 13.562 13.427 13.136 12.789 12.721 12.722 7.00 2.25 1.17 2.25 5.43 NM BPL276 GCS9 Cl* Melotte 22 BPL 276 +03 53 21.62 +23 58 53.9 0 13.493 13.135 12.644 12.051 11.822 47.53 2.30 -56.56 2.30 1.90 NM SK132_DH782 GCS9 Cl* Melotte 22 SK 132 +03 53 23.13 +23 19 20.4 0 17.823 16.897 15.966 15.341 14.850 14.834 21.87 2.36 -4.56 2.36 1.54 NM BPL283_CFHT-Pl-18_M8_L07_A1_104 GCS9 Cl* Melotte 22 BPL 283 +03 53 23.37 +25 05 50.0 0 16.629 16.085 15.495 15.014 14.656 14.648 43.26 2.28 -95.99 2.28 1.97 NM BPL284 GCS9 Cl* Melotte 22 BPL 284 +03 53 34.55 +24 04 38.4 0 16.740 16.177 15.552 14.979 14.606 14.22 2.35 9.30 2.35 1.46 NM BPL287 GCS9 Cl* Melotte 22 BPL 287 +03 53 48.72 +25 04 20.1 0 16.777 16.308 15.238 15.126 14.827 14.820 -6.07 2.28 16.38 2.28 119.28 NM L07_PM_NM_41 GCS9 +03 53 59.92 +23 41 00.2 0 16.200 15.677 15.073 14.485 14.141 14.149 -0.43 2.26 -8.06 2.26 1.04 NM BPL296_L07_PM_NM_42 GCS9 Cl* Melotte 22 BPL 296 +03 54 01.40 +24 44 46.7 0 19.768 18.534 17.775 17.194 16.809 17.194 3.91 3.31 -23.74 3.31 1.84 NM CFHT-Pl-26_XX GCS9 Cl* Melotte 22 CFHT 26 +03 54 15.64 +25 21 18.9 0 15.714 15.104 14.458 13.881 13.530 13.501 1.39 2.95 -13.24 2.95 0.52 NM BPL309 GCS9 Cl* Melotte 22 BPL 309 +03 54 17.46 +23 11 56.9 0 16.231 15.688 15.035 14.453 14.100 14.085 -12.92 2.28 -3.82 2.28 10.54 NM L07_PM_NM_43 GCS9 +03 54 39.33 +23 03 12.4 0 16.447 15.693 14.998 14.476 14.064 14.083 6.47 2.28 -4.39 2.28 6.56 NM L07_PM_NM_44 GCS9 +03 54 44.20 +25 15 10.9 0 17.428 16.693 15.954 15.421 15.043 15.031 -6.19 2.31 -33.42 2.31 4.31 NM BPL316 GCS9 Cl* Melotte 22 BPL 316 +03 54 45.87 +22 53 54.8 0 17.409 16.961 16.437 15.849 15.518 15.619 17.05 2.56 -18.44 2.56 4.02 NM DH807 GCS9 Cl* Melotte 22 DH 807 +03 54 46.11 +23 00 20.6 0 17.056 16.404 15.700 15.125 14.741 14.732 -17.41 2.33 -4.76 2.33 7.01 NM L07_PM_NM_46 GCS9 +03 54 49.71 +25 06 24.2 0 13.354 13.219 12.850 12.393 12.309 12.312 -1.04 2.23 -7.78 2.23 6.31 NM BPL319 GCS9 Cl* Melotte 22 BPL 319 +03 54 51.47 +23 45 12.2 0 19.670 18.540 17.687 17.122 16.691 16.585 56.85 3.56 -24.59 3.56 2.56 NM BRB19_None GCS9 Cl* Melotte 22 BRB 19 +03 54 54.51 +22 50 21.5 0 16.464 15.871 15.230 14.673 14.333 14.338 2.56 2.30 -7.22 2.30 4.32 NM L07_PM_NM_47 GCS9 +03 54 55.74 +25 09 04.2 0 14.981 14.562 14.004 13.425 13.130 13.141 17.22 2.23 -9.40 2.23 12.41 NM BPL320 GCS9 Cl* Melotte 22 BPL 320 +03 54 55.92 +24 37 42.9 0 19.397 19.014 18.467 17.790 17.603 17.582 6.11 5.79 -3.45 5.79 6.15 NM L07_photNM_18 GCS9 +03 55 03.43 +24 28 54.6 0 19.678 18.842 18.141 17.524 17.349 17.331 3.23 4.67 -16.09 4.67 6.30 NM L07_257 GCS9 +03 55 06.14 +25 11 06.2 0 15.549 15.000 14.339 13.790 13.431 13.470 0.47 2.24 -5.03 2.24 4.70 NM BPL323 GCS9 Cl* Melotte 22 BPL 323 +03 55 08.19 +23 58 08.6 0 19.348 18.984 18.417 17.822 17.551 -0.94 4.78 -14.24 4.78 2.60 NM L07_faintYJ_30 GCS9 +03 55 12.61 +23 17 37.2 0 17.842 16.877 15.977 15.348 14.842 15.348 54.40 2.33 -48.46 2.33 8.41 NM CFHT-Pl-15_M7.0_BRB13_L07_A1_109 GCS9 Cl* Melotte 22 CFHT 15 +03 55 14.72 +22 42 08.5 0 16.652 16.043 15.366 14.734 14.395 14.359 1.47 2.29 -7.33 2.29 9.73 NM L07_PM_NM_53 GCS9 +03 55 18.11 +24 17 05.7 0 16.325 15.757 15.111 14.541 14.192 14.208 15.89 2.26 -2.19 2.26 10.94 NM BPL326_L07_A1_110 GCS9 Cl* Melotte 22 BPL 326 +03 55 19.92 +24 12 13.9 0 15.750 15.298 14.699 14.128 13.819 13.821 -7.38 2.25 -20.24 2.25 5.61 NM L07_106 GCS9 +03 55 30.07 +23 54 53.5 0 16.503 15.930 15.308 14.808 14.429 14.420 -15.28 2.27 -4.94 2.27 2.49 NM L07_PM_NM_48 GCS9 +03 55 39.59 +24 12 50.7 0 20.511 19.251 17.844 17.303 16.618 16.648 28.07 3.37 -158.64 3.37 1.11 NM L07_photNM_19 GCS9 +03 55 42.02 +22 57 01.4 0 18.646 17.343 15.943 14.999 14.186 14.191 161.10 2.34 -44.49 2.34 2.38 NM L07_photNM_20 GCS9 +03 55 45.49 +23 51 25.4 0 20.033 19.742 18.723 18.580 18.088 17.959 -5.63 8.56 -13.56 8.56 1.63 NM L07_faintJK_5 GCS9 +03 55 46.37 +23 21 16.0 0 13.188 12.908 12.386 11.820 11.587 11.619 18.86 2.25 -3.24 2.25 4.59 NM SK40 GCS9 Cl* Melotte 22 SK 40 +03 56 21.72 +25 21 10.4 0 16.646 15.945 15.246 14.665 14.234 14.261 4.82 2.53 -4.34 2.53 1.00 NM BPL336 GCS9 Cl* Melotte 22 BPL 336 +03 56 34.22 +24 15 13.2 0 17.385 16.926 16.372 15.806 15.535 15.527 12.43 3.15 -41.24 3.15 0.86 NM DH838 GCS9 Cl* Melotte 22 DH 838 +03 56 36.20 +25 18 05.7 0 16.008 15.372 14.712 14.140 13.761 13.809 8.47 2.50 -13.23 2.50 0.59 NM BPL337 GCS9 Cl* Melotte 22 BPL 337 +03 56 51.33 +25 02 25.4 0 13.297 12.918 12.362 11.804 11.466 11.541 28.86 2.47 0.62 2.47 0.95 NM BPL339 GCS9 Cl* Melotte 22 BPL 339 +03 56 51.90 +23 31 36.6 0 18.325 17.973 17.367 16.753 16.502 16.419 -3.97 3.81 -6.61 3.81 1.07 NM DH2004_844 GCS9 Cl* Melotte 22 DH 844 +03 57 58.45 +24 20 08.9 0 17.341 16.910 16.301 15.694 15.391 15.376 -2.76 3.11 -15.77 3.11 0.84 NM DH861 GCS9 Cl* Melotte 22 DH 861 +03 58 56.42 +24 18 32.2 0 14.148 13.730 13.145 12.585 12.404 12.406 61.58 2.96 -48.24 2.96 45.45 NM HHJ292_DH869 GCS9 Cl* Melotte 22 HHJ 292 +04 00 19.40 +24 41 05.0 0 18.236 17.805 17.236 16.704 16.312 16.366 2.20 3.49 -13.79 3.49 1.46 NM DH2004_890 GCS9 Cl* Melotte 22 DH 890 +03 32 33.72 +24 29 34.7 0 13.066 12.465 12.179 13.40 6.88 -33.70 6.88 4.83 noZY DH021 GCS9 Cl* Melotte 22 DH 021 +03 32 42.31 +23 34 00.0 0 13.393 12.828 12.539 12.522 50.10 2.64 -42.29 2.64 98.04 noZY DH022 GCS9 Cl* Melotte 22 DH 022 +03 34 22.13 +27 23 45.3 0 12.594 12.061 11.758 11.766 19.10 4.62 -39.68 4.62 1.84 noZY DH035 GCS9 Cl* Melotte 22 DH 035 +03 34 46.37 +27 55 32.4 0 12.506 11.899 11.620 11.663 17.73 4.62 -33.95 4.62 0.60 noZY DH038 GCS9 Cl* Melotte 22 DH 038 +03 43 59.09 +22 54 29.0 0 19.298 18.365 18.018 22.78 9.41 4.51 9.41 3.41 noZY int-pl-IZ-56;IPLJ0343590+225429_N GCS9 Cl* Melotte 22 IPL 56 +03 44 19.50 +22 39 03.5 0 18.709 18.333 17.679 17.927 28.27 8.54 -3.53 8.54 9.76 noZY L07_faintJK_8 GCS9 +03 44 27.29 +25 44 41.7 0 18.818 17.787 16.965 16.902 8.52 5.25 -26.68 5.25 1.95 noZY BRB27_CFHT-PLIZ1262_PLZJ32 GCS9 Cl* Melotte 22 BRB 27 +03 45 35.26 +23 36 39.6 0 18.891 18.394 18.140 18.489 25.34 8.42 14.07 8.42 6.64 noZY L07_faintJK_7 GCS9 +03 47 05.68 +20 00 37.0 0 14.107 13.553 13.218 13.230 21.25 2.66 -50.47 2.66 2.67 noZY DH492 GCS9 Cl* Melotte 22 DH 492 +03 47 51.19 +25 26 57.9 0 18.212 17.780 17.630 9.00 noZY L07_faintJK_12 GCS9 +03 48 11.42 +20 46 42.4 0 13.091 12.550 12.217 12.230 18.39 2.65 -43.76 2.65 4.29 noZY DH558 GCS9 Cl* Melotte 22 DH 558 +03 48 32.69 +25 06 05.1 0 18.548 17.996 17.438 17.609 0.60 5.00 3.81 5.00 2.60 noZY L07_faintJK_2 GCS9 +03 48 55.41 +24 20 09.6 0 19.996 19.199 18.367 18.093 -21.91 11.56 22.88 11.56 0.29 noY Roque19 GCS9 Cl* Melotte 22 Roque 19 +03 49 09.15 +24 19 59.9 0 16.407 15.807 15.534 15.463 16.82 2.45 -9.31 2.45 0.38 noZY 18.37 GCS9 Cl* Melotte 22 MHOBD 2 +03 49 11.04 +24 20 51.1 0 12.979 12.444 12.146 12.154 16.62 2.33 -46.70 2.33 1.45 noZY HCG355_HHJ287_BPL199_DH604 GCS9 Cl* Melotte 22 HCG 355 +03 49 11.42 +24 14 24.4 0 13.693 13.150 12.839 12.818 20.72 2.34 -43.10 2.34 1.94 noZY BPL200_DH606 GCS9 Cl* Melotte 22 BPL 200 +03 49 12.20 +23 53 12.2 0 11.727 14.085 10.383 11.580 5.42 2.33 -47.80 2.33 40.02 noZY HII2195_HD23863_Tr607 GCS9 HII2195 +03 49 12.52 +24 11 12.8 0 16.410 15.750 15.245 15.233 14.85 2.44 -40.77 2.44 1.58 noZY BPL201_L07_A1_84 GCS9 Cl* Melotte 22 BPL 201 +03 49 16.79 +24 23 45.7 0 11.577 12.188 10.276 10.839 -151.15 2.33 -101.06 2.33 28.91 noZY HII2220_HD23872_Tr613 GCS9 HII2220 +03 49 18.66 +23 46 48.8 0 14.294 13.665 13.393 13.394 15.38 2.34 -44.61 2.34 0.64 noZY DH2004_610 GCS9 Cl* Melotte 22 DH 610 +03 49 21.76 +24 22 51.2 0 12.746 17.742 11.121 25.15 2.33 -49.52 2.33 24.37 noZY HII2263_HD23873_Tr622 GCS9 HII2263 +03 49 32.54 +23 55 42.4 0 13.238 12.702 12.389 12.412 14.17 2.33 -43.14 2.33 0.80 noZY HCG371_SK310_HHJ221_DH627 GCS9 Cl* Melotte 22 HCG 371 +03 49 33.12 +24 13 04.6 0 14.583 14.051 13.701 13.694 19.96 2.34 -41.58 2.34 2.40 noZY BPL210 GCS9 Cl* Melotte 22 BPL 210 +03 49 36.12 +23 56 23.1 0 13.112 12.569 12.282 12.268 16.38 2.33 -43.47 2.33 1.74 noZY HCG373_SK307_HHJ286_DH632 GCS9 Cl* Melotte 22 HCG 373 +03 49 36.53 +24 18 14.0 0 13.243 12.679 12.382 12.419 13.89 2.34 -45.09 2.34 6.76 noZY HCG375_HHJ250_BPL212_DH634 GCS9 Cl* Melotte 22 HCG 375 +03 49 56.60 +24 20 56.3 0 11.532 12.226 10.215 11.260 27.98 2.33 -35.54 2.33 12.96 noZY HII2488_HD23948_Tr688 GCS9 HII2488 +03 50 58.17 +23 55 42.5 0 13.574 13.042 12.728 12.708 21.42 2.34 -47.56 2.34 23.03 noZY HCG414_DH690 GCS9 Cl* Melotte 22 HCG 414 +03 51 11.54 +24 23 13.0 0 12.113 11.631 11.289 11.323 13.08 2.33 -44.19 2.33 0.66 noZY HCG422_SK245_DH697_Moraux2003_1 GCS9 Cl* Melotte 22 HCG 422 +03 51 12.08 +23 55 57.2 0 11.712 11.710 10.948 11.183 25.68 2.33 -44.30 2.33 3.63 noZY HII2966_SK244_DH698 GCS9 HII2966 +03 51 18.22 +24 20 26.0 0 13.809 13.281 13.036 13.013 48.24 2.34 -48.37 2.34 4.66 noZY SK236 GCS9 Cl* Melotte 22 SK 236 +03 51 20.13 +23 45 17.9 0 19.366 18.614 17.836 18.128 -20.62 7.01 -49.89 7.01 3.15 noZY BRB33_None GCS9 Cl* Melotte 22 BRB 33 +03 51 21.34 +23 52 08.5 0 14.111 13.774 13.735 13.701 -2.74 2.34 -4.49 2.34 5.70 noZY Calar7_XX.X GCS9 Cl* Melotte 22 CALAR 7 +03 51 25.35 +23 53 21.5 0 11.431 11.542 10.779 11.060 19.95 2.33 -43.33 2.33 6.34 noZY HCG430_SK229_DH711_BPL235_CFHT-Pl-21_M8_BRB14_CFHT-Pl-21 GCS9 Cl* Melotte 22 HCG 430 +03 51 25.98 +24 15 29.9 0 19.310 18.627 17.965 17.950 16.58 6.80 -16.12 6.80 2.25 noZY BRB31_None GCS9 Cl* Melotte 22 BRB 31 +03 51 29.95 +23 53 57.0 0 11.172 11.359 10.547 10.916 18.96 2.33 -38.73 2.33 7.81 noZY HII3063_Tr862_HCG431_DH713 GCS9 HII3063 +03 51 34.26 +23 47 49.8 0 13.874 13.299 13.025 12.997 16.21 2.34 -45.54 2.34 0.73 noZY BPL236_DH716_Moraux2003_71 GCS9 Cl* Melotte 22 BPL 236 +03 51 39.20 +23 51 18.1 0 13.736 13.171 12.836 12.848 13.98 2.34 -44.51 2.34 8.17 noZY BPL238_DH720 GCS9 Cl* Melotte 22 BPL 238 +03 51 42.32 +24 21 41.3 0 13.568 13.042 12.728 12.744 19.28 2.34 -41.03 2.34 3.74 noZY BPL239_DH723_Moraux2003_53 GCS9 Cl* Melotte 22 BPL 239 +03 52 54.91 +24 37 18.1 0 18.803 17.754 16.926 16.989 12.05 3.86 -46.85 3.86 2.29 noZY PLZJ37_BRB28_L07_faintJK_11 GCS9 Cl* Melotte 22 PlZJ 37 +03 54 01.44 +23 49 57.5 0 18.686 17.711 16.999 16.927 35.53 4.84 -33.95 4.84 3.15 noZY BRB29_L4.5 GCS9 Cl* Melotte 22 BRB 29 +03 54 13.41 +23 32 22.2 0 18.655 18.120 18.105 18.015 11.41 7.09 -15.53 7.09 2.97 noZY L07_faintJK_6 GCS9 +03 55 57.94 +24 41 41.6 0 18.859 18.183 18.214 17.900 -8.05 7.61 14.92 7.61 1.53 noZY L07_faintJK_3 GCS9 +03 35 18.76 +23 26 20.7 0 15.450 14.829 14.310 13.909 13.942 15.05 3.12 -38.99 3.12 1.24 noZ DH045 GCS9 Cl* Melotte 22 DH 045 +03 36 38.40 +23 08 44.2 0 14.764 14.165 13.620 13.288 13.320 25.49 3.11 -39.56 3.11 0.46 noZ DH060 GCS9 Cl* Melotte 22 DH 060 +03 42 01.68 +23 58 22.4 0 19.951 18.621 17.888 17.153 5.67 5.52 3.25 5.52 6.41 noZ int-pl-IZ-14;IPLJ0342016+235823_N GCS9 Cl* Melotte 22 IPL 14 +03 42 14.28 +22 43 02.5 0 20.062 18.517 18.045 17.495 17.568 -21.44 5.77 0.43 5.77 2.76 noZ L07_faintYJ_1 GCS9 +03 42 59.31 +25 37 38.9 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 6.35 noZ L07_faintYJ_2 GCS9 +03 43 21.40 +24 34 42.0 0 20.092 18.782 18.309 17.574 -29.69 9.19 -11.53 9.19 0.28 noZ Roque24 GCS9 Cl* Melotte 22 Roque 24 +03 43 25.89 +24 00 51.8 0 20.716 19.423 18.592 18.203 -16.04 8.54 2.59 8.54 1.10 noZ int-pl-IZ-23;IPLJ0343258+240051_N GCS9 Cl* Melotte 22 IPL 23 +03 43 26.65 +23 41 38.8 0 20.084 19.258 18.586 17.889 0.50 8.68 -12.21 8.68 1.87 noZ int-pl-IZ-4;IPLJ0343266+234139_N GCS9 Cl* Melotte 22 IPL 4 +03 43 49.93 +24 03 37.6 0 20.056 18.915 18.060 17.609 10.66 6.29 -12.02 6.29 7.85 noZ int-pl-IZ-17;IPLJ0343498+240337_N GCS9 Cl* Melotte 22 IPL 17 +03 44 00.27 +24 33 25.1 0 12.731 11.579 13.069 11.261 -135.16 3.07 159.00 3.07 1.29 noZ HII232_HD23194_Tr074 GCS9 HII232 +03 44 30.52 +24 21 17.4 0 19.589 18.397 17.705 17.310 17.563 9.90 4.99 2.04 4.99 5.70 noZ L07_faintYJ_3 GCS9 +03 44 31.28 +25 35 14.7 0 19.426 18.269 17.398 16.675 16.604 10.61 3.73 -39.40 3.73 1.93 noZ BRB22_CFHT-PLIZ2141_PLZJ61_L07_faintYJ_4 GCS9 Cl* Melotte 22 BRB 22 +03 44 47.33 +24 21 35.7 0 19.688 18.409 17.506 16.906 16.979 35.23 3.66 -31.91 3.66 3.07 noZ L07_faintYJ_5 GCS9 +03 44 49.73 +22 51 10.6 0 19.950 19.044 18.679 18.196 57.62 9.24 -32.33 9.24 2.37 noZ int-pl-IZ-51;IPLJ0344496+225111_N GCS9 Cl* Melotte 22 IPL 51 +03 44 51.70 +23 02 30.4 0 19.970 19.021 18.836 18.867 -3.01 8.34 -2.50 8.34 2.83 noZ int-pl-IZ-61;IPLJ0344516+230230_N GCS9 Cl* Melotte 22 IPL 61 +03 45 01.81 +24 04 17.7 0 20.559 19.037 18.830 18.050 18.035 34.71 7.32 -15.30 7.32 2.57 noZ L07_faintYJ_6 GCS9 +03 45 04.22 +26 40 44.6 0 14.156 13.564 13.007 12.685 12.707 21.95 3.00 -39.21 3.00 0.12 noZ HHJ185_DH371_Moraux2003_56 GCS9 Cl* Melotte 22 HHJ 185 +03 45 14.52 +22 29 28.6 0 19.756 18.383 17.410 16.493 14.00 4.20 -53.10 4.20 1.66 noZ int-pl-IZ-69;IPLJ0345144+222929_Y GCS9 Cl* Melotte 22 IPL 69 +03 45 20.05 +22 33 25.5 0 19.320 18.432 17.776 17.052 35.01 5.33 -16.01 5.33 2.52 noZ int-pl-IZ-75;IPLJ0345200+223325_N GCS9 Cl* Melotte 22 IPL 75 +03 45 27.73 +23 10 13.1 0 13.121 12.596 11.997 11.686 11.703 10.78 2.28 -31.45 2.28 3.05 noZ SK520 GCS9 Cl* Melotte 22 SK 520 +03 45 33.30 +23 34 34.3 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 1.70 noZ L07_faintYJ_7 GCS9 +03 45 33.99 +23 11 06.5 0 14.420 13.828 13.290 12.974 12.977 22.86 2.28 -40.74 2.28 3.12 noZ DH396 GCS9 Cl* Melotte 22 DH 396 +03 45 35.06 +21 56 22.2 0 19.470 18.169 17.595 17.136 17.002 -7.69 5.05 -13.84 5.05 1.35 noZ L07_faintYJ_8 GCS9 +03 45 42.26 +22 48 07.5 0 16.198 15.598 15.013 14.722 1.77 2.31 -16.21 2.31 5.74 noZ int-pl-IZ-50;2MASSJ0345422+224807_N GCS9 Cl* Melotte 22 IPL 50 +03 45 55.17 +22 51 31.0 0 16.109 15.358 14.804 14.433 14.420 14.67 2.30 -48.69 2.30 5.52 noZ int-pl-IZ-42_2MASSJ0345551+225131_Y_L07_243 GCS9 Cl* Melotte 22 IPL 42 +03 46 08.43 +22 37 57.3 0 20.002 19.462 18.771 17.791 22.24 9.40 21.27 9.40 3.53 noZ int-pl-IZ-73;IPLJ0346083+223757_N GCS9 Cl* Melotte 22 IPL 73 +03 46 09.59 +22 42 37.2 0 13.286 12.709 12.202 11.874 11.888 14.57 2.28 -37.86 2.28 3.40 noZ HCG217_SK487_HHJ297_BPL91_DH424 GCS9 Cl* Melotte 22 HCG 217 +03 46 13.85 +22 42 19.6 0 13.878 13.286 12.749 12.429 12.464 15.81 2.28 -42.30 2.28 3.16 noZ HHJ200_BPL92_DH431 GCS9 Cl* Melotte 22 HHJ 200 +03 46 27.94 +23 42 39.0 0 19.824 18.591 17.757 17.478 17.275 -7.35 4.50 -5.31 4.50 4.50 noZ L07_faintYJ_9 GCS9 +03 46 29.11 +22 59 47.6 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 1.38 noZ L07_faintYJ_10 GCS9 +03 46 43.13 +22 35 19.9 0 19.439 18.710 18.058 17.757 4.51 7.62 -19.89 7.62 4.95 noZ int-pl-IZ-40;IPLJ0346430+223520_N GCS9 Cl* Melotte 22 IPL 40 +03 46 51.06 +22 34 28.6 0 19.887 18.580 18.106 17.309 17.424 10.21 5.90 -91.85 5.90 0.99 noZ L07_faintYJ_11 GCS9 +03 47 23.86 +23 08 56.9 0 13.440 12.896 12.358 12.060 12.050 16.54 2.28 -42.81 2.28 0.72 noZ HCG270_SK427_HHJ275_DH511 GCS9 Cl* Melotte 22 HCG 270 +03 47 23.97 +22 42 37.3 0 16.123 15.354 14.809 14.402 14.361 -1.91 2.31 -9.05 2.31 13.33 noZ BPL142_int-pl-IZ-37;RPLJ0347239+22423_Roque17_Y GCS9 Cl* Melotte 22 BPL 142 +03 47 25.58 +22 41 05.7 0 16.097 15.475 14.892 14.567 50.44 2.31 -53.06 2.31 7.71 noZ int-pl-IZ-36;2MASSJ0347255+224105_N GCS9 Cl* Melotte 22 IPL 36 +03 47 34.79 +22 48 04.5 0 13.538 13.004 12.450 12.140 12.143 14.52 2.28 -42.93 2.28 3.56 noZ HCG284_HHJ278_BPL148_DH524 GCS9 Cl* Melotte 22 HCG 284 +03 47 38.47 +23 56 27.7 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 0.74 noZ L07_faintYJ_12 GCS9 +03 47 41.41 +22 44 32.9 0 16.111 15.521 14.865 14.511 14.527 13.41 2.31 0.90 2.31 15.45 noZ int-pl-IZ-47_RPLJ0347413+224433_Roque48_N GCS9 Cl* Melotte 22 IPL 47 +03 47 43.67 +26 48 12.7 0 14.029 13.455 12.910 12.602 12.617 20.73 2.99 -29.14 2.99 0.15 noZ HHJ193 GCS9 Cl* Melotte 22 HHJ 193 +03 47 44.16 +24 57 24.0 0 20.205 18.716 17.889 17.262 17.066 10.23 4.17 -30.52 4.17 2.74 noZ L07_faintYJ_13 GCS9 +03 47 46.52 +24 55 46.5 0 19.536 18.271 17.418 16.608 16.543 20.20 3.10 -45.92 3.10 3.57 noZ L07_faintYJ_14 GCS9 +03 47 54.17 +22 39 25.5 0 14.311 13.757 13.214 12.899 12.921 18.12 2.28 -43.32 2.28 6.76 noZ HHJ145_BPL161 GCS9 Cl* Melotte 22 HHJ 145 +03 48 04.95 +22 51 29.4 0 19.175 18.460 17.613 17.200 9.01 5.49 -14.56 5.49 4.62 noZ int-pl-IZ-46;IPLJ0348048+225129_N GCS9 Cl* Melotte 22 IPL 46 +03 48 05.47 +21 57 31.2 0 20.160 18.769 17.961 17.316 17.407 2.75 6.05 -28.88 6.05 3.01 noZ L07_faintYJ_15 GCS9 +03 48 15.65 +25 50 08.9 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 1.86 noZ L07_faintYJ_16 GCS9 +03 48 19.48 +23 56 24.6 0 20.187 19.592 18.702 18.018 18.761 -0.62 6.47 -7.62 6.47 0.70 noZ Festin98_011 GCS9 Cl* Melotte 22 NPL 011 +03 48 39.57 +23 56 11.7 0 20.722 19.221 18.750 18.179 18.196 3.85 6.28 -27.06 6.28 1.63 noZ L07_faintYJ_17 GCS9 +03 48 45.69 +25 42 01.5 0 13.138 12.569 11.988 11.686 11.709 18.97 2.27 -44.69 2.27 0.64 noZ HHJ333_DH591 GCS9 Cl* Melotte 22 HHJ 333 +03 48 49.37 +22 45 50.0 0 19.901 19.049 18.457 18.022 18.177 2.45 9.50 2.39 9.50 2.37 noZ Roque26 GCS9 Cl* Melotte 22 Roque 26 +03 49 12.65 +25 42 07.2 0 13.114 12.530 12.019 11.673 11.710 12.39 2.27 -44.95 2.27 17.46 noZ HCG365_SK325_HHJ331_DH608 GCS9 Cl* Melotte 22 HCG 365 +03 50 15.96 +24 23 28.6 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 1.54 noZ L07_faintYJ_18 GCS9 +03 50 39.55 +25 02 54.6 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 2.83 noZ BRB23_L3.5_L07_faintYJ_19 GCS9 Cl* Melotte 22 BRB 23 +03 50 41.21 +25 39 30.4 0 14.791 14.167 13.614 13.272 13.262 17.33 2.28 -46.49 2.28 6.53 noZ DH682 GCS9 Cl* Melotte 22 DH 682 +03 50 56.62 +25 35 06.3 0 12.970 12.392 11.893 11.585 11.581 14.82 2.27 -42.39 2.27 7.19 noZ HCG412_SK253_DH688 GCS9 Cl* Melotte 22 HCG 412 +03 51 29.47 +24 00 37.4 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3.20 noZ L07_faintYJ_20 GCS9 +03 51 41.62 +25 55 45.4 0 20.584 19.121 18.467 18.048 18.073 -19.45 8.36 -21.24 8.36 0.66 noZ L07_faintYJ_21 GCS9 +03 52 05.33 +25 37 34.0 0 18.965 17.690 17.946 17.510 17.886 153.64 8.22 -46.42 8.22 14.51 noZ L07_faintYJ_22 GCS9 +03 52 27.19 +23 12 08.0 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 0.70 noZ L07_faintYJ_23 GCS9 +03 52 34.75 +22 56 04.5 0 19.602 18.412 17.568 17.084 17.019 34.50 4.45 -2.97 4.45 0.86 noZ L07_faintYJ_24 GCS9 +03 52 39.15 +24 46 29.4 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 1.97 noZ BRB20_L1.0_CFHT-PLIZ-35_L07_faintYJ_25 GCS9 Cl* Melotte 22 BRB 20 +03 52 59.62 +24 42 35.6 0 20.657 19.082 18.693 18.234 18.298 1.65 9.10 16.05 9.10 3.50 noZ L07_faintYJ_26 GCS9 +03 52 59.70 +23 51 55.7 0 19.756 18.587 18.105 17.405 17.261 2.86 5.19 4.78 5.19 1.78 noZ BRB25_None GCS9 Cl* Melotte 22 BRB 25 +03 53 18.93 +23 12 39.1 0 20.060 18.328 17.612 16.887 16.883 -24.95 4.25 2.99 4.25 3.50 noZ L07_faintYJ_27 GCS9 +03 53 19.24 +24 53 30.2 0 18.319 17.810 17.093 16.905 16.967 5.95 3.90 1.25 3.90 2.08 noZ PLZJ112_None GCS9 Cl* Melotte 22 PlZJ 112 +03 53 24.50 +25 02 07.0 0 13.975 13.398 12.853 12.542 12.550 14.26 2.28 -40.35 2.28 34.86 noZ HCG471_HHJ170_BPL286_DH785 GCS9 Cl* Melotte 22 HCG 471 +03 53 40.96 +24 25 09.6 0 12.946 12.445 11.889 11.573 11.626 17.52 2.28 -38.69 2.28 14.78 noZ HCG473_SK122_HHJ372_BPL288 GCS9 Cl* Melotte 22 HCG 473 +03 53 58.23 +24 25 08.8 0 16.052 15.397 14.825 14.435 14.461 14.64 2.31 -34.72 2.31 1.63 noZ BPL295 GCS9 Cl* Melotte 22 BPL 295 +03 54 00.38 +24 34 49.8 0 15.467 14.807 14.279 13.873 13.921 17.48 2.29 -38.45 2.29 1.22 noZ BPL297_Moraux2003_100 GCS9 Cl* Melotte 22 BPL 297 +03 54 10.28 +23 41 40.0 0 19.260 18.143 17.171 16.393 16.395 13.79 3.22 -50.49 3.22 4.20 noZ PLZJ4_BRB21_L3.0_L07_faintYJ_28 GCS9 Cl* Melotte 22 PlZJ 4 +03 54 30.49 +25 11 21.8 0 19.979 18.677 18.127 17.536 17.561 -8.62 5.35 -35.68 5.35 0.30 noZ L07_faintYJ_29 GCS9 +03 55 49.29 +24 53 47.2 0 16.057 15.302 14.707 14.299 14.299 -23.95 2.30 -51.07 2.30 9.97 noZ BPL331 GCS9 Cl* Melotte 22 BPL 331 +03 55 58.17 +24 32 59.6 0 13.269 12.731 12.207 11.901 11.914 12.73 2.28 -40.90 2.28 6.04 noZ DH2004_824 GCS9 Cl* Melotte 22 DH 824 +03 55 58.84 +24 57 39.9 0 13.575 12.975 12.458 12.124 12.142 14.04 2.28 -42.01 2.28 6.81 noZ HHJ260_BPL333_DH825 GCS9 Cl* Melotte 22 HHJ 260 +03 56 11.39 +25 03 36.5 0 15.908 15.193 14.655 14.257 14.290 16.67 2.30 -41.79 2.30 70.90 noZ BPL334_L07_A1_114_L07_215 GCS9 Cl* Melotte 22 BPL 334 +03 41 52.33 +24 15 12.7 0 20.667 19.205 18.358 18.067 0.214 -19.58 9.32 -9.18 9.32 0.78 noY int-pl-IZ-87;IPLJ0341523+241512_N GCS9 Cl* Melotte 22 IPL 87 +03 46 28.19 +22 48 57.7 0 15.194 14.082 13.518 13.192 13.168 45.73 2.28 -58.16 2.28 2.41 noY HHJ87_BPL104 GCS9 Cl* Melotte 22 HHJ 87 +03 46 39.31 +22 47 48.2 0 17.213 16.235 15.626 15.376 15.346 -14.43 2.42 -20.96 2.42 3.51 noY HHJ1 GCS9 Cl* Melotte 22 HHJ 1 +03 46 42.37 +22 41 01.5 0 16.964 15.709 15.192 14.813 0.012 50.06 2.35 -12.61 2.35 5.12 noY int-pl-IZ-41;2MASSJ0346423+224101_N GCS9 Cl* Melotte 22 IPL 41 +03 46 59.02 +22 41 40.3 0 20.165 18.548 18.140 17.540 0.150 48.91 7.04 -26.50 7.04 1.44 noY int-pl-IZ-39;IPLJ0346589+224140_N GCS9 Cl* Melotte 22 IPL 39 +03 51 26.60 +22 48 45.9 0 18.883 16.679 15.938 15.325 15.339 26.84 2.54 -77.34 2.54 6.64 noY L07_A1_96 GCS9 XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 180 / Number of bytes per record NAXIS2 = 1314 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 23 / Number of data fields (columns) EXTNAME = 'J_MNRAS_422_1495_tablec1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablec1' / Table name in METAtab Coordinates, near-infrared (ZYJHK1K2) photometry and proper motions all new Pleiades member candidates identified in the UKIDSS GCS DR9 with the probabilistic and standard selection methods TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:12 offset=1 TFORM1 = 'A12 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 15 / UCD=pos.eq.dec;meta.main char:12 offset=14 TFORM2 = 'A12 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 28 / UCD=meta.number short: ....... offset=27 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 32 / UCD=phot.mag;em.opt.I float: . offset=31 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 39 / UCD=phot.mag;em.IR.J float: .. offset=38 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 46 / UCD=phot.mag;em.IR.J float: .. offset=45 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 53 / UCD=phot.mag;em.IR.H float: .. offset=52 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 60 / UCD=phot.mag;em.IR.K float: .. offset=59 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude, first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 67 / UCD=phot.mag;em.IR.K float: .. offset=66 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude, second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 74 / UCD=pos.pm;pos.eq.ra float: .. offset=73 TFORM10 = 'F6.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 81 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=80 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA*cosDE TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 87 / UCD=pos.pm;pos.eq.dec float: . offset=86 TFORM12 = 'F6.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.probability float: .. offset=99 TFORM14 = 'F5.2 ' / Fortran Format TTYPE14 = 'Mmb ' / [0/1]? Membership probability TBCOL15 = 106 / UCD=meta.note short: ......... offset=105 TFORM15 = 'I2 ' / Fortran Format TTYPE15 = 'n_Mmb ' / [1/12] Method used for identification (1) TBCOL16 = 109 / UCD=meta.ref.url char:4 ...... offset=108 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL17 = 114 / UCD=meta.id char:24* ......... offset=113 TFORM17 = 'A24 ' / Fortran Format TTYPE17 = 'SimbadName' / Simbad column added by the CDS TBCOL18 = 139 / UCD=stat.error;phot.mag;em.opt.I f offset=138 TFORM18 = 'F6.3 ' / Fortran Format TTYPE18 = 'e_Zmag ' / ? rms uncertainty on Zmag TUNIT18 = 'mag ' / magnitude TBCOL19 = 146 / UCD=stat.error;phot.mag;em.opt.B f offset=145 TFORM19 = 'F6.3 ' / Fortran Format TTYPE19 = 'e_Ymag ' / ? rms uncertainty on Ymag TUNIT19 = 'mag ' / magnitude TBCOL20 = 153 / UCD=stat.error;phot.mag;em.IR.J fl offset=152 TFORM20 = 'F6.3 ' / Fortran Format TTYPE20 = 'e_Jmag ' / rms uncertainty on Jmag TUNIT20 = 'mag ' / magnitude TBCOL21 = 160 / UCD=stat.error;phot.mag;em.IR.H fl offset=159 TFORM21 = 'F6.3 ' / Fortran Format TTYPE21 = 'e_Hmag ' / rms uncertainty on Hmag TUNIT21 = 'mag ' / magnitude TBCOL22 = 167 / UCD=stat.error;phot.mag;em.IR.K fl offset=166 TFORM22 = 'F6.3 ' / Fortran Format TTYPE22 = 'e_K1mag ' / rms uncertainty on K1mag TUNIT22 = 'mag ' / magnitude TBCOL23 = 174 / UCD=stat.error;phot.mag;em.IR.K fl offset=173 TFORM23 = 'F6.3 ' / Fortran Format TTYPE23 = 'e_K2mag ' / ? rms uncertainty on K2mag TUNIT23 = 'mag ' / magnitude END +03 36 01.950 +27 11 04.70 1 16.248 15.492 14.774 14.234 13.810 13.782 15.79 3.79 -42.70 3.79 0.72 12 GCS9 UGCS J033601.95+271104.7 0.006 0.004 0.004 0.004 0.004 0.003 +03 51 27.880 +27 58 55.70 0 15.548 14.979 14.360 13.821 13.468 13.470 22.99 2.95 -41.88 2.95 0.74 12 GCS9 UGCS J035127.88+275855.7 0.005 0.003 0.004 0.003 0.003 0.002 +03 45 06.250 +28 42 19.10 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.85 12 GCS9 Cl* Melotte 22 DH 374 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 06.210 +28 08 32.00 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.62 12 GCS9 Cl* Melotte 22 DH 125 0.004 0.004 0.004 0.004 0.003 0.002 +03 50 54.420 +27 26 13.00 0 15.915 15.313 14.686 14.132 13.756 13.726 19.89 2.89 -41.91 2.89 0.88 12 GCS9 UGCS J035054.41+272612.9 0.006 0.004 0.004 0.004 0.004 0.003 +03 51 30.600 +27 22 49.50 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.89 12 GCS9 Cl* Melotte 22 DH 714 0.004 0.003 0.003 0.002 0.002 0.002 +03 41 33.580 +27 09 50.10 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.89 12 GCS9 Cl* Melotte 22 DH 178 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 57.510 +27 38 25.60 0 15.618 15.106 14.510 13.963 13.618 13.613 16.32 2.89 -40.24 2.89 0.86 12 GCS9 UGCS J034857.50+273825.6 0.005 0.004 0.004 0.003 0.004 0.003 +03 41 38.790 +27 41 07.10 0 12.878 12.514 12.023 11.588 11.219 11.285 24.87 3.28 -39.81 3.28 0.65 12 GCS9 UGCS J034138.79+274107.0 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 40.770 +28 32 05.80 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.75 12 GCS9 Cl* Melotte 22 DH 399 0.004 0.003 0.003 0.003 0.003 0.002 +03 46 23.740 +26 34 23.00 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.73 12 GCS9 Cl* Melotte 22 DH 444 0.003 0.003 0.002 0.002 0.002 0.001 +03 47 56.660 +26 31 50.90 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 0.76 12 GCS9 Cl* Melotte 22 DH 543 0.001 0.001 0.001 0.001 0.001 0.001 +03 47 28.410 +26 32 05.50 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.93 12 GCS9 Cl* Melotte 22 DH 516 0.003 0.002 0.002 0.002 0.001 0.001 +03 50 39.700 +26 34 20.10 0 14.506 14.061 13.500 12.959 12.652 12.675 20.26 2.95 -42.79 2.95 0.94 12 GCS9 UGCS J035039.70+263420.0 0.003 0.003 0.002 0.002 0.002 0.001 +03 50 39.700 +26 34 17.70 0 14.817 14.334 13.753 13.228 12.895 12.916 19.47 2.95 -42.84 2.95 0.94 12 GCS9 UGCS J035039.69+263417.7 0.004 0.003 0.003 0.002 0.002 0.001 +03 41 56.490 +27 02 57.90 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.86 12 GCS9 Cl* Melotte 22 DH 195 0.003 0.003 0.003 0.002 0.002 0.002 +03 41 53.050 +27 04 42.90 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.92 12 GCS9 Cl* Melotte 22 DH 192 0.003 0.003 0.003 0.002 0.002 0.002 +04 05 52.090 +27 09 13.10 0 15.196 14.742 14.147 13.596 13.266 13.266 22.95 2.96 -45.92 2.96 0.66 12 GCS9 UGCS J040552.08+270913.1 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 44.090 +25 39 49.50 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 0.90 12 GCS9 Cl* Melotte 22 MBSC 72 0.004 0.003 0.003 0.002 0.002 0.002 +03 44 10.760 +25 37 38.20 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 0.62 12 GCS9 Cl* Melotte 22 DH 316 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 34.140 +25 35 25.80 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 0.93 12 GCS9 V* V622 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 25.080 +25 34 03.90 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 0.88 12 GCS9 Cl* Melotte 22 HHJ 100 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 52.790 +25 29 30.30 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 298 0.003 0.003 0.003 0.002 0.002 0.001 +03 43 53.880 +25 28 30.00 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 0.76 12 GCS9 V* MQ Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 43 37.320 +25 24 32.00 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 0.69 12 GCS9 V* LY Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 33.490 +26 14 53.10 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.93 12 GCS9 Cl* Melotte 22 DH 347 0.002 0.002 0.002 0.001 0.001 0.001 +03 54 46.530 +25 31 34.90 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.90 12 GCS9 Cl* Melotte 22 DH 808 0.003 0.003 0.002 0.002 0.002 0.001 +03 43 07.580 +25 34 29.00 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 0.92 12 GCS9 V* V845 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 42 43.810 +25 32 06.20 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 341 0.002 0.002 0.002 0.001 0.001 0.001 +03 56 46.600 +26 21 00.40 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 0.77 12 GCS9 Cl* Melotte 22 DH 843 0.004 0.003 0.003 0.002 0.003 0.002 +03 43 11.770 +25 31 31.90 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 0.72 12 GCS9 UGCS J034311.76+253131.9 0.007 0.005 0.005 0.004 0.005 0.003 +03 47 25.900 +25 26 26.40 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 0.91 12 GCS9 Cl* Melotte 22 MBSC 49 0.003 0.002 0.002 0.002 0.001 0.001 +03 47 37.350 +25 20 02.30 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 0.93 12 GCS9 Cl* Melotte 22 MBSC 43 0.003 0.002 0.002 0.002 0.002 0.001 +03 36 42.670 +28 21 05.90 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.78 12 GCS9 Cl* Melotte 22 DH 62 0.004 0.003 0.004 0.003 0.002 0.002 +03 45 58.800 +26 20 01.70 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.81 12 GCS9 Cl* Melotte 22 DH 413 0.005 0.004 0.003 0.003 0.003 0.002 +03 38 43.300 +25 22 26.90 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 0.79 12 GCS9 V* V783 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 41 11.540 +26 16 18.80 0 15.386 14.841 14.239 13.677 13.324 13.349 21.71 3.00 -37.68 3.00 0.60 12 GCS9 UGCS J034111.53+261618.8 0.005 0.003 0.004 0.003 0.003 0.002 +03 51 13.850 +25 23 11.30 0 13.741 13.323 12.796 12.246 11.944 12.001 16.21 2.23 -43.01 2.23 0.93 12 GCS9 V* V801 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 39.800 +25 38 45.20 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.68 12 GCS9 Cl* Melotte 22 DH 46 0.003 0.003 0.003 0.003 0.001 +04 03 25.000 +28 32 23.60 0 14.208 13.836 13.311 12.724 12.453 12.501 24.32 2.96 -39.19 2.96 0.66 12 GCS9 UGCS J040325.00+283223.6 0.003 0.002 0.002 0.001 0.002 0.001 +03 55 56.430 +25 17 59.80 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.94 12 GCS9 V* V690 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 08.420 +25 32 55.70 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 0.93 12 GCS9 V* V671 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 49 47.040 +25 42 36.80 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 638 0.002 0.002 0.002 0.001 0.001 0.001 +03 54 11.490 +25 18 42.70 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.88 12 GCS9 V* V887 Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 23.060 +25 29 47.60 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.93 12 GCS9 V* KO Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 59.750 +26 27 39.60 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.64 12 GCS9 Cl* Melotte 22 DH 200 0.003 0.002 0.003 0.002 0.002 0.001 +03 37 24.150 +25 43 20.10 0 12.730 12.420 11.937 11.360 11.085 11.153 22.40 2.95 -44.05 2.95 0.78 12 GCS9 UGCS J033724.15+254320.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 45.780 +25 27 30.20 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 0.88 12 GCS9 V* V532 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 34 47.540 +26 22 13.40 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.60 12 GCS9 Cl* Melotte 22 DH 39 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 04.750 +25 22 50.00 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 0.87 12 GCS9 V* V452 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 15.560 +26 48 03.60 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.74 12 GCS9 Cl* Melotte 22 DH 102 0.004 0.003 0.004 0.003 0.003 0.002 +03 47 43.880 +26 13 26.80 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.86 12 GCS9 Cl* Melotte 22 DH 534 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 30.590 +26 16 44.50 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.93 12 GCS9 V* V456 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 21.940 +26 22 47.20 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 0.71 12 GCS9 Cl* Melotte 22 DH 508 0.003 0.002 0.002 0.002 0.002 0.001 +03 40 11.040 +25 23 26.60 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.87 12 GCS9 Cl* Melotte 22 DH 129 0.004 0.003 0.003 0.002 0.002 0.002 +03 40 14.910 +25 19 18.70 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.87 12 GCS9 Cl* Melotte 22 DH 131 0.002 0.001 0.001 0.001 0.001 0.001 +03 44 04.910 +26 58 10.50 0 15.115 14.597 13.992 13.447 13.119 13.096 16.42 2.94 -41.32 2.94 0.88 12 GCS9 UGCS J034404.91+265810.5 0.004 0.003 0.003 0.003 0.003 0.002 +03 51 39.510 +26 52 55.60 0 15.480 14.951 14.332 13.794 13.454 13.448 20.14 2.96 -44.60 2.96 0.87 12 GCS9 UGCS J035139.51+265255.5 0.005 0.004 0.004 0.003 0.003 0.002 +03 51 55.240 +26 57 40.20 0 13.156 12.689 12.074 11.716 11.274 11.253 17.80 2.95 -40.13 2.95 0.91 12 GCS9 UGCS J035155.24+265740.1 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 13.200 +26 11 45.30 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 0.88 12 GCS9 Cl* Melotte 22 DH 745 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 12.190 +26 22 08.90 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 0.86 12 GCS9 V* V564 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 35 56.060 +25 21 59.30 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.87 12 GCS9 Cl* Melotte 22 DH 50 0.002 0.001 0.001 0.001 0.001 +03 36 05.330 +25 21 03.40 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.94 12 GCS9 Cl* Melotte 22 DH 51 0.003 0.002 0.002 0.002 0.001 +03 42 38.350 +25 28 44.30 0 15.625 15.076 14.453 13.924 13.588 13.556 21.08 2.24 -41.30 2.24 0.85 12 GCS9 UGCS J034238.35+252844.2 0.006 0.005 0.004 0.003 0.004 0.002 +03 50 14.760 +25 25 29.80 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 662 0.003 0.003 0.003 0.002 0.002 0.001 +03 50 01.570 +25 24 01.50 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 646 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 20.310 +25 25 42.40 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 0.66 12 GCS9 V* V798 Tau 0.003 0.002 0.002 0.002 0.001 0.001 +03 46 47.190 +25 20 53.10 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 0.66 12 GCS9 Cl* Melotte 22 DH 469 0.003 0.002 0.002 0.002 0.002 0.001 +03 41 04.150 +25 30 25.60 0 15.724 15.167 14.547 14.003 13.636 13.636 12.89 2.24 -40.93 2.24 0.71 12 GCS9 UGCS J034104.14+253025.6 0.006 0.005 0.005 0.003 0.004 0.003 +03 42 08.290 +25 36 59.90 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 211 0.003 0.002 0.002 0.001 0.001 0.001 +03 42 03.420 +25 22 39.10 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 0.68 12 GCS9 Cl* Melotte 22 DH 206 0.003 0.002 0.002 0.002 0.001 0.001 +03 41 30.350 +25 17 05.90 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 0.60 12 GCS9 UGCS J034130.35+251705.9 0.010 0.008 0.007 0.006 0.006 0.005 +03 52 41.820 +26 46 10.50 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.74 12 GCS9 Cl* Melotte 22 DH 761 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 09.040 +25 32 49.00 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 377 0.003 0.003 0.003 0.002 0.002 0.001 +04 06 54.480 +26 20 07.50 0 12.888 12.543 11.998 11.384 11.046 11.113 14.03 2.96 -40.84 2.96 0.70 12 GCS9 UGCS J040654.47+262007.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 22.420 +26 11 36.00 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 0.93 12 GCS9 Cl* Melotte 22 DH 104 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 12.620 +26 49 44.90 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.91 12 GCS9 Cl* Melotte 22 DH 215 0.003 0.002 0.002 0.001 0.001 0.001 +03 37 48.930 +26 51 45.10 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.86 12 GCS9 Cl* Melotte 22 DH 79 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 58.780 +25 26 19.00 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.90 12 GCS9 Cl* Melotte 22 BPL 277 0.004 0.004 0.003 0.003 0.003 0.002 +03 52 54.250 +25 17 43.30 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.89 12 GCS9 V* V477 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 48.160 +26 37 47.50 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.89 12 GCS9 Cl* Melotte 22 MBSC 98 0.006 0.005 0.004 0.004 0.004 0.003 +03 38 27.830 +26 51 25.40 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.81 12 GCS9 Cl* Melotte 22 DH 89 0.003 0.003 0.003 0.002 0.002 0.001 +03 37 15.610 +26 29 29.80 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.85 12 GCS9 Cl* Melotte 22 STAR 15 0.007 0.005 0.005 0.004 0.004 0.003 +03 48 50.450 +25 17 54.70 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 0.67 12 GCS9 Cl* Melotte 22 BPL 192 0.009 0.007 0.006 0.005 0.005 0.004 +03 52 07.960 +25 27 54.60 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.67 12 GCS9 V* V390 Tau 0.004 0.004 0.003 0.003 0.003 0.002 +03 37 11.980 +26 46 28.70 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.65 12 GCS9 Cl* Melotte 22 DH 68 0.003 0.002 0.002 0.001 0.001 0.001 +03 46 44.790 +24 44 58.20 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 109 0.005 0.003 0.003 0.002 0.003 0.002 +03 47 16.450 +24 44 50.10 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 136 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 38.050 +24 49 10.80 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 0.91 12 GCS9 Cl* Melotte 22 BPL 151 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 22.810 +24 48 53.40 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 0.78 12 GCS9 Cl* Melotte 22 BPL 176 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 34.170 +25 43 05.90 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 522 0.003 0.002 0.002 0.002 0.001 0.001 +03 47 33.060 +25 38 18.40 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 0.90 12 GCS9 V* V791 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 28.430 +24 40 33.00 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 0.87 12 GCS9 Cl* Melotte 22 DH 517 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 05.980 +25 40 20.80 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.89 12 GCS9 Cl* Melotte 22 DH 124 0.004 0.003 0.003 0.002 0.002 0.001 +03 46 15.110 +26 46 48.80 1 16.624 15.838 15.032 14.495 14.037 14.088 20.73 2.97 -42.79 2.97 0.68 12 GCS9 UGCS J034615.11+264648.8 0.011 0.007 0.006 0.005 0.005 0.003 +03 39 08.130 +24 46 14.40 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.91 12 GCS9 V* KM Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 49 38.240 +26 51 05.60 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.94 12 GCS9 Cl* Melotte 22 MBSC 29 0.002 0.002 0.002 0.001 0.001 0.001 +03 36 10.560 +24 45 59.70 0 14.996 14.487 13.888 13.330 13.004 12.994 17.12 2.63 -43.88 2.63 0.94 12 GCS9 UGCS J033610.55+244559.7 0.004 0.003 0.003 0.002 0.003 0.002 +03 50 06.430 +26 58 18.70 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.92 12 GCS9 Cl* Melotte 22 DH 652 0.004 0.003 0.003 0.002 0.002 0.001 +03 50 40.830 +24 40 02.60 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 0.78 12 GCS9 Cl* Melotte 22 DH 681 0.005 0.004 0.003 0.003 0.003 0.002 +03 46 35.360 +23 57 07.40 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 0.75 12 GCS9 Cl* Melotte 22 SHF 35 0.012 0.007 0.006 0.005 0.006 0.004 +03 46 55.780 +23 56 24.10 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 480 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 00.360 +23 52 48.20 0 15.230 14.607 13.981 13.439 13.093 13.073 14.89 2.22 -45.67 2.22 0.80 12 GCS9 UGCS J034700.35+235248.1 0.005 0.003 0.003 0.002 0.002 0.002 +03 46 22.200 +23 52 41.00 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 441 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 13.670 +23 49 53.20 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 0.89 12 GCS9 V* V645 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 27.690 +23 48 45.50 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 161 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 58.820 +24 40 04.30 0 13.675 13.254 12.728 12.172 11.871 11.897 14.65 2.20 -42.70 2.20 0.88 12 GCS9 UGCS J035158.82+244004.3 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 59.320 +24 39 58.80 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.83 12 GCS9 V* V387 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 10.960 +24 48 45.80 0 14.889 14.418 13.865 13.318 13.020 21.80 2.97 -46.03 2.97 0.87 12 GCS9 UGCS J034410.95+244845.7 0.004 0.003 0.003 0.002 0.002 +03 44 25.600 +24 40 52.60 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 0.62 12 GCS9 V* V438 Tau 0.002 0.002 0.002 0.001 0.001 +03 43 12.120 +24 44 45.10 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 0.61 12 GCS9 V* V509 Tau 0.002 0.002 0.001 0.001 0.001 +04 00 28.190 +23 51 24.00 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.78 12 GCS9 Cl* Melotte 22 DH 892 0.002 0.002 0.002 0.001 0.001 +03 49 22.150 +25 47 37.70 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 0.80 12 GCS9 Cl* Melotte 22 DH 618 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 32.810 +25 47 46.80 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 628 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 09.750 +24 41 32.70 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 0.67 12 GCS9 V* LU Tau 0.002 0.001 0.001 0.001 0.001 +03 43 13.070 +24 39 19.30 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 0.91 12 GCS9 V* LV Tau 0.002 0.001 0.001 0.001 0.001 +03 49 58.610 +25 53 46.20 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 0.89 12 GCS9 UGCS J034958.61+255346.1 0.004 0.003 0.003 0.002 0.002 0.002 +03 41 54.210 +25 43 47.10 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 0.89 12 GCS9 V* V608 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 42 10.990 +25 44 35.00 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 0.83 12 GCS9 UGCS J034210.99+254435.0 0.005 0.004 0.004 0.003 0.003 0.002 +03 53 09.630 +23 33 47.70 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.63 12 GCS9 2MASS J03530962+2333480 0.007 0.005 0.005 0.005 0.003 +03 44 51.510 +25 05 16.50 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 0.91 12 GCS9 UGCS J034451.50+250516.4 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 02.880 +25 05 19.60 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 0.75 12 GCS9 Cl* Melotte 22 DH 368 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 18.150 +25 05 58.10 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 0.74 12 GCS9 V* OR Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 44 32.170 +25 08 12.30 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 0.90 12 GCS9 Cl* Melotte 22 HHJ 68 0.004 0.004 0.003 0.003 0.003 0.002 +03 52 51.790 +23 33 47.90 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 0.88 12 GCS9 2MASS J03525177+2333483 0.006 0.005 0.005 0.004 0.003 +03 41 40.910 +25 54 24.10 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 0.74 12 GCS9 2MASS J03414089+2554242 0.012 0.008 0.007 0.005 0.006 0.004 +03 45 16.990 +25 15 47.50 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 0.89 12 GCS9 V* V520 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 44.050 +25 06 22.40 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 589 0.003 0.002 0.002 0.002 0.002 0.001 +04 04 37.180 +23 23 51.00 0 12.985 12.568 12.054 11.620 11.305 11.370 15.43 3.77 -45.51 3.77 0.63 12 GCS9 UGCS J040437.18+232351.0 0.001 0.001 0.001 0.001 0.001 0.001 +03 41 19.350 +23 51 41.70 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 0.90 12 GCS9 V* V734 Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 58.330 +25 06 20.90 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 0.88 12 GCS9 Cl* Melotte 22 BPL 219 0.005 0.003 0.003 0.003 0.003 0.002 +03 41 05.230 +23 50 14.90 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 0.85 12 GCS9 Cl* Melotte 22 BPL 19 0.006 0.004 0.004 0.004 0.005 0.002 +03 50 01.870 +25 12 40.90 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 0.85 12 GCS9 V* V552 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 51 17.960 +26 01 22.10 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 701 0.003 0.003 0.003 0.002 0.002 0.001 +03 51 18.720 +26 03 15.40 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 0.90 12 GCS9 UGCS J035118.71+260315.4 0.003 0.003 0.003 0.002 0.002 0.001 +03 51 18.860 +26 03 08.70 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 0.92 12 GCS9 Cl* Melotte 22 SK 237 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 44.290 +23 54 14.90 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 0.86 12 GCS9 2MASS J03524427+2354151 0.005 0.004 0.004 0.004 0.004 0.002 +03 51 24.170 +26 03 11.30 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 0.93 12 GCS9 V* V380 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 33.340 +23 51 06.70 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 756 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 18.720 +23 52 36.60 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 0.82 12 GCS9 Cl* Melotte 22 BPL 260 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 51.950 +25 10 01.70 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 0.92 12 GCS9 Cl* Melotte 22 DH 404 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 39.040 +25 13 27.60 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 0.82 12 GCS9 V* OQ Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 50.190 +22 12 42.30 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 0.87 12 GCS9 Cl* Melotte 22 BPL 110 0.006 0.004 0.004 0.003 0.004 0.002 +03 46 03.670 +25 52 28.80 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 416 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 52.600 +25 54 59.80 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 405 0.002 0.002 0.002 0.001 0.001 0.001 +03 57 33.070 +23 56 47.10 0 14.486 14.043 13.479 12.922 12.627 12.632 17.44 2.96 -47.07 2.96 0.88 12 GCS9 UGCS J035733.06+235647.0 0.003 0.002 0.002 0.002 0.002 0.001 +03 53 24.240 +25 14 37.70 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 0.71 12 GCS9 UGCS J035324.24+251437.6 0.011 0.008 0.007 0.006 0.007 0.005 +04 00 26.150 +23 26 17.30 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.89 12 GCS9 Cl* Melotte 22 DH 891 0.002 0.002 0.002 0.001 0.001 0.001 +04 00 16.930 +23 26 22.60 0 14.173 13.780 13.204 12.602 12.318 12.347 15.97 3.35 -48.85 3.35 0.70 12 GCS9 UGCS J040016.93+232622.5 0.002 0.002 0.002 0.002 0.002 0.001 +03 37 31.070 +23 46 07.20 0 15.431 14.956 14.357 13.829 13.476 13.487 16.45 2.50 -38.98 2.50 0.81 12 GCS9 UGCS J033731.07+234607.1 0.005 0.004 0.004 0.003 0.003 0.002 +04 00 40.260 +23 26 53.80 0 14.831 14.274 13.616 13.072 12.736 12.745 18.94 3.35 -45.90 3.35 0.92 12 GCS9 UGCS J040040.25+232653.8 0.004 0.003 0.003 0.002 0.002 0.001 +04 01 12.080 +23 54 12.80 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.82 12 GCS9 Cl* Melotte 22 DH 898 0.002 0.001 0.001 0.001 0.001 +03 46 31.320 +22 18 19.60 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 0.92 12 GCS9 V* V859 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 46 40.600 +22 22 03.50 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 0.88 12 GCS9 UGCS J034640.59+222203.5 0.006 0.004 0.004 0.003 0.004 0.002 +03 49 08.840 +25 53 48.60 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 603 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 57.150 +26 07 00.10 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.82 12 GCS9 Cl* Melotte 22 MBSC 94 0.005 0.004 0.004 0.003 0.003 0.002 +03 50 05.000 +23 18 17.20 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 0.77 12 GCS9 Cl* Melotte 22 DH 650 0.005 0.004 0.003 0.003 0.003 0.002 +03 46 00.930 +22 12 29.40 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 0.68 12 GCS9 Cl* Melotte 22 STAR 9 0.007 0.006 0.005 0.004 0.005 0.003 +03 40 10.930 +26 06 40.80 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.67 12 GCS9 V* KS Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 15.630 +23 22 49.10 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 0.88 12 GCS9 Cl* Melotte 22 STAR 28 0.005 0.004 0.004 0.003 0.003 0.002 +03 49 32.570 +23 24 41.00 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 0.91 12 GCS9 Cl* Melotte 22 HHJ 245 0.003 0.002 0.002 0.002 0.001 0.001 +03 41 19.860 +25 06 49.00 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.87 12 GCS9 Cl* Melotte 22 HHJ 191 0.003 0.003 0.002 0.002 0.001 +03 41 30.710 +25 11 52.30 0 15.090 14.523 13.857 13.309 12.925 18.12 2.97 -40.52 2.97 0.88 12 GCS9 UGCS J034130.71+251152.2 0.004 0.003 0.003 0.002 0.001 +03 40 59.260 +25 11 55.20 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.86 12 GCS9 Cl* Melotte 22 DH 162 0.005 0.004 0.004 0.003 0.002 +03 40 39.460 +23 26 34.80 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 0.69 12 GCS9 Cl* Melotte 22 DH 147 0.008 0.006 0.006 0.005 0.006 0.004 +03 53 16.440 +23 20 58.10 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 0.80 12 GCS9 Cl* Melotte 22 BPL 281 0.004 0.003 0.003 0.003 0.003 0.002 +03 46 07.560 +23 44 42.60 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 0.70 12 GCS9 UGCS J034607.55+234442.5 0.008 0.004 0.003 0.002 0.002 0.001 +03 38 06.260 +25 05 39.90 0 13.122 12.831 12.361 11.819 11.524 11.570 14.79 2.48 -45.21 2.48 0.87 12 GCS9 UGCS J033806.26+250539.9 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 06.520 +23 50 20.20 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 0.81 12 GCS9 Cl* Melotte 22 HII 892 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 04.300 +23 55 40.60 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 363 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 55.130 +23 23 36.10 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 0.70 12 GCS9 2MASS J03535511+2323363 0.012 0.007 0.006 0.005 0.005 0.004 +03 45 59.200 +23 48 46.80 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 0.92 12 GCS9 Cl* Melotte 22 HHJ 127 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 49.420 +23 57 05.70 0 15.929 15.340 14.688 14.182 13.809 13.790 15.31 2.23 -44.67 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 16 0.006 0.005 0.004 0.003 0.004 0.003 +03 43 06.500 +22 17 49.20 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 0.67 12 GCS9 Cl* Melotte 22 HHJ 18 0.006 0.005 0.004 0.004 0.004 0.003 +03 37 50.990 +25 09 25.30 0 13.080 12.684 12.159 11.654 11.378 11.376 21.24 2.48 -46.66 2.48 0.85 12 GCS9 UGCS J033750.99+250925.2 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 46.900 +23 53 00.30 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 0.88 12 GCS9 UGCS J034546.90+235300.3 0.006 0.004 0.004 0.003 0.003 0.002 +03 45 46.480 +23 47 43.10 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 0.89 12 GCS9 Cl* Melotte 22 HHJ 421 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 38.990 +23 57 00.90 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 0.69 12 GCS9 UGCS J034538.98+235700.8 0.004 0.003 0.003 0.002 0.003 0.002 +03 45 38.910 +23 48 55.90 0 15.708 14.967 14.161 13.398 12.966 12.956 20.47 2.22 -40.70 2.22 0.85 12 GCS9 UGCS J034538.90+234855.8 0.006 0.004 0.003 0.002 0.002 0.001 +03 42 56.240 +22 22 38.50 0 15.481 14.854 14.196 13.662 13.283 13.273 23.05 2.51 -45.22 2.51 0.69 12 GCS9 UGCS J034256.23+222238.4 0.005 0.004 0.003 0.003 0.003 0.002 +03 29 58.760 +23 22 18.30 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.81 12 GCS9 Cl* Melotte 22 DH 9 0.002 0.001 0.002 0.001 0.001 0.001 +03 53 48.040 +23 49 09.60 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 0.81 12 GCS9 Cl* Melotte 22 BPL 291 0.005 0.004 0.004 0.003 0.003 0.002 +03 53 24.120 +23 47 58.40 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 784 0.003 0.002 0.002 0.002 0.002 0.001 +04 03 43.840 +23 53 47.00 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.93 12 GCS9 Cl* Melotte 22 DH 909 0.002 0.002 0.002 0.001 0.001 0.001 +03 39 44.190 +22 07 45.50 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 111 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 29.700 +22 18 13.30 0 14.741 14.294 13.748 13.159 12.858 12.841 19.08 2.51 -39.34 2.51 0.90 12 GCS9 UGCS J034929.70+221813.2 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 25.550 +22 18 44.40 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 0.79 12 GCS9 Cl* Melotte 22 BPL 206 0.004 0.004 0.003 0.003 0.003 0.002 +03 49 35.460 +22 23 26.10 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.93 12 GCS9 Cl* Melotte 22 BPL 211 0.003 0.003 0.003 0.002 0.003 0.002 +03 45 12.620 +23 53 45.00 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 0.71 12 GCS9 Cl* Melotte 22 HHJ 14 0.007 0.005 0.004 0.003 0.004 0.003 +03 52 38.910 +25 50 25.30 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.89 12 GCS9 V* V768 Tau 0.002 0.002 0.002 0.002 0.001 0.001 +03 52 07.430 +25 53 02.70 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 0.88 12 GCS9 UGCS J035207.42+255302.6 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 31.380 +25 15 07.50 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 755 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 47.080 +26 04 35.30 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 311 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 17.370 +23 48 23.50 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 188 0.003 0.003 0.003 0.002 0.002 0.001 +03 35 46.410 +22 24 49.70 0 15.172 14.684 14.105 13.586 13.262 13.247 22.16 2.94 -42.14 2.94 0.81 12 GCS9 UGCS J033546.40+222449.6 0.004 0.003 0.003 0.003 0.003 0.002 +03 42 02.930 +23 55 53.70 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.87 12 GCS9 V* V727 Tau 0.001 0.001 0.001 0.001 0.000 +03 41 10.270 +25 45 55.90 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 164 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 40.320 +25 50 48.10 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 148 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 33.080 +25 45 09.50 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.71 12 GCS9 V* V742 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 33 46.650 +23 48 19.40 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 0.94 12 GCS9 Cl* Melotte 22 DH 30 0.002 0.002 0.002 0.001 0.001 0.001 +03 57 30.160 +25 16 46.70 0 13.312 12.967 12.456 11.833 11.542 11.620 21.77 2.47 -45.25 2.47 0.88 12 GCS9 UGCS J035730.15+251646.7 0.002 0.001 0.002 0.001 0.001 0.001 +03 42 54.000 +26 08 16.10 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 244 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 04.990 +23 46 06.40 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 372 0.004 0.003 0.003 0.002 0.002 0.001 +03 43 19.060 +26 04 43.90 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 260 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 58.590 +23 55 40.90 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 363 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 56.010 +23 55 53.40 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 0.91 12 GCS9 V* NX Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 26.200 +26 02 30.60 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 0.92 12 GCS9 V* V619 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 34.300 +23 51 24.60 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 14 0.011 0.007 0.007 0.005 0.006 0.004 +03 36 16.320 +25 08 48.80 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.91 12 GCS9 Cl* Melotte 22 DH 54 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 57.530 +25 48 31.20 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 0.94 12 GCS9 V* V561 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 58.040 +22 06 50.80 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 0.74 12 GCS9 2MASS J03475802+2206511 0.011 0.008 0.007 0.006 0.008 0.004 +03 46 54.030 +25 14 44.80 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 0.92 12 GCS9 V* V860 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 15.290 +25 06 55.30 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 0.89 12 GCS9 Cl* Melotte 22 SK 432 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 20.840 +25 05 12.10 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 0.77 12 GCS9 Cl* Melotte 22 HHJ 417 0.001 0.001 0.001 0.001 0.001 0.000 +03 47 25.800 +25 08 32.80 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 0.93 12 GCS9 V* V539 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 41 23.470 +22 01 53.90 0 14.746 14.157 13.488 12.874 12.525 12.523 15.55 2.51 -42.58 2.51 0.91 12 GCS9 UGCS J034123.46+220153.9 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 47.870 +25 13 34.30 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 0.94 12 GCS9 Cl* Melotte 22 DH 537 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 18.460 +22 00 53.30 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 749 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 15.490 +25 14 36.40 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 0.74 12 GCS9 Cl* Melotte 22 DH 563 0.004 0.003 0.003 0.002 0.002 0.002 +03 42 36.950 +25 13 57.50 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 0.70 12 GCS9 Cl* Melotte 22 DH 233 0.004 0.003 0.003 0.003 0.002 +03 42 42.120 +25 11 48.70 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 0.82 12 GCS9 Cl* Melotte 22 DH 237 0.005 0.004 0.004 0.003 0.002 +04 00 10.300 +22 02 17.00 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 888 0.003 0.002 0.002 0.002 0.001 0.001 +03 47 20.430 +22 21 56.30 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 0.80 12 GCS9 UGCS J034720.42+222156.3 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 44.670 +22 12 43.90 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 0.74 12 GCS9 Cl* Melotte 22 BPL 154 0.006 0.005 0.005 0.004 0.005 0.003 +03 47 44.680 +22 23 53.00 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.90 12 GCS9 V* V335 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 43 36.680 +25 47 00.50 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 0.85 12 GCS9 Cl* Melotte 22 DH 276 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 59.910 +26 01 32.20 0 15.077 14.539 13.909 13.336 12.989 17.12 5.31 -37.68 5.31 0.73 12 GCS9 UGCS J033559.91+260132.2 0.004 0.003 0.003 0.003 0.001 +03 43 42.900 +25 51 37.00 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 77 0.004 0.004 0.003 0.002 0.003 0.002 +03 50 44.350 +25 07 05.10 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 0.84 12 GCS9 UGCS J035044.34+250705.0 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 23.890 +25 05 52.60 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 0.88 12 GCS9 Cl* Melotte 22 DH 708 0.002 0.001 0.001 0.001 0.001 0.001 +03 44 58.380 +22 11 30.10 0 15.843 15.286 14.652 14.119 13.760 13.765 15.85 2.52 -42.05 2.52 0.88 12 GCS9 UGCS J034458.38+221130.0 0.006 0.005 0.004 0.004 0.004 0.003 +03 43 56.700 +25 15 43.90 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.86 12 GCS9 Cl* Melotte 22 SK 596 0.001 0.001 0.001 0.001 0.001 +03 54 28.110 +23 56 36.00 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 313 0.006 0.004 0.004 0.004 0.004 0.002 +03 39 13.330 +25 43 49.50 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.90 12 GCS9 Cl* Melotte 22 DH 100 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 36.410 +25 13 40.60 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 717 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 02.640 +25 06 14.90 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 736 0.003 0.003 0.002 0.002 0.002 0.001 +03 55 27.060 +25 14 45.80 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 0.60 12 GCS9 Cl* Melotte 22 BPL 328 0.008 0.005 0.004 0.004 0.004 0.002 +03 36 44.120 +22 01 38.80 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.85 12 GCS9 Cl* Melotte 22 DH 63 0.004 0.003 0.003 0.002 0.002 0.002 +04 01 11.370 +22 10 37.80 0 15.142 14.644 14.046 13.493 13.176 13.178 24.34 3.41 -43.11 3.41 0.60 12 GCS9 UGCS J040111.36+221037.8 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 57.000 +23 57 05.70 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 0.90 12 GCS9 V* V739 Tau 0.003 0.002 0.002 0.001 0.001 +03 44 19.690 +23 53 45.60 0 15.780 15.031 14.317 13.697 13.262 15.52 2.31 -45.09 2.31 0.85 12 GCS9 Cl* Melotte 22 PPL 5 0.006 0.004 0.004 0.003 0.002 +03 44 12.140 +23 52 37.30 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 0.87 12 GCS9 Cl* Melotte 22 HHJ 217 0.003 0.003 0.002 0.002 0.001 +03 44 14.650 +23 49 40.00 1 15.892 15.245 14.547 13.957 13.546 14.46 2.31 -40.15 2.31 0.79 12 GCS9 Cl* Melotte 22 PPL 11 0.006 0.005 0.004 0.004 0.002 +03 37 41.350 +22 17 19.00 0 15.026 14.524 13.892 13.401 13.008 13.055 19.22 2.94 -40.01 2.94 0.86 12 GCS9 UGCS J033741.35+221718.9 0.004 0.003 0.003 0.002 0.003 0.002 +03 39 42.720 +23 54 27.60 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 0.88 12 GCS9 V* V489 Tau 0.002 0.002 0.002 0.002 0.002 0.001 +03 40 18.850 +23 54 23.30 0 14.517 14.091 13.554 13.046 12.719 12.723 16.56 2.50 -42.50 2.50 0.93 12 GCS9 UGCS J034018.84+235423.3 0.003 0.003 0.002 0.002 0.002 0.001 +03 39 47.990 +23 50 56.60 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 115 0.002 0.002 0.002 0.001 0.001 0.001 +04 04 34.790 +22 02 46.20 0 14.508 14.023 13.423 12.876 12.548 12.543 20.21 5.07 -43.45 5.07 0.94 12 GCS9 UGCS J040434.79+220246.2 0.003 0.002 0.002 0.002 0.001 0.001 +03 39 48.510 +23 46 03.80 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 0.83 12 GCS9 Cl* Melotte 22 BPL 6 0.004 0.003 0.003 0.003 0.003 0.002 +03 39 50.680 +23 45 52.30 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 0.89 12 GCS9 Cl* Melotte 22 DH 118 0.005 0.004 0.004 0.004 0.004 0.002 +03 58 01.970 +23 53 54.50 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 862 0.004 0.003 0.003 0.002 0.002 0.002 +03 59 31.470 +21 16 18.30 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.87 12 GCS9 Cl* Melotte 22 DH 879 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 48.440 +22 10 48.30 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 0.71 12 GCS9 V* V877 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 01.840 +25 04 19.50 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 0.93 12 GCS9 Cl* Melotte 22 HHJ 102 0.004 0.003 0.003 0.002 0.002 0.001 +03 40 31.170 +25 08 52.80 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 0.93 12 GCS9 V* KV Tau 0.002 0.002 0.001 0.001 0.001 0.001 +03 48 05.870 +23 53 00.90 0 15.251 14.707 14.138 13.612 13.253 13.280 17.10 2.22 -37.49 2.22 0.71 12 GCS9 UGCS J034805.86+235300.8 0.004 0.003 0.003 0.002 0.003 0.002 +03 47 29.590 +23 52 49.30 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 8 0.008 0.006 0.005 0.004 0.005 0.003 +03 47 31.650 +23 52 19.10 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 0.82 12 GCS9 UGCS J034731.64+235219.0 0.003 0.003 0.003 0.002 0.002 0.001 +04 01 49.170 +22 10 05.00 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.83 12 GCS9 Cl* Melotte 22 DH 901 0.002 0.002 0.002 0.001 0.001 0.001 +03 38 27.520 +25 30 18.10 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 0.69 12 GCS9 Cl* Melotte 22 HHJ 2 0.011 0.007 0.007 0.005 0.006 0.004 +03 38 24.220 +25 19 49.20 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 86 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 39.120 +22 04 23.10 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 0.67 12 GCS9 Cl* Melotte 22 BPL 75 0.005 0.004 0.003 0.003 0.003 0.002 +03 37 54.790 +25 26 31.30 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.83 12 GCS9 Cl* Melotte 22 HHJ 402 0.002 0.001 0.001 0.001 0.001 0.001 +03 54 58.030 +25 14 29.00 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 0.82 12 GCS9 Cl* Melotte 22 DH 810 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 31.950 +22 08 47.50 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 0.75 12 GCS9 V* V674 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 57 49.370 +22 08 30.90 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.72 12 GCS9 Cl* Melotte 22 DH 858 0.007 0.005 0.005 0.005 0.003 0.004 +03 44 02.500 +21 13 15.80 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.85 12 GCS9 Cl* Melotte 22 DH 307 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 25.160 +23 55 41.80 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 0.90 12 GCS9 V* V373 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 12.460 +23 55 35.90 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 659 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 02.180 +23 51 44.60 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 0.81 12 GCS9 V* V368 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 12.600 +23 48 48.90 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 0.77 12 GCS9 Cl* Melotte 22 DH 660 0.005 0.004 0.004 0.003 0.003 0.002 +03 57 42.980 +25 23 06.70 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 0.92 12 GCS9 V* V583 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 16.610 +23 50 01.50 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.92 12 GCS9 Cl* Melotte 22 DH 258 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 04.480 +24 14 39.60 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 739 0.003 0.003 0.003 0.002 0.002 0.001 +03 36 11.070 +23 48 23.00 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 0.61 12 GCS9 Cl* Melotte 22 DH 53 0.004 0.003 0.003 0.003 0.003 0.002 +03 34 54.950 +22 04 46.20 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.66 12 GCS9 Cl* Melotte 22 DH 40 0.001 0.001 0.001 0.001 0.001 0.001 +03 44 59.480 +23 21 18.10 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 365 0.005 0.004 0.004 0.003 0.003 0.002 +03 45 12.160 +23 21 52.90 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 0.93 12 GCS9 V* V442 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 58.020 +23 24 31.00 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 0.79 12 GCS9 Cl* Melotte 22 DH 362 0.003 0.002 0.002 0.001 0.001 0.001 +03 45 08.410 +23 25 00.90 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 0.79 12 GCS9 UGCS J034508.41+232500.9 0.005 0.004 0.004 0.003 0.003 0.002 +03 45 54.990 +24 13 26.00 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 82 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 36.280 +23 30 10.90 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 0.93 12 GCS9 V* NT Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 45 40.250 +24 17 20.60 0 14.722 14.287 13.746 13.164 12.893 12.881 22.35 2.22 -39.61 2.22 0.84 12 GCS9 UGCS J034540.24+241720.5 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 51.330 +24 17 44.30 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 0.75 12 GCS9 UGCS J034551.33+241744.2 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 30.230 +24 18 45.30 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 0.77 12 GCS9 Cl* Melotte 22 MT 59 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 49.940 +23 19 44.70 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 0.66 12 GCS9 UGCS J034549.94+231944.7 0.006 0.005 0.004 0.003 0.004 0.003 +03 45 42.870 +23 20 12.20 0 15.145 14.690 14.081 13.551 13.217 13.217 17.99 2.24 -40.55 2.24 0.88 12 GCS9 UGCS J034542.87+232012.1 0.004 0.003 0.003 0.002 0.003 0.002 +03 45 37.790 +24 20 08.10 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 0.93 12 GCS9 UGCS J034537.78+242008.1 0.002 0.002 0.001 0.001 0.000 0.001 +03 45 37.950 +24 20 03.40 0 13.151 12.791 12.328 11.940 11.503 11.541 16.88 2.22 -43.71 2.22 0.93 12 GCS9 UGCS J034537.95+242003.4 0.002 0.001 0.001 0.001 0.001 0.001 +03 45 24.790 +24 20 45.30 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 392 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 07.510 +24 22 27.60 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.89 12 GCS9 V* V638 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 41 24.690 +25 23 05.80 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 117 0.004 0.003 0.003 0.002 0.003 0.002 +03 52 05.830 +24 17 31.00 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 0.67 12 GCS9 2MASS J03520581+2417314 0.009 0.007 0.006 0.006 0.007 0.004 +03 52 44.480 +24 20 59.30 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 766 0.004 0.003 0.003 0.002 0.003 0.002 +03 45 09.040 +25 22 29.70 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 81 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 05.320 +25 29 10.90 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 373 0.002 0.001 0.001 0.001 0.001 0.001 +03 45 01.210 +25 21 05.60 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 367 0.004 0.003 0.003 0.002 0.002 0.002 +03 38 02.050 +24 20 15.10 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 81 0.002 0.002 0.002 0.002 0.001 0.001 +03 44 32.330 +25 25 17.90 0 16.994 16.209 15.450 14.883 14.476 14.459 14.24 2.27 -43.51 2.27 0.64 12 GCS9 2MASS J03443231+2525181 0.012 0.009 0.008 0.007 0.007 0.005 +03 48 48.790 +23 24 48.00 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 0.64 12 GCS9 Cl* Melotte 22 HHJ 86 0.004 0.003 0.003 0.003 0.002 0.002 +03 48 15.250 +23 26 05.40 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 0.89 12 GCS9 Cl* Melotte 22 HHJ 232 0.003 0.002 0.002 0.002 0.001 0.001 +03 49 01.500 +24 11 38.30 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 0.81 12 GCS9 Cl* Melotte 22 DH 599 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 35.490 +24 12 03.00 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 0.85 12 GCS9 V* V352 Tau 0.004 0.003 0.003 0.002 0.003 0.002 +03 48 39.910 +24 12 42.70 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 0.87 12 GCS9 V* V873 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 22.710 +23 27 42.80 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 573 0.003 0.003 0.003 0.002 0.002 0.001 +03 48 25.240 +24 14 25.80 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 0.94 12 GCS9 V* V871 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 14.300 +24 15 50.50 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 0.69 12 GCS9 Cl* Melotte 22 BPL 169 0.010 0.007 0.006 0.005 0.006 0.004 +03 48 31.060 +24 16 53.10 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 0.79 12 GCS9 V* V349 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 57.360 +24 19 43.60 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 0.89 12 GCS9 V* V759 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 26.560 +22 31 32.00 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 0.67 12 GCS9 Cl* Melotte 22 DH 393 0.003 0.002 0.002 0.001 0.002 0.001 +03 48 55.650 +24 21 40.10 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 0.70 12 GCS9 Cl* Melotte 22 HHJ 8 0.007 0.005 0.005 0.004 0.005 0.003 +03 37 56.420 +23 22 56.60 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.65 12 GCS9 Cl* Melotte 22 DH 80 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 00.390 +24 13 35.00 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.89 12 GCS9 Cl* Melotte 22 BPL 17 0.003 0.003 0.003 0.002 0.003 0.001 +03 41 22.450 +24 23 51.60 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 0.90 12 GCS9 Cl* Melotte 22 DH 169 0.004 0.004 0.003 0.003 0.003 0.002 +03 50 37.420 +22 28 08.00 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 0.87 12 GCS9 V* V878 Tau 0.002 0.002 0.002 0.002 0.002 0.001 +03 56 52.910 +23 25 43.70 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 846 0.003 0.002 0.002 0.001 0.001 0.001 +03 46 52.610 +23 38 43.00 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 473 0.003 0.002 0.002 0.001 0.001 0.001 +03 46 43.190 +23 37 21.90 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 466 0.004 0.003 0.003 0.002 0.002 0.002 +03 46 44.880 +23 37 07.30 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 0.83 12 GCS9 UGCS J034644.87+233707.2 0.005 0.003 0.003 0.002 0.002 0.001 +03 47 03.780 +23 36 58.60 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 0.80 12 GCS9 V* QQ Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 58.170 +23 33 38.80 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 482 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 02.350 +23 32 36.00 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 487 0.006 0.004 0.004 0.003 0.003 0.002 +03 46 50.090 +23 31 56.10 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 0.94 12 GCS9 V* PU Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 56 25.930 +24 16 51.30 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.83 12 GCS9 Cl* Melotte 22 SK 18 0.001 0.001 0.001 0.001 0.001 0.001 +03 58 57.040 +23 42 31.10 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.82 12 GCS9 V* V405 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 54 01.120 +23 19 41.00 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 299 0.006 0.005 0.005 0.004 0.004 0.003 +03 54 13.020 +23 20 50.80 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 0.94 12 GCS9 V* V400 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 42 02.870 +24 12 36.00 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.61 12 GCS9 V* V497 Tau 0.002 0.002 0.001 0.001 0.001 +03 48 07.960 +23 44 23.50 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.92 12 GCS9 V* V658 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 22.680 +23 44 06.70 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 0.92 12 GCS9 V* V537 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 44.660 +23 42 03.10 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 536 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 33.460 +23 41 32.80 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.88 12 GCS9 V* QV Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 47 51.970 +23 39 48.00 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 540 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 26.770 +23 38 02.40 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 0.93 12 GCS9 V* V864 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 29.930 +23 33 15.00 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 0.74 12 GCS9 Cl* Melotte 22 HHJ 16 0.007 0.005 0.005 0.003 0.004 0.003 +03 43 39.050 +23 44 05.20 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 0.93 12 GCS9 V* MP Tau 0.003 0.002 0.002 0.001 0.001 +03 43 39.720 +23 41 32.40 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.86 12 GCS9 Cl* Melotte 22 HHJ 40 0.005 0.004 0.004 0.003 0.002 +03 43 37.110 +23 38 31.90 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.91 12 GCS9 Cl* Melotte 22 DH 278 0.002 0.002 0.002 0.001 0.001 +03 44 16.460 +23 37 04.00 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 0.93 12 GCS9 V* MW Tau 0.002 0.002 0.002 0.001 0.001 +03 44 47.840 +24 12 52.50 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 0.94 12 GCS9 V* NU Tau 0.003 0.002 0.002 0.001 0.002 0.001 +03 44 27.500 +24 14 17.00 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 341 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 13.140 +24 15 23.60 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 0.86 12 GCS9 V* NZ Tau 0.004 0.003 0.003 0.002 0.002 0.002 +04 02 49.970 +23 30 38.40 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.81 12 GCS9 Cl* Melotte 22 DH 904 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 46.140 +24 23 02.80 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 24 0.006 0.005 0.004 0.003 0.004 0.003 +03 42 27.310 +22 34 24.60 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 0.90 12 GCS9 V* V612 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 08.690 +24 24 09.30 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 0.74 12 GCS9 UGCS J034508.68+242409.3 0.009 0.006 0.006 0.005 0.006 0.004 +03 57 08.610 +20 07 42.70 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.90 12 GCS9 Cl* Melotte 22 DH 849 0.005 0.005 0.004 0.003 0.004 0.003 +03 51 11.880 +23 44 43.30 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 0.89 12 GCS9 Cl* Melotte 22 BPL 232 0.005 0.004 0.003 0.002 0.003 0.002 +03 51 03.280 +23 35 00.10 0 15.395 14.846 14.243 13.676 13.365 13.342 15.36 2.22 -44.88 2.22 0.85 12 GCS9 UGCS J035103.27+233500.0 0.005 0.004 0.003 0.002 0.003 0.002 +03 51 51.550 +23 34 49.10 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 0.89 12 GCS9 2MASS J03515154+2334494 0.005 0.004 0.003 0.003 0.003 0.002 +03 40 32.380 +19 54 30.00 0 15.279 14.784 14.160 13.581 13.241 13.222 13.86 5.02 -39.74 5.02 0.73 12 GCS9 UGCS J034032.38+195429.9 0.004 0.003 0.003 0.003 0.002 0.002 +03 42 34.030 +23 24 51.80 0 15.870 15.322 14.668 14.108 13.758 13.737 14.90 2.26 -38.79 2.26 0.74 12 GCS9 UGCS J034234.02+232451.8 0.006 0.005 0.004 0.004 0.004 0.003 +03 42 36.270 +23 22 04.70 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 0.73 12 GCS9 V* V433 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 56 09.600 +22 28 01.40 0 14.827 14.311 13.669 13.091 12.772 12.772 24.94 2.51 -41.87 2.51 0.72 12 GCS9 2MASS J03560958+2228017 0.004 0.003 0.003 0.002 0.002 0.001 +03 43 01.400 +23 29 30.40 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 0.94 12 GCS9 UGCS J034301.40+232930.4 0.004 0.003 0.003 0.002 0.002 0.002 +03 39 38.690 +23 23 20.20 0 14.410 14.024 13.460 12.927 12.628 12.603 18.87 2.98 -38.42 2.98 0.86 12 GCS9 UGCS J033938.69+232320.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 41.120 +23 28 23.30 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.70 12 GCS9 Cl* Melotte 22 HHJ 83 0.004 0.003 0.003 0.002 0.002 0.002 +03 39 57.350 +23 19 42.20 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.61 12 GCS9 Cl* Melotte 22 HHJ 103 0.004 0.003 0.003 0.002 0.002 0.002 +03 40 00.190 +23 26 05.70 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 0.85 12 GCS9 V* KU Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 53 30.760 +19 54 24.10 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.91 12 GCS9 Cl* Melotte 22 DH 787 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 26.270 +23 21 29.10 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.71 12 GCS9 Cl* Melotte 22 HHJ 167 0.003 0.003 0.002 0.002 0.002 0.001 +03 51 06.130 +22 38 00.60 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 0.82 12 GCS9 Cl* Melotte 22 DH 694 0.004 0.003 0.003 0.002 0.002 0.002 +03 54 22.490 +23 38 11.90 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 801 0.003 0.002 0.002 0.002 0.002 0.001 +03 54 02.730 +23 35 00.90 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 0.80 12 GCS9 Cl* Melotte 22 BPL 301 0.004 0.003 0.003 0.002 0.002 0.002 +03 51 07.110 +23 20 57.60 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 695 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 22.280 +24 16 59.90 0 16.097 15.485 14.840 14.281 13.922 13.904 20.93 2.23 -44.36 2.23 0.64 12 GCS9 UGCS J034722.27+241659.8 0.007 0.005 0.004 0.003 0.004 0.003 +03 47 22.380 +24 14 18.80 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 0.81 12 GCS9 Cl* Melotte 22 BPL 139 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 25.110 +24 15 17.20 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 0.93 12 GCS9 Cl* Melotte 22 BPL 143 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 29.480 +24 12 19.70 0 15.420 14.860 14.256 13.725 13.372 13.384 17.13 2.22 -47.51 2.22 0.76 12 GCS9 UGCS J034729.48+241219.7 0.005 0.003 0.003 0.002 0.003 0.002 +03 47 30.600 +24 22 13.80 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 0.94 12 GCS9 V* QT Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 39.080 +23 22 04.30 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 237 0.004 0.003 0.003 0.002 0.002 0.002 +03 52 05.580 +22 34 55.10 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 740 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 34.480 +22 30 07.50 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 0.63 12 GCS9 V* V395 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 22.470 +23 29 08.00 0 14.962 14.417 13.743 13.146 12.784 12.747 19.14 2.24 -43.87 2.24 0.94 12 GCS9 UGCS J034622.46+232908.0 0.004 0.003 0.003 0.002 0.002 0.001 +03 52 51.720 +22 31 32.70 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 0.94 12 GCS9 V* V568 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 15.380 +23 26 05.80 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 0.92 12 GCS9 Cl* Melotte 22 HHJ 203 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 56.970 +22 26 01.10 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.92 12 GCS9 V* V883 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 43 43.530 +24 12 50.00 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 284 0.005 0.004 0.004 0.003 0.002 +03 47 22.030 +23 21 36.30 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 0.93 12 GCS9 UGCS J034722.02+232136.3 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 28.120 +23 26 53.40 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 0.92 12 GCS9 Cl* Melotte 22 MSK 140 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 32.190 +23 18 23.30 0 14.313 13.888 13.351 12.830 12.511 12.509 18.09 2.24 -46.67 2.24 0.90 12 GCS9 UGCS J034732.19+231823.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 51.760 +24 14 15.90 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 0.90 12 GCS9 V* V847 Tau 0.003 0.002 0.002 0.002 0.001 +03 43 52.770 +24 18 48.40 0 16.556 15.907 15.211 14.628 14.221 19.79 2.32 -39.76 2.32 0.64 12 GCS9 UGCS J034352.77+241848.4 0.009 0.007 0.006 0.005 0.004 +03 47 36.040 +23 28 26.70 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 526 0.004 0.003 0.003 0.002 0.003 0.002 +03 43 57.290 +24 13 20.30 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 0.72 12 GCS9 V* V511 Tau 0.003 0.002 0.002 0.001 0.001 +04 00 08.160 +22 32 01.10 1 16.449 15.820 15.102 14.503 14.098 14.104 16.31 2.88 -39.63 2.88 0.64 12 GCS9 UGCS J040008.16+223201.0 0.008 0.006 0.005 0.006 0.004 0.004 +03 48 04.980 +23 24 13.40 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 0.89 12 GCS9 Cl* Melotte 22 DH 546 0.007 0.005 0.005 0.004 0.004 0.003 +03 39 55.210 +24 12 54.10 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 0.86 12 GCS9 Cl* Melotte 22 MHO 2 0.005 0.004 0.003 0.003 0.003 0.002 +03 58 56.150 +23 27 53.50 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.81 12 GCS9 Cl* Melotte 22 DH 868 0.002 0.001 0.001 0.001 0.001 0.001 +03 40 07.110 +24 13 03.30 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.90 12 GCS9 Cl* Melotte 22 DH 126 0.005 0.004 0.004 0.003 0.003 0.002 +03 59 07.060 +22 27 48.20 0 14.565 14.030 13.393 12.824 12.466 12.480 16.23 2.84 -40.81 2.84 0.91 12 GCS9 UGCS J035907.05+222748.1 0.003 0.002 0.002 0.002 0.001 0.001 +03 59 18.990 +23 31 23.90 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.92 12 GCS9 Cl* Melotte 22 DH 874 0.004 0.003 0.003 0.002 0.002 0.001 +03 40 26.950 +24 14 14.20 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 140 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 59.540 +24 11 45.60 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 220 0.005 0.004 0.004 0.003 0.003 0.002 +03 50 27.350 +23 22 44.90 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 12 GCS9 Cl* Melotte 22 DH 670 0.003 0.002 0.002 0.002 0.001 0.001 +03 50 12.870 +24 21 06.20 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 0.75 12 GCS9 UGCS J035012.86+242106.2 0.001 0.001 0.001 0.001 0.001 0.000 +03 50 13.000 +24 21 07.00 0 13.248 12.864 12.329 11.821 11.509 11.534 20.58 2.22 -44.46 2.22 0.92 12 GCS9 UGCS J035012.99+242106.9 0.002 0.001 0.001 0.001 0.001 0.001 +03 50 15.270 +24 13 36.00 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.94 12 GCS9 V* V469 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 19.150 +24 16 34.00 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 0.67 12 GCS9 Cl* Melotte 22 BPL 228 0.008 0.006 0.005 0.004 0.005 0.003 +03 50 42.150 +23 29 33.10 0 15.510 14.987 14.385 13.858 13.532 13.543 21.92 2.14 -45.96 2.14 0.74 12 GCS9 UGCS J035042.14+232933.0 0.005 0.004 0.004 0.003 0.003 0.002 +03 50 33.080 +24 20 21.60 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 674 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 42.440 +24 12 55.40 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 683 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 54.650 +24 21 55.70 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 687 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 22.460 +22 31 10.80 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 140 0.005 0.004 0.004 0.003 0.003 0.002 +03 46 55.320 +23 22 49.30 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 479 0.004 0.003 0.003 0.002 0.003 0.002 +03 40 01.870 +24 46 25.90 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.94 12 GCS9 V* V598 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 09.520 +23 25 56.30 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 498 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 05.720 +22 38 09.30 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 0.82 12 GCS9 V* V341 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 08.840 +23 35 16.80 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 0.72 12 GCS9 Cl* Melotte 22 DH 212 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 32.320 +24 16 01.10 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.91 12 GCS9 V* V597 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 25.330 +22 32 55.80 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.89 12 GCS9 V* V492 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 59.150 +23 41 04.60 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.83 12 GCS9 Cl* Melotte 22 DH 883 0.004 0.003 0.003 0.003 0.002 +03 59 56.490 +23 40 15.30 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.69 12 GCS9 Cl* Melotte 22 DH 882 0.006 0.005 0.004 0.006 0.003 +04 03 49.570 +23 43 13.70 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.90 12 GCS9 Cl* Melotte 22 DH 910 0.003 0.003 0.003 0.002 0.002 0.001 +04 01 13.990 +23 10 29.90 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.77 12 GCS9 Cl* Melotte 22 DH 899 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 16.420 +23 34 01.60 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 384 0.006 0.004 0.004 0.003 0.003 0.002 +03 45 06.790 +23 36 51.40 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 0.92 12 GCS9 V* V516 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 44 56.690 +23 36 23.50 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 361 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 51.260 +23 34 20.60 0 16.524 15.857 15.186 14.615 14.212 14.214 20.98 2.24 -41.95 2.24 0.66 12 GCS9 UGCS J034451.26+233420.5 0.009 0.006 0.006 0.005 0.005 0.004 +03 44 35.900 +23 34 41.90 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 0.72 12 GCS9 Cl* Melotte 22 HHJ 5 0.008 0.006 0.005 0.004 0.004 0.003 +03 44 31.720 +23 35 26.00 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 345 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 01.160 +23 38 15.50 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 598 0.005 0.004 0.004 0.003 0.003 0.002 +03 36 22.330 +22 44 32.70 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 0.85 12 GCS9 Cl* Melotte 22 DH 55 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 19.840 +23 36 11.80 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 568 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 16.090 +23 35 15.20 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 564 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 15.270 +23 42 03.40 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 562 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 13.780 +23 37 59.30 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 0.94 12 GCS9 V* V458 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 08.960 +23 42 23.30 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 552 0.003 0.002 0.002 0.002 0.002 0.001 +03 41 02.990 +23 43 21.40 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.71 12 GCS9 V* V603 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 38 13.040 +23 37 20.70 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 0.84 12 GCS9 Cl* Melotte 22 HHJ 32 0.005 0.004 0.004 0.003 0.003 0.002 +03 41 43.720 +23 07 59.70 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 0.60 12 GCS9 UGCS J034143.72+230759.7 0.009 0.006 0.006 0.005 0.006 0.004 +03 42 15.370 +23 11 30.90 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 0.81 12 GCS9 Cl* Melotte 22 HHJ 109 0.004 0.003 0.003 0.002 0.002 0.002 +03 54 25.040 +24 42 43.40 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 0.94 12 GCS9 V* V401 Tau 0.004 0.003 0.003 0.002 0.002 0.001 +03 54 24.390 +22 41 20.50 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 0.92 12 GCS9 UGCS J035424.39+224120.5 0.004 0.003 0.003 0.002 0.002 0.002 +03 49 58.340 +23 42 33.70 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 0.93 12 GCS9 Cl* Melotte 22 MSK 211 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 57.640 +23 43 28.20 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 644 0.005 0.004 0.003 0.002 0.003 0.002 +04 01 39.830 +22 47 53.70 0 16.669 15.899 15.153 14.575 14.169 14.157 21.16 3.39 -42.62 3.39 0.65 12 GCS9 UGCS J040139.83+224753.7 0.009 0.006 0.006 0.006 0.006 0.004 +03 49 31.220 +23 41 19.60 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 625 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 29.810 +23 38 55.20 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 624 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 21.500 +23 39 06.30 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 0.82 12 GCS9 V* V551 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 12.670 +23 35 13.60 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 0.94 12 GCS9 UGCS J034612.66+233513.5 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 27.520 +23 37 56.90 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 0.90 12 GCS9 Cl* Melotte 22 HHJ 106 0.004 0.003 0.003 0.002 0.002 0.002 +03 31 33.200 +27 32 49.10 0 14.985 14.331 13.622 13.095 12.730 20.59 6.93 -48.45 6.93 0.77 12 GCS9 UGCS J033133.20+273249.0 0.003 0.002 0.002 0.002 0.001 +03 48 23.920 +23 08 08.10 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 0.83 12 GCS9 Cl* Melotte 22 DH 576 0.004 0.003 0.003 0.003 0.002 0.002 +03 48 40.990 +23 14 17.20 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 0.92 12 GCS9 V* V875 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 15.430 +22 46 19.90 0 16.872 16.103 15.397 14.842 14.414 14.387 15.23 2.19 -42.79 2.19 0.70 12 GCS9 UGCS J035015.43+224619.8 0.011 0.008 0.007 0.007 0.006 0.005 +03 42 56.550 +22 51 17.90 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 0.78 12 GCS9 UGCS J034256.54+225117.8 0.006 0.005 0.005 0.004 0.004 0.003 +03 43 04.200 +22 48 03.30 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 0.90 12 GCS9 V* V435 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 42 29.430 +22 47 25.90 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 0.90 12 GCS9 V* V613 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 43 19.010 +22 47 10.40 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 0.89 12 GCS9 V* V621 Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 41 26.360 +23 08 02.70 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 0.92 12 GCS9 Cl* Melotte 22 DH 174 0.003 0.002 0.003 0.002 0.002 0.001 +03 40 51.820 +23 13 50.50 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 0.93 12 GCS9 V* V601 Tau 0.002 0.002 0.002 0.002 0.002 0.001 +03 41 25.970 +23 15 35.80 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 0.94 12 GCS9 UGCS J034125.96+231535.8 0.003 0.003 0.003 0.002 0.002 0.001 +03 43 27.440 +22 37 41.00 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 0.67 12 GCS9 V* LX Tau 0.004 0.003 0.003 0.003 0.003 0.002 +03 39 44.790 +22 39 15.30 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.94 12 GCS9 Cl* Melotte 22 DH 112 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 07.010 +22 38 47.50 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.89 12 GCS9 Cl* Melotte 22 HHJ 114 0.004 0.003 0.003 0.002 0.002 0.002 +03 34 35.570 +23 38 43.30 0 15.561 15.064 14.437 13.919 13.538 13.570 17.78 2.61 -36.55 2.61 0.60 12 GCS9 UGCS J033435.57+233843.3 0.005 0.004 0.004 0.003 0.004 0.002 +03 50 25.190 +22 40 31.70 0 15.262 14.699 14.109 13.541 13.204 13.220 18.66 2.13 -45.07 2.13 0.88 12 GCS9 UGCS J035025.18+224031.7 0.005 0.003 0.003 0.003 0.002 0.002 +03 50 04.250 +23 10 44.50 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 0.84 12 GCS9 Cl* Melotte 22 DH 649 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 41.140 +23 14 59.30 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 0.83 12 GCS9 UGCS J034941.13+231459.2 0.004 0.003 0.003 0.003 0.002 0.002 +03 49 32.160 +23 16 17.90 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 0.86 12 GCS9 Cl* Melotte 22 DH 626 0.005 0.004 0.003 0.003 0.003 0.002 +03 56 24.990 +23 05 26.60 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 0.72 12 GCS9 Cl* Melotte 22 HHJ 419 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 28.900 +27 28 07.20 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.88 12 GCS9 Cl* Melotte 22 DH 394 0.001 0.001 0.001 0.001 0.001 0.001 +03 36 24.180 +22 37 24.30 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 0.93 12 GCS9 UGCS J033624.18+223724.3 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 36.570 +23 12 34.10 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 0.79 12 GCS9 V* MN Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 09.320 +23 08 46.80 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 0.91 12 GCS9 Cl* Melotte 22 DH 313 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 22.140 +23 10 54.80 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 329 0.006 0.004 0.004 0.003 0.003 0.002 +03 45 24.700 +24 38 46.40 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 0.89 12 GCS9 UGCS J034524.69+243846.4 0.006 0.004 0.004 0.003 0.004 0.002 +03 45 06.560 +24 40 42.70 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 0.71 12 GCS9 Cl* Melotte 22 HHJ 48 0.005 0.004 0.003 0.002 0.003 0.002 +03 45 08.760 +24 50 31.50 0 13.942 13.474 12.930 12.429 12.086 12.117 17.85 2.21 -41.29 2.21 0.93 12 GCS9 UGCS J034508.76+245031.4 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 16.530 +23 11 04.20 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.84 12 GCS9 Cl* Melotte 22 DH 69 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 01.140 +24 46 40.90 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 0.71 12 GCS9 V* V744 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +04 06 28.550 +22 36 59.60 0 14.067 13.659 13.076 12.477 12.156 12.133 17.57 5.07 -40.55 5.07 0.92 12 GCS9 UGCS J040628.54+223659.6 0.002 0.002 0.002 0.002 0.001 0.001 +03 58 34.180 +22 40 11.10 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 0.81 12 GCS9 Cl* Melotte 22 DH 867 0.004 0.004 0.003 0.002 0.002 0.002 +03 50 08.620 +24 40 17.70 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 0.90 12 GCS9 Cl* Melotte 22 DH 655 0.005 0.003 0.003 0.002 0.003 0.002 +03 49 55.790 +24 44 31.30 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 0.92 12 GCS9 V* V364 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 31 49.860 +22 50 24.50 0 14.569 14.083 13.494 12.998 12.676 12.690 25.11 3.42 -44.59 3.42 0.68 12 GCS9 UGCS J033149.85+225024.4 0.003 0.002 0.002 0.002 0.002 0.001 +03 32 07.870 +23 13 57.20 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.84 12 GCS9 Cl* Melotte 22 DH 17 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 26.390 +24 34 01.20 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.93 12 GCS9 Cl* Melotte 22 DH 71 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 44.370 +23 06 16.10 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 0.75 12 GCS9 Cl* Melotte 22 DH 240 0.003 0.003 0.003 0.002 0.002 0.001 +03 42 51.680 +23 08 43.70 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 0.92 12 GCS9 UGCS J034251.68+230843.6 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 21.250 +24 41 40.80 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 615 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 39.310 +24 50 19.90 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 585 0.005 0.004 0.003 0.003 0.003 0.002 +03 54 37.360 +23 13 32.50 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 0.81 12 GCS9 Cl* Melotte 22 DH 805 0.003 0.002 0.002 0.002 0.002 0.001 +03 51 23.820 +22 50 29.10 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 0.88 12 GCS9 Cl* Melotte 22 DH 706 0.001 0.001 0.001 0.001 0.001 0.000 +03 39 43.320 +23 12 25.30 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.67 12 GCS9 Cl* Melotte 22 DH 110 0.004 0.003 0.003 0.003 0.002 0.002 +03 46 28.630 +24 45 32.10 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 0.81 12 GCS9 V* V446 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 17.940 +24 41 09.30 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 433 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 08.700 +24 40 33.10 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 0.93 12 GCS9 V* V857 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 02.960 +24 40 55.60 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 414 0.005 0.003 0.003 0.002 0.002 0.002 +03 45 36.720 +24 39 06.50 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 0.85 12 GCS9 V* V443 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 50 18.220 +24 35 15.30 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 0.85 12 GCS9 Cl* Melotte 22 DH 665 0.003 0.003 0.003 0.002 0.002 0.001 +03 50 10.770 +24 28 41.40 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 656 0.004 0.003 0.003 0.002 0.002 0.001 +03 49 33.040 +24 32 02.40 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 0.79 12 GCS9 V* V361 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 27.010 +24 27 13.90 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 0.82 12 GCS9 Cl* Melotte 22 DH 447 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 24.640 +24 28 46.20 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 0.93 12 GCS9 V* V1275 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 24.120 +24 30 12.70 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 101 0.006 0.005 0.004 0.003 0.004 0.003 +03 46 23.030 +24 36 17.90 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 442 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 21.360 +24 33 52.20 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 439 0.004 0.003 0.003 0.002 0.002 0.001 +03 56 57.080 +24 48 34.30 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 0.89 12 GCS9 V* V693 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 05.640 +24 36 44.30 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 0.93 12 GCS9 Cl* Melotte 22 SK 488 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 05.690 +24 36 49.70 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 0.87 12 GCS9 UGCS J034605.69+243649.7 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 51.090 +24 26 10.90 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 25 0.006 0.005 0.004 0.003 0.004 0.002 +03 45 49.370 +24 25 07.10 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 77 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 35.690 +24 24 34.10 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 0.74 12 GCS9 UGCS J034535.69+242434.1 0.009 0.006 0.005 0.004 0.005 0.003 +03 41 59.670 +24 42 18.30 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 0.65 12 GCS9 Cl* Melotte 22 BPL 31 0.007 0.006 0.005 0.005 0.003 +03 52 46.450 +24 33 41.00 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 0.76 12 GCS9 Cl* Melotte 22 BPL 270 0.003 0.003 0.003 0.002 0.002 0.001 +03 52 43.240 +24 27 58.50 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 763 0.004 0.003 0.003 0.002 0.002 0.001 +03 52 30.910 +24 32 39.50 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.89 12 GCS9 V* V476 Tau 0.002 0.001 0.002 0.001 0.001 0.001 +03 52 20.660 +24 33 55.50 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 0.78 12 GCS9 V* V392 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 42 03.300 +24 32 13.30 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 0.79 12 GCS9 Cl* Melotte 22 DH 205 0.002 0.002 0.001 0.001 0.001 +03 50 38.920 +23 13 02.50 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 0.79 12 GCS9 Cl* Melotte 22 DH 678 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 50.450 +22 44 29.80 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 0.71 12 GCS9 Cl* Melotte 22 HHJ 3 0.010 0.006 0.006 0.005 0.004 0.004 +03 46 57.100 +23 15 02.20 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 0.83 12 GCS9 V* V453 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 49 15.120 +24 36 22.50 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 0.64 12 GCS9 UGCS J034915.12+243622.5 0.011 0.007 0.006 0.005 0.007 0.004 +03 48 45.350 +24 37 26.30 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 0.83 12 GCS9 V* V463 Tau 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 44.690 +24 37 23.50 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 0.75 12 GCS9 2MASS J03484468+2437237 0.008 0.005 0.005 0.004 0.005 0.003 +03 48 42.690 +24 27 19.40 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.85 12 GCS9 Cl* Melotte 22 DH 587 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 40.430 +24 36 34.00 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 0.89 12 GCS9 V* V662 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 51 19.480 +23 09 49.40 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 703 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 51.150 +23 17 41.10 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 0.93 12 GCS9 V* V802 Tau 0.002 0.002 0.001 0.001 0.001 0.001 +03 58 30.990 +23 04 12.70 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 866 0.005 0.004 0.003 0.002 0.002 0.002 +03 42 29.710 +20 48 39.60 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.82 12 GCS9 Cl* Melotte 22 DH 229 0.002 0.002 0.002 0.002 0.001 0.001 +03 43 22.550 +23 00 56.50 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 0.70 12 GCS9 UGCS J034322.55+230056.5 0.007 0.006 0.005 0.005 0.005 0.003 +03 51 19.770 +23 04 04.10 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 704 0.005 0.004 0.003 0.003 0.003 0.002 +03 51 50.600 +22 53 44.30 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 0.72 12 GCS9 V* V386 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 11.230 +20 52 33.40 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.91 12 GCS9 Cl* Melotte 22 DH 743 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 36.070 +23 04 17.20 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 0.94 12 GCS9 Cl* Melotte 22 MSK 100 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 48.790 +23 04 07.30 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 0.90 12 GCS9 V* V533 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 21.620 +23 04 00.90 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 440 0.003 0.003 0.003 0.002 0.002 0.001 +03 46 31.020 +23 01 34.60 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 453 0.006 0.004 0.004 0.003 0.004 0.003 +03 46 19.410 +23 00 55.70 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 0.80 12 GCS9 Cl* Melotte 22 DH 435 0.005 0.003 0.003 0.002 0.002 0.002 +03 46 34.800 +22 56 07.50 0 12.325 12.095 11.650 11.585 10.883 11.064 23.59 2.23 -45.09 2.23 0.61 12 GCS9 V* V750 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 48 05.830 +23 02 02.70 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 0.93 12 GCS9 V* V340 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 39.800 +23 00 04.40 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 0.88 12 GCS9 Cl* Melotte 22 HHJ 94 0.004 0.003 0.003 0.002 0.003 0.002 +03 47 52.880 +22 59 33.80 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 0.89 12 GCS9 Cl* Melotte 22 BPL 160 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 44.970 +23 03 21.10 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 0.84 12 GCS9 Cl* Melotte 22 HHJ 321 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 25.160 +22 53 44.30 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 0.85 12 GCS9 Cl* Melotte 22 DH 263 0.004 0.003 0.003 0.002 0.002 0.001 +03 50 27.830 +23 03 54.90 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 671 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 17.590 +22 55 58.80 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 0.89 12 GCS9 Cl* Melotte 22 DH 664 0.005 0.004 0.004 0.003 0.002 0.002 +03 54 55.340 +22 59 40.10 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 0.78 12 GCS9 Cl* Melotte 22 HHJ 62 0.005 0.004 0.004 0.003 0.003 0.002 +03 55 11.850 +22 58 02.50 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 0.83 12 GCS9 Cl* Melotte 22 HHJ 61 0.004 0.003 0.003 0.002 0.002 0.001 +03 40 23.840 +23 04 09.00 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.90 12 GCS9 Cl* Melotte 22 DH 135 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 49.720 +23 03 26.20 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.89 12 GCS9 Cl* Melotte 22 DH 117 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 51.080 +20 41 17.20 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.75 12 GCS9 Cl* Melotte 22 DH 155 0.005 0.004 0.004 0.003 0.004 0.003 +03 42 23.660 +27 45 56.30 0 13.845 13.397 12.838 12.326 11.997 12.001 23.57 3.28 -40.53 3.28 0.76 12 GCS9 UGCS J034223.65+274556.2 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 01.020 +22 58 49.20 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 0.84 12 GCS9 V* V548 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 50.670 +23 04 30.00 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 0.90 12 GCS9 Cl* Melotte 22 HHJ 116 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 35.200 +22 53 42.10 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 0.62 12 GCS9 V* V661 Tau 0.007 0.005 0.005 0.004 0.003 0.003 +03 48 22.660 +22 52 21.30 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 0.91 12 GCS9 V* V870 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 41 58.660 +22 57 01.70 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 0.90 12 GCS9 V* V432 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 46.660 +23 01 19.60 0 15.129 14.622 14.032 13.502 13.175 13.164 18.10 2.25 -44.32 2.25 0.89 12 GCS9 UGCS J034146.65+230119.6 0.004 0.003 0.003 0.002 0.003 0.002 +03 40 54.480 +22 54 25.50 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 159 0.004 0.003 0.003 0.002 0.002 0.001 +03 53 15.710 +22 52 14.30 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 0.93 12 GCS9 V* V681 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 10.160 +23 03 10.60 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 779 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 01.630 +22 58 48.20 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 0.94 12 GCS9 Cl* Melotte 22 DH 776 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 41.210 +22 56 40.50 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 0.69 12 GCS9 Cl* Melotte 22 BPL 213 0.009 0.006 0.005 0.005 0.005 0.004 +03 49 25.610 +23 02 49.90 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 620 0.005 0.003 0.003 0.003 0.002 0.002 +03 45 56.970 +23 01 29.00 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 0.90 12 GCS9 V* V524 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 35.270 +22 59 08.00 0 13.457 13.075 12.532 11.943 11.634 11.688 20.34 2.99 -43.22 2.99 0.93 12 GCS9 UGCS J035735.26+225907.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 29.420 +20 34 29.20 0 16.672 16.008 15.311 14.764 14.373 14.392 16.88 4.05 -38.85 4.05 0.60 12 GCS9 UGCS J035929.42+203429.1 0.008 0.007 0.006 0.006 0.007 0.005 +03 33 49.810 +22 56 19.60 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.83 12 GCS9 Cl* Melotte 22 DH 31 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 10.820 +23 02 58.00 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 0.92 12 GCS9 V* V440 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 38.950 +23 02 25.30 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 351 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 37.780 +22 55 15.50 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.88 12 GCS9 V* NS Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 51 20.680 +19 38 37.80 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.77 12 GCS9 Cl* Melotte 22 DH 705 0.002 0.001 0.002 0.001 0.001 0.001 +03 53 35.390 +21 47 05.70 0 16.517 15.849 15.160 14.619 14.236 14.231 17.16 2.55 -42.61 2.55 0.75 12 GCS9 UGCS J035335.39+214705.6 0.010 0.007 0.007 0.005 0.005 0.004 +03 42 35.650 +21 50 29.70 0 12.761 12.502 12.030 11.527 11.224 11.275 23.68 2.51 -42.67 2.51 0.75 12 GCS9 V* V614 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 08.890 +19 43 29.90 0 16.502 15.850 15.168 14.631 14.237 14.214 17.00 4.02 -40.72 4.02 0.71 12 GCS9 UGCS J034508.89+194329.8 0.007 0.006 0.006 0.005 0.006 0.004 +03 49 41.710 +21 56 19.20 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.72 12 GCS9 Cl* Melotte 22 BPL 214 0.006 0.005 0.004 0.004 0.004 0.003 +03 59 29.330 +21 39 56.00 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.79 12 GCS9 Cl* Melotte 22 DH 878 0.005 0.004 0.005 0.004 0.002 0.003 +03 59 28.420 +19 30 41.20 0 14.604 14.084 13.471 12.921 12.572 12.598 16.99 3.04 -38.71 3.04 0.86 12 GCS9 UGCS J035928.42+193041.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 01.510 +19 23 22.10 0 15.564 15.023 14.454 13.871 13.553 13.566 19.45 6.06 -38.27 6.06 0.77 12 GCS9 UGCS J035701.50+192322.0 0.004 0.003 0.004 0.003 0.003 0.002 +03 56 38.760 +21 35 08.30 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.93 12 GCS9 Cl* Melotte 22 DH 841 0.003 0.003 0.003 0.002 0.002 0.001 +04 01 26.060 +21 35 08.50 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.94 12 GCS9 Cl* Melotte 22 DH 900 0.002 0.002 0.002 0.001 0.001 0.001 +04 01 45.820 +21 44 04.90 0 16.644 15.914 15.183 14.661 14.260 14.256 18.38 3.82 -39.06 3.82 0.62 12 GCS9 UGCS J040145.82+214404.8 0.009 0.007 0.007 0.006 0.004 0.004 +03 46 16.080 +21 41 09.90 0 15.678 15.135 14.516 13.979 13.651 13.622 18.91 2.66 -43.14 2.66 0.90 12 GCS9 UGCS J034616.08+214109.8 0.005 0.004 0.004 0.003 0.004 0.002 +03 42 33.110 +21 42 16.60 0 14.159 13.678 13.082 12.475 12.163 12.158 18.09 3.58 -43.09 3.58 0.94 12 GCS9 UGCS J034233.10+214216.6 0.002 0.002 0.002 0.002 0.001 0.001 +03 35 04.720 +25 50 48.00 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.85 12 GCS9 Cl* Melotte 22 DH 41 0.006 0.004 0.004 0.004 0.002 +03 44 11.920 +19 18 19.10 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 0.77 12 GCS9 Cl* Melotte 22 DH 318 0.001 0.001 0.001 0.001 0.001 0.001 +03 32 57.570 +27 17 19.40 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.80 12 GCS9 Cl* Melotte 22 DH 24 0.005 0.003 0.004 0.003 0.003 0.002 +03 46 40.270 +25 43 53.40 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 464 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 37.820 +21 24 22.50 0 14.983 14.452 13.790 13.151 12.787 12.764 22.77 3.58 -38.56 3.58 0.76 12 GCS9 UGCS J034037.82+212422.5 0.003 0.003 0.003 0.002 0.002 0.001 +03 55 03.610 +21 31 09.60 0 15.425 14.932 14.277 13.754 13.419 13.419 21.47 3.36 -41.78 3.36 0.84 12 GCS9 UGCS J035503.61+213109.6 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 48.230 +21 24 40.50 0 14.474 14.049 13.470 12.887 12.617 12.608 22.67 3.05 -42.13 3.05 0.89 12 GCS9 UGCS J034848.23+212440.4 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 32.790 +19 17 30.20 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 0.90 12 GCS9 Cl* Melotte 22 DH 455 0.002 0.002 0.002 0.002 0.001 0.001 +03 27 54.260 +24 56 10.90 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.93 12 GCS9 Cl* Melotte 22 DH 3 0.002 0.002 0.002 0.001 0.001 +03 43 28.210 +24 53 30.90 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.87 12 GCS9 V* V436 Tau 0.002 0.002 0.002 0.001 0.001 +03 43 48.450 +25 02 36.70 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.93 12 GCS9 V* V625 Tau 0.002 0.002 0.002 0.001 0.001 +03 44 24.680 +24 51 53.20 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 0.86 12 GCS9 V* V630 Tau 0.002 0.002 0.002 0.001 0.001 +03 49 13.230 +21 22 00.80 0 15.580 15.063 14.449 13.905 13.547 13.560 21.58 3.05 -41.77 3.05 0.83 12 GCS9 UGCS J034913.22+212200.8 0.005 0.004 0.004 0.003 0.003 0.002 +04 00 33.590 +21 27 10.10 0 15.280 14.774 14.134 13.608 13.262 13.253 18.18 3.00 -39.40 3.00 0.84 12 GCS9 UGCS J040033.58+212710.0 0.004 0.003 0.004 0.003 0.003 0.002 +03 50 29.960 +25 03 06.70 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.87 12 GCS9 V* V470 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 44.440 +24 36 55.70 0 15.085 14.555 13.994 13.447 13.132 13.121 14.49 2.21 -41.75 2.21 0.83 12 GCS9 UGCS J034744.44+243655.6 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 38.370 +24 35 59.70 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 0.85 12 GCS9 UGCS J034738.36+243559.6 0.005 0.004 0.003 0.003 0.003 0.002 +03 47 59.380 +24 35 37.00 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 0.87 12 GCS9 V* V1281 Tau 0.005 0.004 0.004 0.003 0.004 0.002 +03 48 21.540 +24 34 43.40 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 0.73 12 GCS9 Cl* Melotte 22 DH 571 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 50.950 +24 30 18.60 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 0.92 12 GCS9 V* V654 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 39.360 +24 27 31.90 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 0.94 12 GCS9 V* V457 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 49.790 +24 25 43.10 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 0.87 12 GCS9 V* V866 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 37.660 +24 24 23.10 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 0.84 12 GCS9 V* V1280 Tau 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 32.140 +24 24 18.40 0 14.949 14.466 13.873 13.317 13.042 13.004 16.18 2.21 -46.43 2.21 0.88 12 GCS9 UGCS J034732.14+242418.3 0.004 0.003 0.003 0.002 0.002 0.002 +03 38 45.750 +24 28 03.70 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 0.84 12 GCS9 Cl* Melotte 22 DH 94 0.004 0.003 0.003 0.003 0.002 0.002 +03 40 24.200 +24 35 04.00 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.91 12 GCS9 V* V491 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 40 26.090 +24 32 13.00 0 14.779 14.302 13.776 13.231 12.923 12.924 16.10 2.49 -36.49 2.49 0.61 12 GCS9 UGCS J034026.08+243212.9 0.003 0.003 0.003 0.002 0.002 0.001 +03 35 57.210 +24 36 08.70 0 13.467 13.062 12.538 12.051 11.754 11.753 14.41 2.62 -44.34 2.62 0.87 12 GCS9 UGCS J033557.21+243608.7 0.002 0.001 0.001 0.001 0.001 0.001 +03 51 34.010 +24 34 08.90 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 715 0.003 0.002 0.002 0.001 0.002 0.001 +03 43 26.980 +24 27 09.60 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.92 12 GCS9 Cl* Melotte 22 DH 267 0.002 0.001 0.001 0.001 0.001 +03 43 20.580 +24 26 34.90 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.88 12 GCS9 V* V620 Tau 0.004 0.003 0.003 0.003 0.002 +03 44 09.610 +24 35 22.10 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.89 12 GCS9 Cl* Melotte 22 DH 314 0.002 0.002 0.002 0.001 0.001 +03 44 19.060 +24 35 18.20 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.90 12 GCS9 V* V512 Tau 0.003 0.002 0.002 0.002 0.001 +03 43 43.150 +24 32 56.00 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.69 12 GCS9 Cl* Melotte 22 DH 283 0.004 0.003 0.003 0.002 0.002 +03 44 26.540 +24 29 11.30 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 0.91 12 GCS9 Cl* Melotte 22 DH 338 0.003 0.002 0.002 0.001 0.001 +03 44 17.760 +24 26 46.70 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.88 12 GCS9 V* V849 Tau 0.002 0.002 0.002 0.001 0.001 +03 54 39.130 +24 35 54.50 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 0.88 12 GCS9 Cl* Melotte 22 DH 806 0.006 0.005 0.005 0.004 0.004 0.002 +03 37 37.700 +26 21 04.00 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.83 12 GCS9 Cl* Melotte 22 DH 76 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 54.990 +24 33 03.50 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 0.85 12 GCS9 Cl* Melotte 22 MBSC 69 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 03.620 +24 32 35.10 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 0.78 12 GCS9 Cl* Melotte 22 DH 691 0.003 0.002 0.002 0.001 0.002 0.001 +03 50 57.410 +24 24 44.40 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 0.83 12 GCS9 2MASS J03505739+2424447 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 15.960 +26 11 37.00 0 16.295 15.586 14.902 14.337 13.979 13.985 18.50 2.96 -41.40 2.96 0.73 12 GCS9 UGCS J035015.96+261137.0 0.008 0.006 0.005 0.005 0.005 0.003 +03 42 56.570 +24 13 45.40 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.92 12 GCS9 Cl* Melotte 22 BPL 47 0.004 0.003 0.003 0.002 0.003 0.001 +03 43 01.270 +24 14 52.20 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 0.68 12 GCS9 Cl* Melotte 22 BPL 50 0.004 0.004 0.003 0.003 0.003 0.002 +03 43 13.470 +24 11 00.90 0 15.692 15.163 14.527 14.001 13.642 13.649 16.42 2.13 -42.52 2.13 0.89 12 GCS9 UGCS J034313.46+241100.9 0.005 0.004 0.004 0.003 0.004 0.002 +03 49 35.290 +25 59 34.60 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 630 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 41.340 +26 08 53.20 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 636 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 35.520 +26 07 08.00 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.87 12 GCS9 Cl* Melotte 22 DH 790 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 43.180 +26 02 26.60 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 401 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 53.610 +24 17 14.80 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 0.85 12 GCS9 V* PV Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 55.320 +24 11 16.60 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 478 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 07.880 +24 23 37.80 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 0.80 12 GCS9 V* V1278 Tau 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 08.150 +24 18 24.50 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 0.90 12 GCS9 V* V861 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 09.420 +24 15 34.70 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 0.68 12 GCS9 Cl* Melotte 22 DH 496 0.005 0.004 0.004 0.003 0.003 0.002 +03 47 11.030 +24 13 51.50 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 0.90 12 GCS9 V* QS Tau 0.005 0.004 0.003 0.002 0.003 0.002 +03 47 11.790 +24 13 31.30 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.73 12 GCS9 Cl* Melotte 22 BPL 130 0.008 0.005 0.004 0.003 0.004 0.003 +03 47 11.860 +24 13 53.80 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 499 0.004 0.003 0.003 0.002 0.002 0.002 +03 35 09.410 +24 14 19.80 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 0.83 12 GCS9 Cl* Melotte 22 DH 42 0.001 0.001 0.001 0.001 0.001 0.000 +03 40 43.200 +22 49 53.80 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 151 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 56.810 +24 59 07.10 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 0.61 12 GCS9 Cl* Melotte 22 KPNO 5 0.009 0.006 0.005 0.005 0.006 0.004 +03 50 06.560 +24 59 46.30 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 653 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 19.860 +24 59 01.30 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 0.88 12 GCS9 Cl* Melotte 22 DH 437 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 12.440 +22 41 50.80 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 0.87 12 GCS9 Cl* Melotte 22 DH 380 0.003 0.002 0.002 0.001 0.001 0.001 +03 52 35.320 +25 01 04.50 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 758 0.004 0.003 0.003 0.002 0.002 0.001 +03 49 21.930 +24 54 43.20 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 0.94 12 GCS9 Cl* Melotte 22 DH 617 0.003 0.002 0.002 0.002 0.002 0.001 +03 37 47.510 +24 53 46.10 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 0.86 12 GCS9 Cl* Melotte 22 DH 78 0.004 0.003 0.003 0.003 0.003 0.002 +03 49 26.640 +22 50 54.50 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 0.91 12 GCS9 Cl* Melotte 22 SK 315 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 17.610 +22 04 00.90 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 0.94 12 GCS9 Cl* Melotte 22 BPL 171 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 42.150 +25 00 28.20 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.92 12 GCS9 V* V461 Tau 0.002 0.002 0.001 0.001 0.001 0.001 +03 48 40.600 +25 01 19.80 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 186 0.006 0.004 0.004 0.003 0.004 0.003 +03 41 39.840 +24 52 30.00 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 0.88 12 GCS9 Cl* Melotte 22 HHJ 69 0.004 0.004 0.003 0.003 0.002 +03 41 47.090 +25 00 21.90 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.92 12 GCS9 Cl* Melotte 22 HHJ 125 0.004 0.003 0.003 0.002 0.002 +03 42 28.660 +25 01 00.20 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 0.93 12 GCS9 Cl* Melotte 22 HHJ 362 0.002 0.001 0.001 0.001 0.001 +03 42 00.020 +25 01 47.10 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 201 0.002 0.002 0.002 0.001 0.001 +03 47 35.860 +24 52 26.70 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 0.91 12 GCS9 V* V752 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 48 20.290 +24 54 54.90 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 0.94 12 GCS9 V* V344 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 02.970 +21 54 46.90 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 0.75 12 GCS9 Cl* Melotte 22 BPL 197 0.005 0.004 0.004 0.003 0.003 0.002 +03 47 40.960 +21 49 05.00 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 0.80 12 GCS9 UGCS J034740.95+214905.0 0.007 0.005 0.005 0.004 0.005 0.003 +03 59 26.930 +21 48 18.70 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 876 0.003 0.002 0.002 0.002 0.001 0.001 +03 52 01.650 +25 01 29.10 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 0.89 12 GCS9 V* V562 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 03.580 +25 01 13.60 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 737 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 51.560 +21 49 16.80 0 14.207 13.736 13.193 12.644 12.340 12.343 24.16 2.51 -42.78 2.51 0.81 12 GCS9 UGCS J035151.56+214916.8 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 16.750 +24 57 38.50 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 0.85 12 GCS9 Cl* Melotte 22 DH 103 0.004 0.003 0.003 0.003 0.002 0.002 +03 40 20.090 +21 51 33.60 0 15.554 14.955 14.342 13.783 13.442 13.436 12.33 2.51 -42.73 2.51 0.69 12 GCS9 UGCS J034020.08+215133.6 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 29.760 +23 58 05.80 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 0.92 12 GCS9 V* V460 Tau 0.004 0.003 0.003 0.002 0.002 0.001 +03 48 10.180 +23 59 20.10 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.88 12 GCS9 Cl* Melotte 22 DH 555 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 33.780 +24 01 58.80 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 582 0.003 0.003 0.003 0.002 0.002 0.001 +03 48 31.840 +24 01 58.60 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 0.93 12 GCS9 V* V872 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 58 07.690 +20 23 38.10 0 14.755 14.283 13.696 13.187 12.883 12.864 17.14 3.97 -39.44 3.97 0.89 12 GCS9 UGCS J035807.69+202338.0 0.003 0.003 0.003 0.002 0.002 0.001 +03 57 58.500 +20 24 19.50 0 14.806 14.330 13.710 13.193 12.861 12.867 16.74 3.97 -41.74 3.97 0.93 12 GCS9 UGCS J035758.50+202419.4 0.003 0.003 0.003 0.002 0.002 0.001 +04 05 13.750 +24 08 42.70 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.94 12 GCS9 Cl* Melotte 22 DH 915 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 09.460 +23 58 44.70 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 0.72 12 GCS9 Cl* Melotte 22 PPL 2 0.013 0.008 0.007 0.006 0.007 0.005 +03 44 57.340 +23 59 32.30 0 13.672 13.326 12.770 12.194 11.941 11.960 16.63 2.22 -39.69 2.22 0.88 12 GCS9 V* V712 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 27.920 +23 59 59.60 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 342 0.005 0.004 0.004 0.003 0.003 0.002 +03 44 47.330 +24 00 37.70 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 0.94 12 GCS9 V* NW Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 53.210 +24 01 06.50 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 359 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 09.810 +24 04 32.70 0 15.935 15.388 14.737 14.174 13.793 13.744 17.63 2.23 -39.69 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 5 0.007 0.005 0.004 0.003 0.004 0.003 +03 45 16.130 +24 07 16.00 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 0.91 12 GCS9 V* V519 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 46.020 +24 10 12.50 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 592 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 06.050 +23 58 19.10 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 0.73 12 GCS9 Cl* Melotte 22 SHF 26 0.006 0.005 0.005 0.004 0.005 0.003 +03 45 42.330 +24 04 11.10 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 0.70 12 GCS9 Cl* Melotte 22 SHF 12 0.009 0.006 0.006 0.005 0.006 0.004 +03 45 39.290 +24 08 20.40 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 398 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 57.910 +24 08 40.90 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 0.90 12 GCS9 Cl* Melotte 22 DH 412 0.005 0.004 0.004 0.003 0.004 0.003 +03 46 04.570 +24 09 55.80 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 0.84 12 GCS9 2MASS J03460455+2409561 0.004 0.003 0.003 0.002 0.003 0.002 +03 49 52.440 +24 03 43.00 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 0.70 12 GCS9 UGCS J034952.43+240342.9 0.007 0.005 0.005 0.004 0.005 0.003 +03 49 16.420 +24 03 49.00 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 0.71 12 GCS9 V* V467 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 49 55.490 +24 06 05.00 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 641 0.003 0.002 0.002 0.002 0.002 0.001 +03 51 58.350 +23 58 19.30 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 0.65 12 GCS9 Cl* Melotte 22 DH 732 0.003 0.003 0.003 0.002 0.002 0.001 +03 59 23.500 +20 20 16.90 0 15.988 15.360 14.723 14.163 13.823 13.837 20.00 4.01 -37.38 4.01 0.67 12 GCS9 UGCS J035923.50+202016.8 0.006 0.005 0.004 0.004 0.004 0.003 +03 41 31.210 +24 03 24.70 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 177 0.004 0.003 0.003 0.002 0.002 +03 56 18.600 +23 57 51.60 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.89 12 GCS9 Cl* Melotte 22 HHJ 386 0.001 0.001 0.001 0.001 0.001 0.001 +03 56 28.910 +24 01 54.10 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.93 12 GCS9 Cl* Melotte 22 DH 837 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 17.900 +24 06 57.60 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.94 12 GCS9 Cl* Melotte 22 DH 217 0.003 0.003 0.003 0.002 0.001 +03 41 52.940 +24 07 24.70 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 0.82 12 GCS9 Cl* Melotte 22 DH 191 0.004 0.003 0.003 0.003 0.002 +03 42 00.370 +24 10 12.60 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 0.73 12 GCS9 Cl* Melotte 22 BPL 32 0.007 0.006 0.005 0.005 0.004 +03 42 13.900 +20 21 43.60 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.76 12 GCS9 Cl* Melotte 22 DH 216 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 18.540 +23 59 02.50 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 0.88 12 GCS9 Cl* Melotte 22 SHF 27 0.007 0.005 0.004 0.003 0.004 0.003 +03 46 23.470 +24 01 51.20 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 0.94 12 GCS9 V* V528 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 46 25.390 +24 09 36.20 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.64 12 GCS9 V* OZ Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 35.540 +24 01 35.40 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.89 12 GCS9 2MASS J03463552+2401355 0.003 0.003 0.002 0.002 0.002 0.001 +03 46 43.600 +23 59 42.30 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 467 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 51.440 +24 06 16.10 0 15.287 14.777 14.196 13.624 13.272 13.288 17.34 2.22 -38.74 2.22 0.81 12 GCS9 UGCS J034651.43+240616.1 0.004 0.003 0.003 0.002 0.003 0.002 +03 46 53.960 +24 07 57.10 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 0.78 12 GCS9 Cl* Melotte 22 MHO 8 0.004 0.003 0.003 0.002 0.002 0.002 +03 46 58.260 +24 01 41.30 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 0.79 12 GCS9 UGCS J034658.26+240141.3 0.004 0.003 0.003 0.002 0.002 0.001 +03 46 59.320 +24 01 42.80 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 484 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 10.650 +23 58 16.40 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 0.72 12 GCS9 Cl* Melotte 22 SHF 41 0.007 0.005 0.005 0.004 0.004 0.003 +03 47 13.170 +24 00 45.20 0 16.022 15.428 14.759 14.221 13.828 13.866 20.02 2.23 -40.38 2.23 0.66 12 GCS9 2MASS J03471316+2400453 0.007 0.005 0.004 0.003 0.004 0.003 +03 47 09.180 +24 03 07.70 0 13.411 13.064 12.521 11.964 11.675 11.719 20.92 2.22 -38.72 2.22 0.82 12 GCS9 Cl* Melotte 22 SRS 60765 0.002 0.001 0.001 0.001 0.001 0.001 +03 43 01.640 +20 20 13.80 0 15.588 15.080 14.494 13.912 13.618 13.585 15.94 3.44 -42.59 3.44 0.88 12 GCS9 UGCS J034301.63+202013.7 0.005 0.004 0.004 0.003 0.003 0.003 +03 55 08.980 +24 05 02.50 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 0.90 12 GCS9 Cl* Melotte 22 DH 812 0.002 0.002 0.002 0.001 0.001 +03 50 57.420 +24 06 30.70 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 0.80 12 GCS9 V* V800 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 19.070 +24 10 13.10 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 0.92 12 GCS9 V* V466 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 55.050 +23 57 42.00 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 0.86 12 GCS9 V* V388 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 25.350 +24 02 56.80 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 0.88 12 GCS9 Cl* Melotte 22 HHJ 427 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 32.000 +24 10 24.70 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 147 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 34.520 +24 02 23.00 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 0.72 12 GCS9 Cl* Melotte 22 DH 523 0.003 0.003 0.002 0.002 0.002 0.001 +03 47 46.400 +24 03 02.30 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 438 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 06.410 +24 06 51.70 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.94 12 GCS9 UGCS J034806.41+240651.6 0.003 0.003 0.002 0.002 0.002 0.001 +03 48 06.640 +24 00 06.70 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 549 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 09.220 +23 58 40.50 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 553 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 13.310 +23 58 46.80 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 0.93 12 GCS9 V* V342 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 43 28.740 +24 09 06.10 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.90 12 GCS9 Cl* Melotte 22 HHJ 76 0.004 0.003 0.003 0.003 0.002 +03 58 25.140 +24 00 58.50 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 865 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 09.870 +23 58 52.40 0 16.201 15.649 14.939 14.373 14.000 14.020 15.05 2.62 -40.39 2.62 0.63 12 GCS9 UGCS J033909.86+235852.4 0.007 0.006 0.005 0.005 0.004 0.003 +03 39 35.460 +24 07 06.10 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 0.87 12 GCS9 Cl* Melotte 22 DH 108 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 46.350 +23 58 52.90 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 0.69 12 GCS9 Cl* Melotte 22 DH 113 0.002 0.002 0.002 0.001 0.001 0.001 +03 42 41.180 +24 01 42.70 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.93 12 GCS9 V* LQ Tau 0.004 0.003 0.003 0.002 0.002 0.002 +03 42 41.850 +24 00 15.50 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.94 12 GCS9 Cl* Melotte 22 DH 236 0.003 0.003 0.002 0.002 0.002 0.001 +03 42 56.550 +24 04 57.80 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 0.82 12 GCS9 V* LT Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 43 11.650 +24 06 52.40 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 0.74 12 GCS9 Cl* Melotte 22 BPL 52 0.004 0.003 0.003 0.003 0.002 0.002 +03 40 25.620 +24 06 00.00 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 138 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 26.410 +24 05 23.50 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 0.84 12 GCS9 Cl* Melotte 22 SK 778 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 29.730 +21 02 16.70 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.92 12 GCS9 Cl* Melotte 22 DH 452 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 50.380 +20 57 59.90 0 14.307 13.848 13.262 12.675 12.412 12.387 20.01 3.05 -39.37 3.05 0.89 12 GCS9 UGCS J034950.38+205759.8 0.003 0.002 0.002 0.001 0.001 0.001 +03 43 05.730 +21 01 49.10 0 15.042 14.523 13.929 13.374 13.044 13.028 19.59 3.59 -44.27 3.59 0.88 12 GCS9 UGCS J034305.73+210149.1 0.004 0.003 0.003 0.003 0.002 0.002 +03 57 37.100 +21 01 48.00 0 15.678 15.108 14.501 13.963 13.607 13.593 20.33 3.37 -45.03 3.37 0.85 12 GCS9 UGCS J035737.10+210147.9 0.005 0.004 0.004 0.003 0.003 0.002 +03 58 10.250 +20 56 55.60 0 16.121 15.494 14.840 14.289 13.924 13.940 19.36 3.38 -39.83 3.38 0.65 12 GCS9 UGCS J035810.24+205655.5 0.007 0.005 0.005 0.004 0.004 0.003 +03 46 10.100 +26 00 09.00 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.82 12 GCS9 Cl* Melotte 22 HHJ 80 0.004 0.004 0.003 0.003 0.003 0.002 +03 46 19.430 +26 02 35.50 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 0.74 12 GCS9 Cl* Melotte 22 DH 436 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 21.350 +26 05 25.50 0 15.885 15.309 14.637 14.115 13.734 13.722 19.33 2.24 -43.28 2.24 0.89 12 GCS9 UGCS J034521.34+260525.4 0.006 0.005 0.004 0.004 0.004 0.003 +03 52 13.320 +26 08 36.80 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.82 12 GCS9 V* V715 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 41.270 +23 54 09.70 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 0.69 12 GCS9 Cl* Melotte 22 PPL 1 0.014 0.008 0.006 0.005 0.006 0.004 +03 50 22.010 +23 55 30.30 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 0.65 12 GCS9 UGCS J035022.01+235530.3 0.015 0.009 0.008 0.006 0.008 0.006 +03 52 06.720 +24 16 00.40 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 0.68 12 GCS9 2MASS J03520670+2416008 0.013 0.009 0.008 0.008 0.008 0.005 +03 46 14.060 +23 21 56.40 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 0.68 12 GCS9 UGCS J034614.06+232156.4 0.013 0.009 0.008 0.007 0.009 0.006 +03 39 17.050 +22 27 10.90 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 0.69 12 GCS9 2MASS J03391704+2227111 0.013 0.010 0.008 0.007 0.008 0.005 +03 48 04.670 +23 39 30.10 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 0.66 12 GCS9 UGCS J034804.66+233930.1 0.012 0.007 0.006 0.005 0.006 0.004 +03 47 49.450 +23 31 52.80 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 0.65 12 GCS9 UGCS J034749.44+233152.8 0.013 0.009 0.007 0.006 0.008 0.005 +03 41 54.160 +23 05 04.70 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 0.69 12 GCS9 Cl* Melotte 22 MHOBD 3 0.016 0.010 0.008 0.007 0.007 0.005 +03 55 23.080 +24 49 04.90 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 0.72 12 GCS9 Cl* Melotte 22 BPL 327 0.014 0.009 0.008 0.007 0.008 0.005 +03 44 53.120 +23 34 22.80 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 0.67 12 GCS9 UGCS J034453.12+233422.8 0.016 0.009 0.008 0.006 0.008 0.006 +03 41 05.410 +23 32 56.70 0 17.223 16.421 15.660 15.137 14.704 14.689 20.80 2.22 -41.06 2.22 0.68 12 GCS9 UGCS J034105.40+233256.6 0.015 0.010 0.008 0.008 0.008 0.006 +03 49 04.860 +23 33 39.30 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 0.70 12 GCS9 Cl* Melotte 22 IPMBD 20 0.014 0.008 0.007 0.006 0.007 0.005 +03 45 54.960 +23 33 57.80 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 0.72 12 GCS9 Cl* Melotte 22 SHF 21 0.013 0.008 0.007 0.006 0.007 0.005 +03 45 31.370 +24 52 47.40 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 0.66 12 GCS9 2MASS J03453136+2452476 0.015 0.009 0.007 0.006 0.007 0.004 +03 36 54.100 +21 58 02.70 0 17.346 16.486 15.758 15.194 14.752 14.754 19.11 3.00 -39.30 3.00 0.65 12 GCS9 UGCS J033654.10+215802.7 0.014 0.009 0.008 0.009 0.010 0.006 +04 01 24.410 +20 17 15.60 0 17.022 16.289 15.614 15.051 14.645 14.638 22.29 3.22 -42.39 3.22 0.63 12 GCS9 UGCS J040124.40+201715.5 0.012 0.008 0.007 0.006 0.007 0.006 +03 38 10.200 +26 07 33.00 0 17.160 16.444 15.695 15.142 14.702 14.718 18.46 3.04 -40.95 3.04 0.71 12 GCS9 UGCS J033810.20+260733.0 0.015 0.010 0.010 0.007 0.009 0.007 +03 40 55.300 +25 34 57.40 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 0.69 12 GCS9 UGCS J034055.30+253457.3 0.017 0.011 0.010 0.008 0.009 0.006 +03 30 44.540 +25 39 07.70 0 17.609 16.705 15.900 15.296 14.886 14.862 16.76 3.43 -42.84 3.43 0.70 12 GCS9 UGCS J033044.53+253907.6 0.016 0.010 0.009 0.008 0.009 0.007 +03 37 46.600 +26 50 44.50 0 17.381 16.593 15.840 15.292 14.877 14.867 19.58 3.37 -40.72 3.37 0.70 12 GCS9 UGCS J033746.59+265044.5 0.014 0.010 0.009 0.008 0.009 0.006 +03 44 35.160 +25 13 42.80 1 17.656 16.584 15.662 14.985 14.448 14.460 19.33 2.26 -44.97 2.26 0.68 12 GCS9 UGCS J034435.16+251342.7 0.020 0.010 0.007 0.006 0.008 0.005 +03 49 33.960 +22 16 35.90 0 17.942 16.978 16.144 15.535 15.049 15.042 21.92 2.64 -43.77 2.64 0.64 12 GCS9 UGCS J034933.96+221635.8 0.021 0.015 0.011 0.013 0.013 0.008 +03 47 27.720 +22 09 38.50 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 0.72 12 GCS9 Cl* Melotte 22 MHOBD 5 0.017 0.011 0.010 0.009 0.011 0.006 +03 51 44.950 +23 26 39.30 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 0.62 12 GCS9 2MASS J03514491+2326395 0.024 0.012 0.010 0.010 0.011 0.008 +03 50 52.170 +23 27 11.20 1 17.690 16.638 15.700 15.060 14.505 14.555 19.89 2.21 -41.33 2.21 0.71 12 GCS9 UGCS J035052.17+232711.2 0.019 0.010 0.008 0.007 0.006 0.005 +03 26 33.430 +22 39 42.90 0 17.587 16.833 16.109 15.489 15.093 15.096 20.93 5.59 -43.56 5.59 0.69 12 GCS9 UGCS J032633.42+223942.8 0.015 0.011 0.010 0.015 0.010 0.008 +03 42 18.010 +24 55 09.90 0 17.486 16.658 15.876 15.298 14.854 17.65 3.06 -39.29 3.06 0.64 12 GCS9 UGCS J034218.01+245509.9 0.017 0.011 0.009 0.010 0.007 +03 45 50.660 +24 09 03.50 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 0.65 12 GCS9 2MASS J03455065+2409037 0.017 0.010 0.008 0.006 0.008 0.005 +03 47 50.410 +23 54 47.80 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 0.63 12 GCS9 2MASS J03475038+2354480 0.028 0.014 0.011 0.009 0.012 0.008 +03 55 34.280 +22 23 29.20 0 18.055 17.108 16.189 15.579 15.117 15.096 18.47 2.72 -40.79 2.72 0.71 12 GCS9 UGCS J035534.28+222329.2 0.027 0.015 0.012 0.009 0.012 0.008 +03 40 06.590 +21 08 59.90 0 18.210 17.152 16.272 15.651 15.113 15.123 21.31 3.80 -40.02 3.80 0.68 12 GCS9 UGCS J034006.59+210859.9 0.024 0.013 0.011 0.016 0.009 0.007 +03 47 17.920 +24 22 31.60 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 0.68 12 GCS9 UGCS J034717.92+242231.6 0.029 0.013 0.010 0.009 0.011 0.008 +03 46 34.990 +23 31 14.40 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 0.70 12 GCS9 UGCS J034634.98+233114.4 0.036 0.016 0.013 0.011 0.014 0.009 +03 47 59.740 +22 36 01.80 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 0.69 12 GCS9 2MASS J03475972+2236019 0.027 0.015 0.012 0.011 0.014 0.008 +03 45 50.630 +23 44 36.90 0 18.291 17.301 16.379 15.794 15.257 15.244 20.44 2.31 -41.86 2.31 0.71 12 GCS9 Cl* Melotte 22 NPNPL 1 0.032 0.016 0.012 0.011 0.013 0.009 +03 57 18.480 +21 37 32.30 0 18.299 17.369 16.382 15.808 15.239 15.276 21.09 3.54 -41.34 3.54 0.70 12 GCS9 UGCS J035718.48+213732.3 0.029 0.017 0.012 0.013 0.013 0.009 +03 51 38.960 +24 30 44.80 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 0.64 12 GCS9 UGCS J035138.95+243044.7 0.045 0.017 0.012 0.011 0.013 0.008 +03 46 23.120 +24 20 36.00 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 0.66 12 GCS9 2MASS J03462312+2420363 0.028 0.014 0.010 0.009 0.011 0.008 +03 59 09.870 +21 46 02.00 0 18.325 17.223 16.331 15.747 15.203 15.210 20.58 3.13 -40.99 3.13 0.71 12 GCS9 UGCS J035909.86+214602.0 0.029 0.014 0.012 0.018 0.009 0.009 +03 40 38.100 +26 38 29.20 0 18.747 17.679 16.599 15.993 15.481 15.481 18.85 3.21 -40.47 3.21 0.71 12 GCS9 UGCS J034038.09+263829.2 0.039 0.020 0.015 0.012 0.015 0.010 +03 46 34.250 +23 50 03.60 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 0.66 12 GCS9 Cl* Melotte 22 NPNPL 2 0.149 0.046 0.027 0.023 0.028 0.018 +03 34 20.190 +25 22 05.10 0 19.676 18.250 17.178 16.453 15.945 20.30 6.48 -41.54 6.48 0.66 12 GCS9 UGCS J033420.18+252205.0 0.083 0.031 0.021 0.033 0.014 +03 43 03.830 +23 54 19.60 0 18.852 17.703 16.751 16.052 15.562 15.535 18.37 2.48 -38.49 2.48 0.66 12 GCS9 2MASS J03430378+2354200 0.046 0.024 0.017 0.018 0.025 0.012 +04 05 51.620 +23 44 48.10 0 19.517 18.119 17.046 16.350 15.809 15.769 19.98 3.68 -42.86 3.68 0.68 12 GCS9 UGCS J040551.61+234448.0 0.065 0.029 0.021 0.019 0.021 0.013 +03 49 04.740 +23 26 43.10 0 18.943 17.724 16.792 16.171 15.598 15.597 17.56 2.58 -40.60 2.58 0.70 12 GCS9 UGCS J034904.73+232643.1 0.051 0.024 0.019 0.021 0.016 0.014 +03 57 13.460 +24 22 51.80 0 19.665 18.458 17.407 16.653 16.109 16.086 17.40 3.82 -42.66 3.82 0.66 12 GCS9 UGCS J035713.46+242251.7 0.085 0.049 0.031 0.031 0.030 0.021 +03 54 05.350 +23 33 59.20 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 0.61 12 GCS9 2MASS J03540532+2333597 0.041 0.024 0.018 0.016 0.017 0.011 +03 40 27.930 +24 12 09.30 0 19.730 18.501 17.352 16.700 16.088 16.073 15.91 3.22 -42.54 3.22 0.62 12 GCS9 Cl* Melotte 22 IPL 84 0.100 0.045 0.027 0.031 0.031 0.019 +03 45 11.730 +23 41 43.60 0 20.311 18.985 17.617 16.886 16.120 16.204 13.04 2.65 -46.83 2.65 0.62 12 GCS9 Cl* Melotte 22 NPNPL 3 0.181 0.062 0.029 0.026 0.027 0.020 +03 44 22.450 +23 39 01.30 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 0.61 12 GCS9 UGCS J034422.44+233901.2 0.064 0.025 0.017 0.016 0.018 0.013 +03 55 47.450 +22 50 50.10 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 0.63 12 GCS9 UGCS J035547.44+225050.0 0.122 0.050 0.032 0.029 0.032 0.022 +03 36 05.920 +23 01 23.70 0 19.381 18.086 17.063 16.451 15.840 15.845 24.12 3.64 -46.32 3.64 0.64 12 GCS9 UGCS J033605.92+230123.6 0.073 0.035 0.023 0.020 0.022 0.016 +03 46 27.100 +21 48 22.60 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 0.65 12 GCS9 UGCS J034627.10+214822.5 0.125 0.059 0.032 0.026 0.026 0.017 +04 00 58.260 +21 33 31.70 0 19.241 18.035 16.948 16.250 15.691 15.702 18.08 4.76 -43.38 4.76 0.67 12 GCS9 UGCS J040058.25+213331.7 0.070 0.031 0.023 0.021 0.012 0.013 +03 42 18.080 +19 07 33.00 0 19.797 18.358 17.264 16.589 16.057 16.053 17.58 5.18 -47.78 5.18 0.65 12 GCS9 UGCS J034218.08+190732.9 0.081 0.035 0.022 0.024 0.025 0.020 +04 07 10.570 +24 59 49.20 0 18.433 17.603 16.733 15.987 15.491 15.459 19.65 3.35 -39.70 3.35 0.70 12 GCS9 UGCS J040710.56+245949.1 0.027 0.017 0.014 0.016 0.017 0.010 +03 42 30.590 +25 02 39.30 0 19.269 18.013 17.004 16.367 15.746 22.01 3.54 -44.77 3.54 0.68 12 GCS9 UGCS J034230.58+250239.2 0.068 0.033 0.021 0.025 0.016 +03 56 59.900 +24 05 35.50 0 19.904 19.003 17.628 16.950 16.225 16.196 16.25 4.13 -41.75 4.13 0.62 12 GCS9 UGCS J035659.90+240535.4 0.102 0.094 0.038 0.038 0.035 0.023 +03 41 13.210 +24 05 25.60 0 20.248 19.168 17.791 16.898 16.325 16.266 14.11 3.48 -46.41 3.48 0.61 12 GCS9 Cl* Melotte 22 IPL 81 0.166 0.092 0.041 0.042 0.039 0.025 +03 50 16.090 +24 08 34.70 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 0.67 12 GCS9 UGCS J035016.08+240834.7 0.125 0.040 0.022 0.021 0.027 0.018 +03 43 46.290 +23 58 37.90 0 19.124 17.930 16.876 16.248 15.628 20.13 2.62 -45.01 2.62 0.69 12 GCS9 2MASS J03434627+2358382 0.057 0.026 0.017 0.020 0.012 +03 55 30.190 +21 01 10.20 0 18.713 17.522 16.584 15.983 15.414 15.354 19.46 3.52 -41.16 3.52 0.72 12 GCS9 UGCS J035530.19+210110.1 0.038 0.019 0.015 0.013 0.014 0.010 +03 44 09.010 +27 56 42.00 0 14.980 14.512 13.981 13.444 13.140 13.140 27.76 3.70 -38.32 3.70 2 GCS9 UGCS J034409.01+275642.0 0.003 0.003 0.003 0.003 0.003 0.002 +03 48 31.180 +29 22 39.20 0 13.011 12.688 12.199 11.692 11.374 11.413 5.92 5.84 -27.47 5.84 2 GCS9 UGCS J034831.17+292239.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 47 08.900 +29 02 38.80 0 13.793 13.432 12.890 12.285 12.076 12.094 10.64 5.84 -40.62 5.84 2 GCS9 UGCS J034708.90+290238.8 0.002 0.002 0.002 0.001 0.001 0.001 +03 58 33.710 +28 07 09.10 0 16.950 16.428 15.680 15.099 14.755 14.708 24.72 4.01 -50.41 4.01 2 GCS9 UGCS J035833.70+280709.0 0.009 0.008 0.007 0.007 0.010 0.006 +03 53 07.090 +28 07 33.00 0 13.630 13.247 12.738 12.198 11.946 11.966 28.63 3.74 -50.15 3.74 2 GCS9 UGCS J035307.09+280733.0 0.002 0.001 0.002 0.001 0.001 0.001 +03 44 59.810 +29 12 29.50 0 16.688 16.081 15.393 14.773 14.418 14.415 6.41 5.88 -48.49 5.88 2 GCS9 UGCS J034459.81+291229.4 0.008 0.007 0.006 0.005 0.006 0.004 +03 40 56.060 +28 43 39.00 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 2 GCS9 Cl* Melotte 22 DH 161 0.001 0.001 0.001 0.001 0.001 0.001 +03 31 12.320 +27 05 59.20 0 15.984 15.464 14.836 14.275 13.919 -2.97 7.05 -25.10 7.05 2 GCS9 UGCS J033112.32+270559.2 0.005 0.004 0.005 0.006 0.003 +03 48 55.420 +28 41 20.70 0 14.701 14.119 13.468 12.911 12.565 12.571 19.52 2.93 -34.58 2.93 2 GCS9 UGCS J034855.41+284120.6 0.003 0.002 0.002 0.002 0.002 0.001 +03 37 17.780 +28 45 35.80 0 14.292 13.909 13.380 12.741 12.457 12.474 11.77 4.93 -40.45 4.93 2 GCS9 UGCS J033717.77+284535.8 0.002 0.002 0.003 0.002 0.002 0.001 +03 56 10.690 +27 11 17.90 0 13.820 13.467 12.963 12.391 12.128 12.123 8.42 3.30 -35.60 3.30 2 GCS9 UGCS J035610.69+271117.8 0.002 0.002 0.002 0.001 0.001 0.001 +03 29 11.270 +27 09 52.50 0 14.529 13.987 13.341 12.786 12.440 27.16 6.92 -25.14 6.92 2 GCS9 UGCS J032911.26+270952.4 0.003 0.002 0.002 0.002 0.001 +03 29 03.100 +27 04 59.90 0 15.372 14.837 14.194 13.600 13.273 21.10 6.95 -29.46 6.95 2 GCS9 UGCS J032903.10+270459.8 0.004 0.003 0.003 0.003 0.002 +03 30 11.390 +27 37 55.80 0 13.268 12.939 12.423 12.042 11.670 11.668 15.01 4.88 -33.94 4.88 2 GCS9 UGCS J033011.39+273755.7 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 10.110 +27 40 09.10 0 15.074 14.582 14.010 13.477 13.154 13.152 20.96 3.45 -35.33 3.45 2 GCS9 UGCS J034510.11+274009.0 0.004 0.003 0.003 0.003 0.003 0.002 +03 42 24.950 +27 32 21.50 0 15.402 14.912 14.307 13.812 13.463 13.477 18.17 3.30 -32.77 3.30 2 GCS9 UGCS J034224.95+273221.5 0.004 0.004 0.004 0.004 0.004 0.003 +03 41 23.630 +27 24 16.90 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 2 GCS9 Cl* Melotte 22 DH 171 0.004 0.003 0.003 0.003 0.003 0.002 +03 42 24.930 +27 42 15.40 0 15.788 15.273 14.656 14.111 13.783 13.811 18.89 3.32 -32.08 3.32 2 GCS9 UGCS J034224.92+274215.4 0.006 0.005 0.004 0.004 0.005 0.004 +04 01 21.510 +27 12 33.20 0 14.198 13.815 13.270 12.730 12.466 12.482 26.22 4.23 -38.49 4.23 2 GCS9 UGCS J040121.51+271233.1 0.002 0.002 0.002 0.002 0.002 0.001 +03 53 57.160 +28 10 13.30 0 15.617 15.088 14.479 13.890 13.567 13.573 26.79 3.76 -35.49 3.76 2 GCS9 UGCS J035357.16+281013.2 0.005 0.004 0.004 0.003 0.003 0.002 +03 47 42.860 +28 18 59.10 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 2 GCS9 Cl* Melotte 22 DH 533 0.004 0.003 0.004 0.003 0.003 0.002 +03 43 37.560 +26 32 00.90 0 16.418 15.769 15.103 14.563 14.210 14.181 23.24 2.97 -37.05 2.97 2 GCS9 UGCS J034337.55+263200.8 0.009 0.007 0.006 0.005 0.006 0.003 +03 48 52.950 +27 11 09.20 0 12.550 12.284 11.843 11.345 11.027 11.066 26.03 2.88 -33.77 2.88 2 GCS9 UGCS J034852.95+271109.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 28.990 +25 34 55.80 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 2 GCS9 Cl* Melotte 22 HHJ 359 0.002 0.002 0.002 0.001 0.001 0.001 +04 06 08.010 +25 42 34.60 0 16.097 15.463 14.792 14.184 13.818 13.799 11.14 3.39 -47.13 3.39 2 GCS9 UGCS J040608.00+254234.5 0.006 0.005 0.005 0.005 0.005 0.003 +03 43 56.000 +25 36 25.20 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 2 GCS9 Cl* Melotte 22 PLZJ 50 0.010 0.008 0.007 0.006 0.006 0.005 +03 52 16.560 +27 35 51.00 0 15.081 14.622 14.039 13.529 13.200 13.192 11.21 2.89 -39.30 2.89 2 GCS9 UGCS J035216.56+273551.0 0.004 0.003 0.003 0.003 0.003 0.002 +03 53 26.650 +26 11 49.50 0 12.887 12.578 12.116 11.553 11.277 11.275 9.78 2.95 -50.85 2.95 2 GCS9 UGCS J035326.65+261149.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 28 11.330 +26 55 37.00 0 16.205 15.582 14.915 14.351 13.989 25.62 7.01 -37.29 7.01 2 GCS9 UGCS J032811.33+265537.0 0.006 0.005 0.005 0.006 0.003 +03 36 46.480 +28 15 05.90 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 2 GCS9 Cl* Melotte 22 DH 64 0.005 0.004 0.004 0.003 0.003 0.002 +03 51 05.080 +26 36 51.20 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 2 GCS9 Cl* Melotte 22 MBSC 93 0.005 0.004 0.004 0.003 0.003 0.002 +03 26 13.400 +25 32 35.70 0 13.384 12.899 12.323 11.923 11.539 8.41 6.87 -33.03 6.87 2 GCS9 UGCS J032613.39+253235.7 0.002 0.001 0.001 0.001 0.001 +03 47 22.280 +25 43 15.00 0 16.268 15.616 14.929 14.374 14.017 13.977 15.45 2.25 -46.31 2.25 2 GCS9 UGCS J034722.28+254315.0 0.007 0.006 0.005 0.005 0.005 0.004 +03 27 47.670 +26 57 06.10 0 15.256 14.836 14.217 13.603 13.265 20.38 6.95 -28.78 6.95 2 GCS9 UGCS J032747.66+265706.0 0.004 0.003 0.003 0.003 0.002 +03 35 50.290 +25 42 20.50 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 2 GCS9 Cl* Melotte 22 DH 49 0.002 0.002 0.002 0.001 0.001 +03 39 04.060 +27 00 04.70 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 2 GCS9 Cl* Melotte 22 DH 98 0.002 0.002 0.002 0.001 0.001 0.001 +03 39 53.750 +28 34 56.40 0 14.232 13.794 13.189 12.675 12.384 12.373 25.26 4.93 -55.59 4.93 2 GCS9 UGCS J033953.75+283456.3 0.002 0.002 0.002 0.002 0.001 0.001 +03 50 52.180 +26 11 40.10 0 15.944 15.346 14.710 14.173 13.814 13.827 23.44 2.97 -34.78 2.97 2 GCS9 UGCS J035052.18+261140.0 0.007 0.005 0.005 0.003 0.004 0.003 +04 03 40.670 +25 21 34.50 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 2 GCS9 Cl* Melotte 22 DH 908 0.001 0.001 0.001 0.001 0.001 0.001 +03 37 37.450 +25 41 49.80 0 14.994 14.527 13.953 13.399 13.101 13.070 12.66 2.96 -38.52 2.96 2 GCS9 UGCS J033737.45+254149.7 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 39.330 +26 16 03.70 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 2 GCS9 Cl* Melotte 22 DH 680 0.008 0.006 0.005 0.004 0.005 0.003 +04 00 39.240 +26 44 19.00 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 2 GCS9 2MASS J04003922+2644194 0.001 0.001 0.001 0.001 0.001 0.001 +03 37 36.010 +26 32 48.40 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 2 GCS9 Cl* Melotte 22 DH 75 0.001 0.001 0.001 0.001 0.001 0.000 +03 43 04.370 +25 26 12.00 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 2 GCS9 Cl* Melotte 22 HHJ 107 0.004 0.003 0.003 0.002 0.002 0.001 +03 43 38.900 +27 01 01.20 0 15.573 15.021 14.385 13.839 13.499 13.516 25.77 2.95 -39.98 2.95 2 GCS9 UGCS J034338.89+270101.1 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 43.140 +26 32 20.90 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 2 GCS9 Cl* Melotte 22 DH 588 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 44.370 +26 18 18.80 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 2 GCS9 Cl* Melotte 22 MBSC 82 0.004 0.003 0.004 0.002 0.003 0.002 +03 33 08.260 +26 31 13.00 0 16.896 16.129 15.414 14.825 14.421 14.415 19.68 3.01 -38.39 3.01 2 GCS9 UGCS J033308.26+263113.0 0.013 0.008 0.008 0.006 0.007 0.004 +03 37 10.510 +25 17 34.60 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 2 GCS9 Cl* Melotte 22 DH 67 0.004 0.003 0.003 0.002 0.002 0.002 +03 36 42.460 +25 19 26.50 0 15.840 15.296 14.666 14.163 13.816 13.794 15.86 2.97 -36.53 2.97 2 GCS9 UGCS J033642.46+251926.4 0.007 0.005 0.005 0.004 0.004 0.003 +04 06 52.120 +26 21 12.20 0 15.224 14.702 14.074 13.477 13.127 13.144 10.04 2.97 -34.88 2.97 2 GCS9 UGCS J040652.11+262112.2 0.004 0.004 0.003 0.002 0.002 0.002 +03 29 07.680 +25 21 43.50 0 15.576 15.018 14.363 13.811 13.445 13.438 26.60 3.37 -35.82 3.37 2 GCS9 UGCS J032907.68+252143.5 0.005 0.004 0.004 0.003 0.003 0.002 +04 00 33.240 +25 30 20.50 0 15.605 15.106 14.432 13.755 13.426 13.420 24.06 3.32 -32.29 3.32 2 GCS9 UGCS J040033.23+253020.4 0.005 0.004 0.004 0.003 0.003 0.002 +03 32 36.290 +26 58 10.00 0 16.087 15.489 14.800 14.224 13.863 13.827 21.67 2.97 -39.87 2.97 2 GCS9 UGCS J033236.29+265810.0 0.007 0.005 0.005 0.004 0.004 0.003 +03 49 16.180 +26 49 02.90 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 2 GCS9 2MASS J03491617+2649031 0.011 0.008 0.007 0.007 0.007 0.004 +03 41 48.050 +26 47 20.70 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 2 GCS9 Cl* Melotte 22 DH 188 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 29.350 +26 40 42.90 0 12.928 12.672 12.185 11.564 11.326 11.333 12.54 2.95 -40.06 2.95 2 GCS9 UGCS J035329.34+264042.9 0.001 0.001 0.001 0.001 0.001 0.001 +03 53 29.690 +26 40 49.60 0 12.976 12.631 12.090 11.488 11.193 11.211 12.01 2.95 -40.62 2.95 2 GCS9 UGCS J035329.69+264049.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 24 59.740 +25 34 04.50 0 16.878 16.243 15.582 14.987 14.606 38.59 7.21 -49.57 7.21 2 GCS9 UGCS J032459.74+253404.4 0.009 0.007 0.007 0.010 0.005 +03 31 29.600 +26 30 12.10 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 2 GCS9 Cl* Melotte 22 DH 16 0.003 0.002 0.003 0.002 0.002 0.001 +03 53 04.920 +27 01 42.30 0 14.277 13.874 13.309 12.686 12.394 12.404 9.69 2.95 -37.92 2.95 2 GCS9 UGCS J035304.92+270142.2 0.003 0.002 0.002 0.001 0.002 0.001 +03 38 54.160 +24 42 15.60 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 2 GCS9 Cl* Melotte 22 HHJ 63 0.004 0.003 0.003 0.003 0.002 0.002 +03 49 28.800 +26 50 51.40 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 2 GCS9 Cl* Melotte 22 MBSC 95 0.005 0.004 0.004 0.004 0.003 0.002 +03 36 27.370 +24 41 17.10 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 2 GCS9 V* KK Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 36 38.910 +24 38 48.70 0 15.678 15.146 14.550 14.008 13.684 13.736 21.00 2.64 -36.23 2.64 2 GCS9 UGCS J033638.90+243848.7 0.005 0.004 0.004 0.004 0.005 0.003 +03 51 25.880 +24 47 38.70 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 2 GCS9 V* V558 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 35 19.700 +26 33 10.60 0 14.411 13.963 13.402 12.862 12.572 12.563 15.89 3.30 -32.50 3.30 2 GCS9 UGCS J033519.70+263310.6 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 05.790 +23 45 34.70 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 2 GCS9 UGCS J034705.79+234534.7 0.008 0.005 0.005 0.004 0.005 0.003 +03 43 29.900 +24 39 23.40 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 2 GCS9 Cl* Melotte 22 DH 269 0.005 0.004 0.004 0.003 0.002 +03 27 11.130 +26 00 29.20 0 14.463 14.069 13.521 12.933 12.651 9.47 6.88 -28.40 6.88 2 GCS9 UGCS J032711.12+260029.1 0.003 0.002 0.002 0.002 0.001 +03 32 51.370 +25 54 56.70 0 15.038 14.557 13.922 13.352 13.023 14.47 5.31 -36.68 5.31 2 GCS9 UGCS J033251.37+255456.7 0.003 0.003 0.003 0.003 0.002 +03 31 20.710 +25 57 33.60 1 16.847 16.068 15.309 14.745 14.321 14.328 21.73 3.39 -38.42 3.39 2 GCS9 UGCS J033120.70+255733.5 0.009 0.007 0.006 0.005 0.006 0.004 +03 37 16.710 +25 44 11.10 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 2 GCS9 Cl* Melotte 22 DH 70 0.004 0.003 0.003 0.002 0.002 0.001 +03 41 38.870 +22 16 40.30 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 2 GCS9 Cl* Melotte 22 DH 183 0.003 0.002 0.002 0.002 0.002 +03 42 10.600 +22 18 05.50 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 2 GCS9 Cl* Melotte 22 HHJ 55 0.004 0.003 0.003 0.003 0.003 +03 48 36.340 +25 15 41.20 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 2 GCS9 Cl* Melotte 22 BPL 183 0.007 0.005 0.005 0.004 0.005 0.003 +03 42 01.680 +22 23 26.60 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 2 GCS9 V* V495 Tau 0.003 0.002 0.002 0.002 0.002 +04 01 28.430 +23 30 59.60 1 16.318 15.539 14.772 14.202 13.788 13.788 24.55 3.37 -39.20 3.37 2 GCS9 UGCS J040128.42+233059.6 0.008 0.005 0.005 0.004 0.004 0.003 +03 53 07.290 +25 47 21.40 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 2 GCS9 Cl* Melotte 22 DH 778 0.005 0.004 0.003 0.003 0.003 0.002 +03 34 41.550 +26 09 27.10 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 2 GCS9 Cl* Melotte 22 DH 36 0.004 0.003 0.003 0.003 0.002 +03 34 46.960 +26 05 38.40 0 14.123 13.758 13.233 12.636 12.369 9.36 5.31 -55.43 5.31 2 GCS9 UGCS J033446.95+260538.4 0.002 0.002 0.002 0.002 0.001 +03 43 34.490 +25 57 30.60 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 2 GCS9 UGCS J034334.48+255730.5 0.009 0.006 0.005 0.004 0.004 0.003 +03 56 52.310 +25 10 05.10 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2 GCS9 Cl* Melotte 22 HHJ 17 0.007 0.006 0.005 0.003 0.004 0.003 +03 41 42.410 +23 54 57.10 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 2 GCS9 Cl* Melotte 22 STAR 5 0.007 0.006 0.004 0.004 0.002 +03 40 55.050 +22 20 58.70 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 2 GCS9 V* V430 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 56 40.450 +22 08 46.50 0 15.445 14.885 14.241 13.710 13.373 13.371 21.46 2.51 -48.69 2.51 2 GCS9 UGCS J035640.44+220846.5 0.005 0.004 0.004 0.003 0.003 0.002 +03 28 26.620 +26 02 11.60 0 16.852 16.148 15.367 14.824 14.432 4.01 7.08 -51.89 7.08 2 GCS9 UGCS J032826.62+260211.5 0.009 0.007 0.006 0.008 0.004 +03 57 40.680 +25 16 04.10 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 2 GCS9 Cl* Melotte 22 DH 856 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 25.970 +25 58 42.10 0 15.160 14.687 14.062 13.537 13.179 13.218 11.69 2.23 -36.19 2.23 2 GCS9 UGCS J034325.96+255842.1 0.004 0.003 0.003 0.002 0.003 0.002 +03 44 31.040 +22 15 14.90 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2 GCS9 Cl* Melotte 22 DH 344 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 59.860 +22 05 29.30 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 2 GCS9 Cl* Melotte 22 DH 884 0.003 0.003 0.003 0.002 0.002 0.002 +03 27 16.170 +25 51 42.80 0 14.414 14.020 13.433 12.884 12.573 7.75 6.88 -24.82 6.88 2 GCS9 UGCS J032716.16+255142.7 0.002 0.002 0.002 0.002 0.001 +03 45 11.520 +21 14 29.30 0 16.421 15.764 15.109 14.536 14.148 14.145 25.46 2.68 -40.57 2.68 2 GCS9 UGCS J034511.51+211429.2 0.008 0.006 0.005 0.005 0.007 0.004 +03 54 24.000 +23 51 58.70 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 2 GCS9 UGCS J035423.99+235158.7 0.006 0.005 0.005 0.004 0.004 0.003 +03 26 24.300 +24 25 42.40 0 13.990 13.633 13.081 12.464 12.209 1.09 6.95 -51.34 6.95 2 GCS9 UGCS J032624.30+242542.3 0.002 0.002 0.002 0.001 0.001 +04 00 03.210 +22 24 46.00 1 16.534 15.875 15.128 14.550 14.132 14.141 15.92 2.87 -33.98 2.87 2 GCS9 UGCS J040003.20+222445.9 0.008 0.006 0.005 0.006 0.004 0.004 +03 28 02.300 +25 55 26.60 0 14.028 13.631 13.111 12.505 12.216 32.02 6.88 -55.34 6.88 2 GCS9 UGCS J032802.30+255526.5 0.002 0.002 0.002 0.002 0.001 +03 43 24.990 +23 52 01.20 0 16.867 16.116 15.368 14.820 14.365 14.16 2.34 -46.02 2.34 2 GCS9 2MASS J03432497+2352017 0.011 0.008 0.007 0.007 0.004 +04 03 30.230 +25 16 04.50 0 13.760 13.396 12.828 12.208 11.890 11.912 26.34 2.89 -36.38 2.89 2 GCS9 UGCS J040330.22+251604.5 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 42.660 +21 12 14.90 0 15.123 14.590 13.965 13.401 13.060 13.062 28.07 3.76 -38.75 3.76 2 GCS9 UGCS J035942.66+211214.8 0.004 0.003 0.003 0.003 0.001 0.002 +04 02 55.110 +25 53 53.50 0 16.259 15.667 15.021 14.464 14.087 14.062 19.80 3.33 -38.23 3.33 2 GCS9 UGCS J040255.10+255353.5 0.007 0.005 0.005 0.004 0.005 0.004 +03 58 17.430 +22 11 52.70 1 16.259 15.503 14.768 14.193 13.787 13.778 20.64 2.89 -34.95 2.89 2 GCS9 UGCS J035817.43+221152.6 0.007 0.005 0.004 0.005 0.003 0.003 +04 09 15.300 +25 05 36.60 0 13.026 12.757 12.256 11.647 11.441 11.430 24.83 2.98 -38.40 2.98 2 GCS9 2MASS J04091529+2505368 0.002 0.001 0.001 0.001 0.001 0.001 +03 34 13.470 +25 25 25.30 0 14.776 14.276 13.694 13.124 12.802 25.97 5.29 -32.89 5.29 2 GCS9 UGCS J033413.46+252525.3 0.003 0.002 0.003 0.002 0.001 +03 59 59.850 +25 08 53.60 1 16.368 15.695 15.004 14.433 14.029 14.075 14.87 2.51 -35.69 2.51 2 GCS9 UGCS J035959.84+250853.6 0.008 0.006 0.006 0.004 0.005 0.003 +03 32 32.970 +22 18 12.10 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 2 GCS9 Cl* Melotte 22 DH 20 0.003 0.003 0.003 0.002 0.002 0.002 +03 56 22.310 +21 07 16.90 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 2 GCS9 Cl* Melotte 22 DH 831 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 13.410 +23 31 00.70 0 13.938 13.443 12.795 12.223 11.868 11.886 12.02 2.24 -39.32 2.24 2 GCS9 V* OO Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 03.450 +24 20 57.00 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 2 GCS9 V* V856 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 52.760 +23 27 54.00 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 2 GCS9 V* V747 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 40 49.400 +21 12 55.30 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 2 GCS9 Cl* Melotte 22 DH 152 0.003 0.002 0.002 0.002 0.001 0.001 +03 52 02.280 +24 21 47.90 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 2 GCS9 Cl* Melotte 22 DH 734 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 10.220 +19 55 30.60 0 14.068 13.656 13.061 12.514 12.192 12.201 18.29 5.01 -32.86 5.01 2 GCS9 UGCS J033910.21+195530.5 0.002 0.002 0.002 0.002 0.001 0.001 +03 39 37.760 +19 55 53.30 0 14.324 13.934 13.412 12.811 12.532 12.528 4.36 5.02 -36.62 5.02 2 GCS9 UGCS J033937.75+195553.3 0.002 0.002 0.002 0.002 0.002 0.001 +03 47 31.130 +21 10 51.10 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 2 GCS9 Cl* Melotte 22 DH 519 0.003 0.003 0.003 0.002 0.002 0.001 +03 54 49.920 +19 50 44.90 0 14.727 14.266 13.691 13.126 12.808 12.842 8.65 6.05 -45.98 6.05 2 GCS9 UGCS J035449.91+195044.9 0.003 0.002 0.002 0.002 0.002 0.001 +04 05 50.080 +22 35 53.80 0 14.329 13.919 13.378 12.805 12.498 12.507 14.32 4.97 -32.31 4.97 2 GCS9 UGCS J040550.08+223553.8 0.002 0.002 0.002 0.002 0.001 0.001 +03 54 38.020 +19 54 22.50 0 14.169 13.723 13.136 12.596 12.291 12.305 14.93 6.05 -51.92 6.05 2 GCS9 UGCS J035438.01+195422.5 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 10.870 +21 12 09.70 0 15.115 14.622 14.008 13.457 13.133 13.127 27.96 3.41 -35.33 3.41 2 GCS9 UGCS J033710.86+211209.7 0.004 0.003 0.003 0.002 0.002 0.002 +03 57 19.330 +23 27 37.60 0 15.039 14.541 13.948 13.395 13.065 13.076 17.31 3.00 -34.18 3.00 2 GCS9 2MASS J03571931+2327379 0.004 0.004 0.003 0.002 0.002 0.002 +03 42 57.780 +19 47 06.10 0 16.796 16.118 15.392 14.796 14.398 14.370 19.85 4.07 -37.58 4.07 2 GCS9 UGCS J034257.78+194706.1 0.009 0.007 0.006 0.006 0.007 0.005 +03 37 28.280 +24 24 09.10 0 12.968 12.703 12.189 11.596 11.337 11.397 15.93 2.50 -35.80 2.50 2 GCS9 UGCS J033728.27+242409.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 42 13.490 +24 18 49.60 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 2 GCS9 Cl* Melotte 22 BPL 36 0.005 0.005 0.004 0.003 0.002 +03 41 51.500 +24 19 27.30 0 16.617 15.976 15.244 14.667 14.283 16.53 2.33 -37.48 2.33 2 GCS9 Cl* Melotte 22 BPL 27 0.009 0.008 0.006 0.006 0.004 +03 41 38.810 +24 23 09.20 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2 GCS9 Cl* Melotte 22 BPL 25 0.005 0.004 0.004 0.003 0.002 +03 53 53.320 +22 30 37.50 0 15.687 15.153 14.514 13.995 13.648 13.634 25.44 2.52 -43.14 2.52 2 GCS9 UGCS J035353.32+223037.5 0.006 0.005 0.004 0.003 0.004 0.003 +03 32 37.820 +23 25 59.00 0 15.028 14.538 13.929 13.363 13.041 13.050 20.57 3.42 -34.09 3.42 2 GCS9 UGCS J033237.82+232559.0 0.003 0.003 0.003 0.002 0.002 0.002 +03 41 45.070 +22 28 01.80 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2 GCS9 Cl* Melotte 22 DH 185 0.005 0.004 0.004 0.003 0.003 0.002 +03 54 15.600 +24 20 45.70 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 2 GCS9 Cl* Melotte 22 BPL 308 0.006 0.005 0.004 0.004 0.004 0.003 +04 02 41.900 +22 28 18.30 0 16.439 15.775 15.058 14.528 14.129 14.101 18.57 3.42 -47.04 3.42 2 GCS9 UGCS J040241.90+222818.2 0.007 0.005 0.005 0.004 0.005 0.003 +03 40 28.570 +23 33 42.20 0 16.820 16.083 15.334 14.796 14.375 14.420 24.86 2.54 -48.66 2.54 2 GCS9 UGCS J034028.56+233342.2 0.011 0.008 0.007 0.007 0.007 0.005 +03 42 06.940 +19 54 06.00 0 15.496 15.005 14.353 13.759 13.458 13.462 10.23 5.03 -28.73 5.03 2 GCS9 UGCS J034206.94+195405.9 0.004 0.004 0.003 0.003 0.003 0.002 +03 47 56.640 +24 15 31.70 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 2 GCS9 Cl* Melotte 22 BPL 162 0.004 0.003 0.003 0.002 0.003 0.002 +03 44 09.920 +24 16 03.90 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 2 GCS9 V* V629 Tau 0.002 0.002 0.001 0.001 0.001 +03 47 55.280 +23 19 05.80 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2 GCS9 V* PS Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 20.660 +24 15 10.70 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 2 GCS9 Cl* Melotte 22 HHJ 57 0.004 0.004 0.003 0.002 0.002 +03 40 15.930 +24 22 31.30 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 2 GCS9 Cl* Melotte 22 BPL 11 0.006 0.005 0.004 0.004 0.004 0.003 +04 01 05.810 +23 34 43.90 0 14.117 13.665 13.116 12.586 12.289 26.79 3.54 -44.13 3.54 2 GCS9 UGCS J040105.81+233443.8 0.002 0.002 0.002 0.002 0.001 +04 00 50.520 +23 43 52.90 1 16.184 15.380 14.647 14.089 13.660 24.52 3.56 -40.90 3.56 2 GCS9 UGCS J040050.52+234352.9 0.006 0.004 0.004 0.004 0.002 +03 59 12.920 +24 17 18.40 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 2 GCS9 Cl* Melotte 22 DH 873 0.003 0.003 0.003 0.002 0.002 0.001 +03 43 18.750 +20 07 44.40 0 14.292 13.846 13.306 12.791 12.491 12.478 12.00 3.42 -35.92 3.42 2 GCS9 UGCS J034318.74+200744.3 0.002 0.002 0.002 0.002 0.002 0.001 +03 28 16.070 +22 27 32.10 0 16.039 15.477 14.850 14.262 13.924 13.920 26.29 5.04 -36.31 5.04 2 GCS9 UGCS J032816.07+222732.0 0.005 0.004 0.004 0.005 0.003 0.003 +03 42 58.600 +20 12 45.00 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 2 GCS9 Cl* Melotte 22 DH 247 0.003 0.003 0.003 0.002 0.002 0.001 +03 50 43.070 +23 19 55.70 0 16.002 15.424 14.795 14.265 13.908 13.920 23.59 2.14 -41.03 2.14 2 GCS9 UGCS J035043.06+231955.6 0.006 0.005 0.005 0.004 0.003 0.003 +03 55 30.900 +23 23 50.90 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 2 GCS9 Cl* Melotte 22 DH 815 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 20.000 +22 37 53.70 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 2 GCS9 Cl* Melotte 22 DH 168 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 26.250 +22 34 30.90 0 16.766 16.113 15.394 14.869 14.450 14.467 17.90 2.97 -38.24 2.97 2 GCS9 UGCS J033726.25+223430.8 0.010 0.008 0.006 0.006 0.007 0.005 +04 03 49.740 +23 43 25.20 0 16.769 16.047 15.317 14.756 14.349 14.341 25.63 3.40 -44.29 3.40 2 GCS9 UGCS J040349.73+234325.1 0.009 0.006 0.006 0.005 0.006 0.004 +03 58 52.130 +24 43 48.30 0 14.398 14.003 13.466 12.869 12.581 12.617 18.41 2.48 -35.59 2.48 2 GCS9 UGCS J035852.13+244348.3 0.003 0.003 0.003 0.002 0.002 0.001 +03 38 34.490 +23 40 22.30 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 2 GCS9 Cl* Melotte 22 DH 91 0.004 0.003 0.003 0.002 0.002 0.002 +03 42 05.740 +23 07 14.40 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2 GCS9 UGCS J034205.74+230714.3 0.010 0.008 0.007 0.006 0.007 0.005 +03 26 44.060 +24 39 39.40 0 14.123 13.658 13.081 12.585 12.255 18.85 6.95 -22.57 6.95 2 GCS9 UGCS J032644.06+243939.4 0.002 0.002 0.002 0.002 0.001 +03 43 26.440 +22 42 42.50 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 2 GCS9 V* V846 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 54 31.490 +22 39 01.50 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 2 GCS9 UGCS J035431.48+223901.5 0.009 0.007 0.006 0.005 0.006 0.004 +03 45 37.760 +23 43 50.10 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2 GCS9 Cl* Melotte 22 SHF 10 0.008 0.005 0.004 0.003 0.004 0.003 +03 30 43.820 +27 27 42.80 0 16.033 15.515 14.892 14.273 13.976 13.989 4.89 4.92 -41.76 4.92 2 GCS9 UGCS J033043.82+272742.8 0.005 0.004 0.004 0.005 0.004 0.003 +03 48 57.410 +23 13 59.10 1 16.835 16.033 15.222 14.673 14.194 14.194 14.93 2.17 -36.62 2.17 2 GCS9 UGCS J034857.40+231359.0 0.011 0.007 0.006 0.006 0.005 0.004 +03 33 14.390 +24 50 08.10 0 14.746 14.237 13.646 13.119 12.824 12.808 25.31 2.63 -48.57 2.63 2 GCS9 UGCS J033314.38+245008.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 25.510 +22 27 49.80 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 2 GCS9 2MASS J03442549+2227501 0.008 0.006 0.005 0.006 0.006 0.004 +03 44 29.500 +22 25 24.70 0 15.948 15.406 14.761 14.222 13.858 13.860 23.26 2.52 -37.84 2.52 2 GCS9 UGCS J034429.49+222524.6 0.006 0.005 0.004 0.005 0.004 0.003 +03 28 19.340 +24 49 48.70 0 13.462 13.155 12.649 12.117 11.814 11.03 6.95 -38.57 6.95 2 GCS9 UGCS J032819.34+244948.6 0.002 0.001 0.001 0.001 0.001 +03 28 05.420 +24 46 50.80 0 14.243 13.810 13.304 12.740 12.453 16.36 6.95 -28.13 6.95 2 GCS9 UGCS J032805.42+244650.7 0.002 0.002 0.002 0.002 0.001 +03 28 56.800 +24 45 20.20 0 14.988 14.482 13.880 13.310 13.039 7.77 6.96 -34.47 6.96 2 GCS9 UGCS J032856.79+244520.2 0.003 0.003 0.003 0.003 0.001 +03 37 34.180 +24 42 15.10 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2 GCS9 Cl* Melotte 22 DH 74 0.005 0.004 0.004 0.003 0.003 0.002 +03 37 03.480 +24 44 35.30 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 2 GCS9 V* KL Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 56 28.630 +23 09 03.40 0 13.707 13.314 12.787 12.207 11.924 11.944 17.30 2.99 -35.17 2.99 2 GCS9 UGCS J035628.63+230903.4 0.002 0.002 0.002 0.001 0.001 0.001 +04 01 55.860 +24 44 02.30 0 14.205 13.759 13.190 12.627 12.326 12.347 16.22 2.89 -35.93 2.89 2 GCS9 UGCS J040155.85+244402.3 0.002 0.002 0.002 0.002 0.001 0.001 +03 40 56.930 +29 01 09.10 0 14.565 14.058 13.458 12.891 12.549 12.569 11.58 2.96 -44.15 2.96 2 GCS9 UGCS J034056.93+290109.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 37 37.090 +23 10 57.80 0 14.626 14.203 13.619 13.102 12.764 12.763 21.66 2.98 -35.09 2.98 2 GCS9 UGCS J033737.08+231057.8 0.003 0.003 0.003 0.002 0.002 0.001 +04 06 29.990 +22 33 43.60 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 2 GCS9 Cl* Melotte 22 DH 916 0.002 0.002 0.002 0.002 0.001 0.001 +04 06 48.060 +22 26 38.90 0 15.098 14.674 14.081 13.428 13.126 13.129 5.85 5.08 -27.50 5.08 2 GCS9 UGCS J040648.05+222638.9 0.003 0.003 0.003 0.003 0.002 0.002 +04 10 20.200 +22 49 44.20 0 16.876 16.094 15.352 14.801 14.394 14.366 31.66 6.12 -35.95 6.12 2 GCS9 UGCS J041020.20+224944.2 0.009 0.007 0.006 0.007 0.008 0.004 +04 10 52.290 +22 40 50.70 0 16.595 16.009 15.382 14.707 14.334 14.306 28.77 6.10 -34.40 6.10 2 GCS9 UGCS J041052.28+224050.7 0.008 0.006 0.006 0.006 0.007 0.004 +03 38 25.430 +24 27 11.90 0 16.581 15.897 15.248 14.690 14.314 14.316 23.60 2.52 -36.23 2.52 2 GCS9 UGCS J033825.42+242711.8 0.009 0.007 0.006 0.006 0.006 0.004 +03 49 43.170 +24 39 46.40 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 2 GCS9 Cl* Melotte 22 BPL 215 0.008 0.006 0.005 0.004 0.005 0.003 +03 40 09.690 +23 10 32.40 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 2 GCS9 V* KW Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 40 12.740 +23 09 35.60 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 2 GCS9 V* V429 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +04 02 34.770 +23 08 28.40 0 14.671 14.234 13.621 13.093 12.774 12.817 13.37 3.35 -33.49 3.35 2 GCS9 UGCS J040234.77+230828.4 0.003 0.003 0.002 0.002 0.002 0.001 +03 49 27.580 +24 24 13.70 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 2 GCS9 Cl* Melotte 22 DH 621 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 27.650 +24 31 54.10 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 2 GCS9 V* V359 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 42 31.210 +24 49 21.20 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 2 GCS9 V* V611 Tau 0.004 0.003 0.003 0.002 0.002 +03 41 52.320 +24 41 57.60 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 2 GCS9 Cl* Melotte 22 DH 190 0.004 0.003 0.003 0.002 0.001 +03 38 13.040 +24 38 16.80 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 2 GCS9 Cl* Melotte 22 DH 84 0.003 0.003 0.003 0.002 0.002 0.001 +04 06 49.560 +23 09 03.60 0 13.930 13.558 12.993 12.404 12.134 12.138 15.11 3.74 -32.63 3.74 2 GCS9 UGCS J040649.55+230903.6 0.002 0.002 0.002 0.001 0.001 0.001 +03 34 19.370 +22 48 42.20 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 2 GCS9 Cl* Melotte 22 DH 34 0.007 0.005 0.005 0.004 0.004 0.003 +03 57 05.170 +20 44 16.30 0 16.134 15.509 14.849 14.335 13.970 13.945 23.22 4.01 -41.34 4.01 2 GCS9 UGCS J035705.17+204416.3 0.006 0.005 0.005 0.004 0.005 0.004 +03 50 09.720 +20 41 54.00 0 16.759 16.075 15.362 14.808 14.395 14.384 17.58 3.49 -35.38 3.49 2 GCS9 UGCS J035009.71+204154.0 0.008 0.006 0.006 0.006 0.007 0.006 +03 25 45.370 +22 56 36.80 0 13.572 13.247 12.749 12.147 11.900 11.922 8.91 5.07 -37.88 5.07 2 GCS9 UGCS J032545.37+225636.8 0.002 0.001 0.002 0.001 0.001 0.001 +03 25 38.730 +22 57 39.80 1 15.890 15.269 14.601 13.974 13.618 13.628 24.45 5.11 -47.02 5.11 2 GCS9 UGCS J032538.72+225739.8 0.005 0.004 0.004 0.004 0.003 0.002 +03 50 24.960 +20 42 17.80 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 2 GCS9 Cl* Melotte 22 DH 668 0.001 0.001 0.001 0.001 0.001 0.001 +04 01 50.950 +22 59 15.50 0 16.363 15.631 14.906 14.311 13.912 13.906 20.72 3.38 -38.98 3.38 2 GCS9 UGCS J040150.95+225915.5 0.008 0.005 0.005 0.004 0.005 0.003 +03 41 13.500 +20 51 17.20 0 13.069 12.653 12.096 11.582 11.204 11.227 22.59 3.42 -36.61 3.42 2 GCS9 UGCS J034113.49+205117.2 0.001 0.001 0.001 0.001 0.001 0.001 +03 44 08.810 +23 04 47.50 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 2 GCS9 V* MV Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 53 43.800 +27 50 19.20 0 16.584 16.006 15.337 14.720 14.344 14.344 13.37 3.36 -34.48 3.36 2 GCS9 UGCS J035343.79+275019.1 0.007 0.006 0.006 0.005 0.007 0.007 +03 47 15.660 +19 42 10.50 0 14.049 13.614 13.070 12.494 12.226 12.227 8.84 5.04 -28.59 5.04 2 GCS9 UGCS J034715.66+194210.4 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 30.540 +19 40 39.60 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 2 GCS9 Cl* Melotte 22 DH 753 0.003 0.002 0.002 0.002 0.002 0.001 +03 59 16.640 +19 44 27.20 0 14.532 14.106 13.533 12.964 12.659 12.670 11.83 3.04 -34.49 3.04 2 GCS9 UGCS J035916.64+194427.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 55 23.970 +19 36 48.70 0 12.665 12.398 11.904 11.358 11.013 11.061 27.55 6.05 -29.31 6.05 2 GCS9 UGCS J035523.96+193648.7 0.001 0.001 0.001 0.001 0.001 0.000 +03 40 35.340 +21 33 33.30 0 14.187 13.742 13.238 12.559 12.246 12.253 9.37 3.58 -32.98 3.58 2 GCS9 UGCS J034035.33+213333.3 0.002 0.002 0.002 0.002 0.001 0.001 +03 40 47.810 +21 43 21.70 0 15.850 15.249 14.624 14.026 13.626 13.638 20.22 3.60 -35.96 3.60 2 GCS9 UGCS J034047.80+214321.6 0.005 0.004 0.004 0.004 0.003 0.002 +03 55 50.110 +21 43 27.70 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 2 GCS9 Cl* Melotte 22 DH 819 0.001 0.001 0.001 0.001 0.001 0.001 +03 58 50.060 +21 36 52.90 0 15.384 14.775 14.109 13.558 13.179 13.201 24.07 3.76 -38.06 3.76 2 GCS9 UGCS J035850.06+213652.8 0.005 0.004 0.004 0.003 0.002 0.002 +03 44 52.870 +21 34 16.10 0 14.371 13.850 13.252 12.733 12.411 12.398 16.20 2.65 -35.83 2.65 2 GCS9 UGCS J034452.86+213416.0 0.003 0.002 0.002 0.002 0.002 0.001 +03 40 10.500 +21 44 45.50 0 14.286 13.854 13.282 12.790 12.436 12.464 26.94 3.58 -45.88 3.58 2 GCS9 UGCS J034010.50+214445.4 0.002 0.002 0.002 0.002 0.001 0.001 +03 56 33.510 +19 33 25.20 0 16.665 15.941 15.228 14.681 14.293 14.284 22.45 6.10 -29.57 6.10 2 GCS9 UGCS J035633.50+193325.1 0.008 0.006 0.006 0.005 0.005 0.004 +03 56 49.390 +19 30 05.50 0 13.596 13.242 12.727 12.205 11.878 11.890 12.45 6.05 -26.16 6.05 2 GCS9 UGCS J035649.38+193005.4 0.002 0.001 0.002 0.001 0.001 0.001 +03 56 23.930 +19 23 53.40 0 14.848 14.335 13.702 13.158 12.840 12.830 12.00 6.05 -30.52 6.05 2 GCS9 UGCS J035623.92+192353.3 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 46.940 +21 44 49.00 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 2 GCS9 Cl* Melotte 22 HHJ 128 0.003 0.003 0.003 0.002 0.002 0.001 +03 41 14.460 +19 20 30.70 0 12.714 12.465 12.013 11.465 11.274 11.312 32.96 5.01 -35.20 5.01 2 GCS9 UGCS J034114.45+192030.7 0.001 0.001 0.001 0.001 0.001 0.001 +03 57 09.810 +21 36 27.20 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 2 GCS9 Cl* Melotte 22 DH 850 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 29.740 +19 21 01.50 0 16.507 15.848 15.150 14.568 14.214 14.238 13.83 4.07 -37.81 4.07 2 GCS9 UGCS J035029.73+192101.5 0.007 0.006 0.006 0.006 0.006 0.004 +03 30 57.410 +27 16 46.30 0 16.086 15.546 14.839 14.272 13.954 5.81 7.01 -21.54 7.01 2 GCS9 UGCS J033057.40+271646.2 0.006 0.005 0.005 0.005 0.003 +03 42 38.220 +19 28 57.40 0 15.981 15.445 14.822 14.201 13.860 13.854 7.36 4.00 -35.21 4.00 2 GCS9 UGCS J034238.21+192857.4 0.006 0.005 0.005 0.004 0.004 0.003 +03 49 22.520 +21 37 56.00 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 2 GCS9 Cl* Melotte 22 DH 619 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 57.850 +25 55 29.70 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 2 GCS9 Cl* Melotte 22 DH 120 0.005 0.004 0.004 0.003 0.003 0.002 +03 40 11.930 +25 52 32.30 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 2 GCS9 Cl* Melotte 22 HHJ 29 0.006 0.005 0.005 0.003 0.004 0.003 +03 32 11.550 +21 27 55.70 1 16.385 15.652 14.906 14.339 13.949 13.920 13.64 3.89 -40.00 3.89 2 GCS9 UGCS J033211.55+212755.7 0.007 0.005 0.005 0.005 0.005 0.003 +03 43 27.900 +25 01 40.90 0 15.717 15.051 14.370 13.818 13.439 10.73 2.98 -49.42 2.98 2 GCS9 UGCS J034327.89+250140.9 0.006 0.004 0.004 0.003 0.002 +03 44 11.280 +24 52 34.20 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 2 GCS9 Cl* Melotte 22 HHJ 59 0.005 0.004 0.003 0.003 0.002 +03 39 03.500 +21 25 19.90 0 14.680 14.219 13.605 13.055 12.701 12.734 25.01 3.41 -38.17 3.41 2 GCS9 UGCS J033903.49+212519.9 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 14.880 +21 31 01.30 0 13.657 13.248 12.707 12.190 11.892 11.934 25.20 3.36 -39.29 3.36 2 GCS9 UGCS J035714.87+213101.3 0.002 0.002 0.002 0.001 0.001 0.001 +04 09 48.530 +24 54 00.50 0 15.210 14.746 14.051 13.310 12.940 21.67 4.44 -35.52 4.44 2 GCS9 UGCS J040948.53+245400.4 0.005 0.004 0.003 0.002 0.001 +03 43 43.710 +24 29 15.50 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2 GCS9 Cl* Melotte 22 DH 285 0.002 0.001 0.001 0.001 0.001 +03 38 24.800 +26 15 23.50 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 2 GCS9 Cl* Melotte 22 DH 87 0.002 0.002 0.002 0.001 0.001 0.001 +04 05 41.700 +24 31 35.10 0 16.378 15.833 15.175 14.498 14.178 14.167 24.40 2.97 -34.22 2.97 2 GCS9 UGCS J040541.70+243135.0 0.007 0.005 0.005 0.005 0.005 0.003 +03 41 58.860 +26 12 21.10 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 2 GCS9 V* KQ Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 33 32.390 +26 08 10.20 0 15.097 14.615 13.992 13.411 13.059 15.67 5.32 -27.22 5.32 2 GCS9 UGCS J033332.39+260810.1 0.004 0.003 0.003 0.003 0.002 +03 43 16.110 +24 22 28.00 0 16.600 15.927 15.220 14.697 14.293 14.308 13.35 2.16 -47.16 2.16 2 GCS9 UGCS J034316.11+242227.9 0.009 0.007 0.006 0.006 0.008 0.004 +03 49 36.320 +26 02 16.30 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 2 GCS9 Cl* Melotte 22 DH 633 0.005 0.004 0.004 0.003 0.003 0.002 +03 46 52.970 +24 15 07.80 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 2 GCS9 Cl* Melotte 22 MHO 7 0.008 0.006 0.005 0.004 0.005 0.003 +03 47 01.850 +24 13 28.10 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 2 GCS9 Cl* Melotte 22 MHO 10 0.007 0.005 0.005 0.004 0.005 0.003 +03 47 15.460 +24 23 31.00 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 2 GCS9 Cl* Melotte 22 MHO 12 0.003 0.003 0.003 0.002 0.002 0.001 +03 52 55.920 +24 57 41.80 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2 GCS9 Cl* Melotte 22 BPL 275 0.008 0.007 0.006 0.005 0.006 0.004 +03 44 25.580 +22 40 07.90 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 2 GCS9 UGCS J034425.58+224007.8 0.008 0.005 0.005 0.004 0.005 0.003 +03 37 30.690 +24 50 54.30 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 2 GCS9 Cl* Melotte 22 HHJ 110 0.003 0.003 0.003 0.002 0.002 0.002 +04 00 15.860 +25 01 46.30 0 13.460 13.089 12.543 11.954 11.659 11.681 10.70 2.89 -39.78 2.89 2 GCS9 UGCS J040015.86+250146.2 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 05.180 +22 04 52.70 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 2 GCS9 UGCS J034905.17+220452.6 0.011 0.008 0.007 0.006 0.007 0.005 +04 04 38.550 +21 46 47.70 0 13.894 13.477 12.956 12.429 12.123 12.120 29.74 5.07 -28.67 5.07 2 GCS9 UGCS J040438.55+214647.7 0.002 0.002 0.002 0.002 0.001 0.001 +04 07 29.340 +21 46 09.80 0 14.170 13.699 13.175 12.658 12.334 12.339 12.98 4.97 -51.85 4.97 2 GCS9 UGCS J040729.34+214609.8 0.002 0.002 0.002 0.002 0.001 0.001 +03 59 39.840 +21 54 27.70 0 13.543 13.158 12.617 12.028 11.764 11.777 10.82 2.84 -38.32 2.84 2 GCS9 UGCS J035939.84+215427.7 0.002 0.001 0.002 0.001 0.001 0.001 +03 40 11.980 +21 48 31.80 1 16.578 15.884 15.169 14.580 14.187 14.178 24.16 2.54 -40.67 2.54 2 GCS9 UGCS J034011.98+214831.7 0.010 0.007 0.007 0.005 0.006 0.004 +03 45 57.710 +24 03 04.90 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 2 GCS9 Cl* Melotte 22 HHJ 27 0.006 0.005 0.004 0.003 0.004 0.003 +03 37 53.260 +20 23 01.30 0 14.549 14.107 13.550 13.014 12.698 12.732 23.35 3.86 -53.40 3.86 2 GCS9 UGCS J033753.25+202301.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 32 00.120 +20 23 45.90 0 16.268 15.657 15.038 14.458 14.131 14.118 27.13 6.12 -30.46 6.12 2 GCS9 UGCS J033200.11+202345.8 0.007 0.005 0.005 0.004 0.005 0.003 +03 49 56.070 +20 22 52.30 0 14.520 14.010 13.453 12.888 12.591 12.601 27.18 3.42 -44.80 3.42 2 GCS9 UGCS J034956.07+202252.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 26.090 +24 05 09.50 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 2 GCS9 2MASS J03462608+2405096 0.011 0.006 0.005 0.004 0.005 0.003 +03 51 04.740 +20 14 06.10 0 14.534 14.114 13.550 12.881 12.645 12.663 15.57 3.42 -33.25 3.42 2 GCS9 UGCS J035104.73+201406.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 33.870 +20 19 38.30 0 16.290 15.620 14.949 14.435 14.012 14.021 24.49 3.44 -42.26 3.44 2 GCS9 UGCS J035033.86+201938.3 0.007 0.005 0.005 0.004 0.004 0.004 +03 55 34.430 +23 58 28.30 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 2 GCS9 Cl* Melotte 22 DH 818 0.004 0.003 0.003 0.003 0.002 +03 36 19.260 +24 10 52.30 0 13.345 13.022 12.518 11.979 11.704 11.709 25.71 2.60 -41.47 2.60 2 GCS9 UGCS J033619.26+241052.3 0.002 0.002 0.001 0.001 0.001 0.001 +03 42 40.230 +23 59 21.50 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 2 GCS9 V* LO Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 28 35.630 +24 00 43.70 0 16.995 16.267 15.527 14.966 14.552 14.537 19.57 3.89 -36.94 3.89 2 GCS9 UGCS J032835.63+240043.7 0.010 0.007 0.007 0.006 0.007 0.005 +03 46 09.880 +21 05 52.40 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 2 GCS9 Cl* Melotte 22 DH 425 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 22.310 +21 05 20.00 0 16.232 15.519 14.873 14.327 13.935 13.935 22.61 2.67 -45.95 2.67 2 GCS9 UGCS J034622.31+210519.9 0.007 0.005 0.005 0.005 0.006 0.003 +03 57 52.350 +21 02 15.80 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 2 GCS9 Cl* Melotte 22 DH 859 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 35.330 +20 57 56.60 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 2 GCS9 Cl* Melotte 22 DH 146 0.001 0.001 0.001 0.001 0.001 0.001 +03 32 35.580 +27 03 36.70 0 17.100 16.282 15.495 14.865 14.489 14.499 23.23 5.01 -52.46 5.01 2 GCS9 UGCS J033235.57+270336.6 0.010 0.007 0.006 0.009 0.006 0.005 +03 43 28.050 +26 29 55.50 0 17.173 16.372 15.618 15.060 14.682 14.649 17.49 3.01 -38.50 3.01 2 GCS9 UGCS J034328.04+262955.5 0.015 0.010 0.009 0.008 0.008 0.005 +03 36 53.300 +26 34 27.90 1 17.046 16.171 15.379 14.804 14.355 14.353 15.54 3.33 -34.04 3.33 2 GCS9 UGCS J033653.30+263427.9 0.012 0.008 0.007 0.006 0.006 0.004 +03 46 22.250 +23 52 26.60 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 2 GCS9 Cl* Melotte 22 SHF 31 0.015 0.008 0.007 0.006 0.007 0.005 +04 00 11.720 +23 23 08.90 0 17.275 16.419 15.671 15.086 14.642 14.623 21.83 3.45 -37.29 3.45 2 GCS9 UGCS J040011.71+232308.9 0.014 0.009 0.008 0.008 0.009 0.006 +03 46 05.110 +23 45 34.90 1 17.229 16.347 15.522 14.851 14.385 14.404 15.88 2.25 -39.21 2.25 2 GCS9 Cl* Melotte 22 SHF 25 0.015 0.008 0.007 0.005 0.006 0.004 +03 58 00.620 +21 18 20.80 1 17.399 16.380 15.545 14.931 14.445 14.423 24.81 3.43 -32.13 3.43 2 GCS9 UGCS J035800.61+211820.8 0.015 0.009 0.007 0.006 0.007 0.006 +04 05 23.120 +22 29 03.30 0 17.120 16.364 15.605 15.015 14.577 14.567 24.92 5.10 -41.55 5.10 2 GCS9 UGCS J040523.12+222903.3 0.011 0.008 0.007 0.010 0.006 0.006 +03 47 20.480 +19 54 25.50 1 17.011 16.054 15.210 14.615 14.152 14.140 28.11 5.10 -39.39 5.10 2 GCS9 UGCS J034720.47+195425.5 0.010 0.006 0.006 0.006 0.005 0.004 +04 02 43.660 +22 43 42.50 0 17.125 16.400 15.711 15.021 14.640 14.615 22.00 3.44 -31.92 3.44 2 GCS9 UGCS J040243.65+224342.5 0.013 0.009 0.008 0.007 0.009 0.006 +03 40 45.160 +27 50 40.40 1 17.074 16.218 15.404 14.816 14.321 14.333 12.37 3.34 -40.37 3.34 2 GCS9 UGCS J034045.16+275040.4 0.012 0.008 0.007 0.007 0.007 0.005 +03 42 47.300 +23 00 40.30 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 2 GCS9 UGCS J034247.29+230040.3 0.013 0.009 0.008 0.007 0.008 0.005 +03 45 19.730 +19 15 47.40 0 17.061 16.401 15.697 15.151 14.745 14.731 8.46 4.11 -32.05 4.11 2 GCS9 UGCS J034519.72+191547.4 0.010 0.008 0.008 0.007 0.009 0.006 +03 47 39.020 +24 36 22.20 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 2 GCS9 2MASS J03473900+2436226 0.013 0.008 0.007 0.006 0.008 0.005 +03 51 05.970 +24 36 16.90 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 2 GCS9 2MASS J03510597+2436171 0.013 0.008 0.007 0.007 0.009 0.006 +03 34 38.610 +24 51 28.50 1 17.097 16.247 15.459 14.920 14.445 14.466 13.39 2.69 -37.21 2.69 2 GCS9 UGCS J033438.61+245128.4 0.011 0.008 0.007 0.007 0.008 0.005 +04 09 17.800 +26 03 31.20 1 17.120 16.480 15.759 15.003 14.603 6.56 4.49 -33.73 4.49 2 GCS9 UGCS J040917.79+260331.2 0.013 0.010 0.009 0.006 0.005 +03 43 33.120 +27 55 49.60 0 17.880 16.894 16.092 15.463 15.044 15.026 17.70 4.09 -38.59 4.09 2 GCS9 UGCS J034333.12+275549.5 0.018 0.011 0.009 0.013 0.013 0.008 +04 00 18.830 +27 09 46.80 0 17.560 16.743 16.002 15.448 15.005 15.036 10.06 3.09 -34.16 3.09 2 GCS9 UGCS J040018.82+270946.7 0.019 0.012 0.012 0.009 0.010 0.009 +03 33 30.460 +27 32 55.40 0 17.238 16.553 15.809 15.260 14.841 14.846 18.69 3.90 -36.97 3.90 2 GCS9 UGCS J033330.46+273255.4 0.011 0.008 0.008 0.009 0.010 0.007 +04 04 24.850 +28 39 03.60 0 17.526 16.894 16.132 15.388 14.992 15.035 18.79 3.11 -35.18 3.11 2 GCS9 UGCS J040424.84+283903.5 0.016 0.013 0.011 0.008 0.010 0.008 +03 34 50.820 +25 34 46.40 0 17.916 16.901 16.095 15.494 15.066 21.10 5.70 -37.46 5.70 2 GCS9 UGCS J033450.81+253446.3 0.020 0.012 0.011 0.015 0.007 +04 03 01.220 +25 24 23.00 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 2 GCS9 Cl* Melotte 22 DH 905 0.012 0.009 0.008 0.007 0.009 0.007 +03 52 15.630 +25 31 09.10 0 17.722 16.782 15.959 15.395 14.936 14.897 13.63 3.10 -42.16 3.10 2 GCS9 UGCS J035215.63+253109.1 0.019 0.012 0.010 0.010 0.010 0.008 +03 53 41.460 +26 42 22.30 0 17.559 16.958 16.192 15.590 15.213 15.193 20.55 3.12 -38.29 3.12 2 GCS9 UGCS J035341.45+264222.3 0.020 0.013 0.013 0.009 0.012 0.008 +03 48 38.370 +22 33 51.80 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 2 GCS9 UGCS J034838.37+223351.7 0.017 0.011 0.008 0.008 0.009 0.006 +03 28 20.710 +25 15 40.00 0 17.594 16.841 16.097 15.503 15.097 11.14 7.37 -20.79 7.37 2 GCS9 UGCS J032820.71+251540.0 0.014 0.010 0.009 0.013 0.007 +03 58 22.560 +22 28 36.30 0 17.356 16.631 15.918 15.234 14.804 9.62 6.01 -58.05 6.01 2 GCS9 UGCS J035822.55+222836.3 0.014 0.010 0.009 0.012 0.008 +03 38 43.690 +24 11 24.50 0 17.599 16.762 15.913 15.343 14.861 14.851 24.38 2.57 -36.40 2.57 2 GCS9 UGCS J033843.68+241124.5 0.018 0.011 0.009 0.009 0.010 0.006 +03 40 35.500 +23 13 07.40 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 2 GCS9 UGCS J034035.49+231307.3 0.024 0.014 0.012 0.011 0.013 0.008 +03 56 21.530 +23 08 41.50 0 17.934 16.982 16.151 15.536 15.055 15.042 18.32 3.20 -36.21 3.20 2 GCS9 UGCS J035621.52+230841.4 0.027 0.018 0.014 0.009 0.012 0.009 +03 56 40.930 +22 59 40.60 0 17.830 16.872 16.002 15.424 14.921 14.950 13.47 3.14 -35.44 3.14 2 GCS9 UGCS J035640.92+225940.5 0.027 0.015 0.011 0.008 0.009 0.007 +03 51 25.970 +20 36 12.10 0 17.108 16.526 15.822 15.270 14.881 14.835 15.46 3.52 -35.21 3.52 2 GCS9 UGCS J035125.96+203612.0 0.012 0.008 0.008 0.008 0.009 0.007 +03 37 27.990 +23 02 19.60 0 17.750 16.811 15.973 15.390 14.893 14.904 19.88 3.09 -49.46 3.09 2 GCS9 UGCS J033727.98+230219.5 0.019 0.012 0.010 0.009 0.010 0.007 +04 07 12.860 +27 26 05.40 0 17.420 16.620 15.882 15.297 14.866 14.886 11.86 3.06 -46.68 3.06 2 GCS9 UGCS J040712.86+272605.4 0.015 0.011 0.009 0.007 0.009 0.008 +03 31 17.910 +27 27 46.10 0 17.576 16.828 16.102 15.467 15.101 2.80 7.41 -33.43 7.41 2 GCS9 UGCS J033117.91+272746.1 0.014 0.010 0.010 0.014 0.007 +03 51 35.200 +19 26 25.80 0 17.395 16.730 16.037 15.430 15.101 15.089 13.46 4.25 -36.40 4.25 2 GCS9 UGCS J035135.19+192625.8 0.013 0.009 0.010 0.010 0.013 0.007 +03 48 19.020 +24 25 12.70 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 2 GCS9 2MASS J03481900+2425130 0.020 0.011 0.009 0.008 0.011 0.007 +03 40 53.660 +28 21 11.50 1 18.880 17.570 16.490 15.852 15.194 15.199 13.91 4.06 -31.57 4.06 2 GCS9 UGCS J034053.65+282111.5 0.037 0.016 0.012 0.016 0.013 0.008 +03 34 04.410 +25 31 33.60 0 18.393 17.293 16.390 15.781 15.254 31.63 5.79 -38.18 5.79 2 GCS9 UGCS J033404.41+253133.5 0.029 0.015 0.012 0.018 0.008 +03 33 49.220 +19 59 52.00 1 18.539 18.104 16.453 15.767 15.433 15.492 1.67 6.44 -40.37 6.44 2 GCS9 UGCS J033349.21+195952.0 0.032 0.033 0.014 0.012 0.015 0.010 +03 57 30.820 +23 41 22.60 0 18.532 17.359 16.440 15.839 15.265 15.297 20.84 3.11 -46.90 3.11 2 GCS9 UGCS J035730.82+234122.5 0.032 0.018 0.013 0.013 0.013 0.009 +04 07 10.040 +24 12 32.40 0 18.235 17.375 16.480 15.868 15.404 15.382 20.30 3.50 -47.43 3.50 2 GCS9 UGCS J040710.04+241232.3 0.025 0.015 0.013 0.012 0.014 0.009 +03 39 14.770 +23 23 31.70 0 18.455 17.428 16.499 15.853 15.312 15.326 17.63 3.27 -35.01 3.27 2 GCS9 UGCS J033914.77+232331.7 0.034 0.019 0.015 0.013 0.014 0.011 +03 50 37.960 +23 03 05.00 0 18.420 17.267 16.354 15.734 15.191 15.195 15.37 2.36 -44.53 2.36 2 GCS9 UGCS J035037.95+230305.0 0.033 0.017 0.013 0.014 0.011 0.009 +03 36 03.850 +22 52 03.50 1 18.072 16.873 15.913 15.240 14.679 14.679 22.46 3.15 -46.19 3.15 2 GCS9 UGCS J033603.84+225203.4 0.026 0.014 0.010 0.008 0.008 0.006 +03 43 40.310 +24 30 11.20 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2 GCS9 2MASS J03434028+2430113 0.038 0.021 0.014 0.015 0.010 +03 52 18.640 +24 04 28.10 0 18.327 17.280 16.395 15.738 15.202 15.236 15.37 2.40 -46.07 2.40 2 GCS9 2MASS J03521864+2404283 0.031 0.019 0.015 0.014 0.015 0.010 +03 45 45.360 +27 59 27.60 0 19.568 18.074 17.104 16.454 15.870 15.859 22.97 3.88 -34.58 3.88 2 GCS9 UGCS J034545.35+275927.5 0.100 0.036 0.028 0.025 0.023 0.015 +03 53 00.850 +28 39 04.10 0 20.062 19.110 17.754 17.030 16.313 16.293 15.53 6.33 -39.81 6.33 2 GCS9 UGCS J035300.85+283904.1 0.100 0.060 0.035 0.039 0.038 0.026 +03 45 24.330 +29 23 10.90 0 19.979 18.833 17.716 17.085 16.422 16.407 4.16 7.63 -22.76 7.63 2 GCS9 UGCS J034524.32+292310.8 0.103 0.053 0.032 0.029 0.033 0.025 +03 49 16.680 +27 40 21.80 0 18.545 17.513 16.598 15.957 15.425 15.434 17.98 3.18 -33.47 3.18 2 GCS9 UGCS J034916.67+274021.8 0.031 0.018 0.015 0.016 0.017 0.015 +03 54 06.750 +25 37 45.30 0 18.855 17.669 16.724 16.098 15.522 15.498 15.81 3.44 -37.94 3.44 2 GCS9 UGCS J035406.75+253745.3 0.045 0.026 0.018 0.019 0.017 0.013 +03 50 08.270 +25 30 51.70 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 2 GCS9 UGCS J035008.27+253051.6 0.108 0.041 0.027 0.027 0.026 0.020 +03 47 04.410 +24 47 27.30 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2 GCS9 UGCS J034704.41+244727.3 0.249 0.101 0.041 0.034 0.045 0.027 +03 47 46.780 +25 35 16.60 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 2 GCS9 UGCS J034746.77+253516.5 0.168 0.059 0.033 0.033 0.032 0.022 +03 51 47.660 +24 39 58.90 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 2 GCS9 UGCS J035147.65+243958.9 0.157 0.053 0.028 0.026 0.029 0.019 +03 56 16.380 +23 54 51.30 0 18.685 17.753 16.776 16.182 15.634 15.596 9.87 3.22 -39.24 3.22 2 GCS9 UGCS J035616.37+235451.3 0.036 0.024 0.017 0.019 0.018 0.012 +03 58 05.150 +22 17 27.90 0 20.161 18.583 17.333 16.594 15.971 15.972 23.29 3.91 -48.55 3.91 2 GCS9 UGCS J035805.15+221727.8 0.124 0.043 0.026 0.039 0.020 0.019 +03 48 27.360 +23 46 16.20 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 2 GCS9 UGCS J034827.36+234616.2 0.246 0.114 0.045 0.038 0.039 0.027 +03 40 06.820 +25 15 49.20 0 18.988 17.792 16.838 16.183 15.664 15.610 20.86 2.86 -34.63 2.86 2 GCS9 UGCS J034006.81+251549.2 0.053 0.025 0.018 0.021 0.021 0.012 +03 55 27.630 +25 49 40.60 0 18.871 17.904 16.953 16.336 15.855 15.810 21.98 3.53 -52.06 3.53 2 GCS9 UGCS J035527.62+254940.6 0.048 0.027 0.020 0.017 0.022 0.017 +03 56 44.750 +25 30 10.70 0 18.802 17.819 16.859 16.230 15.664 15.687 25.10 3.01 -34.70 3.01 2 GCS9 UGCS J035644.74+253010.6 0.049 0.029 0.021 0.015 0.018 0.015 +03 45 04.410 +24 15 16.60 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 2 GCS9 UGCS J034504.41+241516.6 0.203 0.070 0.036 0.031 0.037 0.023 +03 33 00.000 +24 18 24.70 0 20.076 18.520 17.447 16.641 16.125 16.084 18.97 3.41 -44.11 3.41 2 GCS9 UGCS J033259.99+241824.6 0.128 0.050 0.033 0.033 0.040 0.022 +03 56 00.910 +22 29 08.70 0 18.397 17.477 16.589 15.928 15.448 15.432 21.10 2.75 -37.24 2.75 2 GCS9 UGCS J035600.91+222908.7 0.033 0.019 0.015 0.012 0.015 0.011 +03 36 18.940 +23 33 17.70 0 20.060 18.441 17.328 16.631 16.003 16.073 17.36 3.52 -38.71 3.52 2 GCS9 UGCS J033618.93+233317.7 0.135 0.050 0.030 0.032 0.034 0.020 +03 47 48.910 +24 17 06.50 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 2 GCS9 UGCS J034748.90+241706.5 0.177 0.075 0.033 0.029 0.036 0.025 +03 43 50.160 +24 12 29.70 0 20.264 18.895 17.579 16.861 16.149 20.17 3.10 -36.90 3.10 2 GCS9 UGCS J034350.15+241229.7 0.147 0.065 0.029 0.034 0.020 +03 44 05.250 +22 50 13.50 0 20.066 18.839 17.666 16.895 16.134 16.158 19.70 3.13 -45.22 3.13 2 GCS9 Cl* Melotte 22 IPL 57 0.147 0.066 0.039 0.035 0.032 0.021 +03 48 11.270 +23 17 25.20 0 19.477 18.335 17.220 16.665 16.007 16.019 24.70 3.10 -48.41 3.10 2 GCS9 UGCS J034811.27+231725.2 0.085 0.041 0.028 0.032 0.025 0.022 +03 27 04.710 +24 40 13.70 0 20.770 19.508 18.154 17.395 16.720 -20.28 14.33 -21.25 14.33 2 GCS9 UGCS J032704.71+244013.7 0.190 0.097 0.051 0.072 0.031 +03 44 18.350 +23 15 22.30 0 18.547 17.519 16.552 15.957 15.400 15.438 14.83 2.46 -47.82 2.46 2 GCS9 UGCS J034418.34+231522.3 0.040 0.020 0.015 0.014 0.016 0.010 +03 54 14.070 +23 17 51.90 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 2 GCS9 UGCS J035414.06+231751.9 0.136 0.062 0.035 0.031 0.030 0.021 +03 46 55.490 +23 11 16.00 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 2 GCS9 UGCS J034655.48+231116.0 0.180 0.066 0.041 0.032 0.038 0.026 +03 48 30.740 +22 44 50.20 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 2 GCS9 UGCS J034830.74+224450.2 0.193 0.065 0.039 0.035 0.027 0.022 +03 48 31.530 +24 34 37.20 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 2 GCS9 UGCS J034831.52+243437.2 0.065 0.025 0.015 0.012 0.014 0.009 +03 51 05.220 +23 15 37.70 0 20.207 19.022 17.842 17.041 16.374 16.313 17.05 3.13 -40.03 3.13 2 GCS9 UGCS J035105.21+231537.7 0.152 0.065 0.035 0.040 0.037 0.024 +03 52 02.100 +23 15 45.40 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 2 GCS9 2MASS J03520209+2315451 0.043 0.022 0.014 0.017 0.017 0.012 +03 43 07.150 +20 41 57.00 0 19.556 18.299 17.188 16.411 15.877 15.884 23.63 4.17 -37.64 4.17 2 GCS9 UGCS J034307.15+204156.9 0.071 0.033 0.023 0.022 0.024 0.021 +03 33 30.240 +21 20 50.50 0 19.630 18.597 17.546 16.802 16.378 16.403 3.11 5.57 -44.71 5.57 2 GCS9 UGCS J033330.23+212050.5 0.069 0.040 0.028 0.032 0.035 0.026 +03 54 12.540 +19 15 05.80 0 20.321 18.802 17.562 16.719 16.048 16.072 7.03 6.90 -37.98 6.90 2 GCS9 UGCS J035412.54+191505.7 0.124 0.044 0.028 0.021 0.022 0.016 +03 39 18.810 +24 27 37.80 0 20.092 18.529 17.384 16.652 15.944 16.010 21.70 3.21 -42.51 3.21 2 GCS9 UGCS J033918.80+242737.8 0.132 0.052 0.031 0.033 0.028 0.018 +03 58 31.810 +26 47 38.70 0 20.342 18.971 17.810 17.035 16.342 16.469 14.01 3.98 -35.22 3.98 2 GCS9 UGCS J035831.80+264738.7 0.124 0.056 0.034 0.048 0.044 0.027 +03 38 05.890 +26 15 03.70 0 20.001 18.573 17.436 16.664 16.051 16.064 19.43 3.91 -39.39 3.91 2 GCS9 UGCS J033805.88+261503.7 0.126 0.049 0.030 0.025 0.027 0.018 +03 48 40.630 +26 17 09.70 0 20.203 18.726 17.655 17.168 16.407 16.359 19.80 4.09 -32.05 4.09 2 GCS9 UGCS J034840.63+261709.7 0.148 0.066 0.039 0.046 0.036 0.022 +03 32 58.900 +26 08 38.40 0 20.923 19.834 18.160 17.335 16.613 2.05 10.05 -30.53 10.05 2 GCS9 UGCS J033258.89+260838.3 0.244 0.133 0.055 0.077 0.030 +03 46 32.130 +24 23 14.60 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 2 GCS9 Cl* Melotte 22 SHF 34 0.066 0.027 0.018 0.017 0.021 0.014 +03 46 20.270 +23 58 18.90 1 19.259 18.174 17.034 16.269 15.650 15.585 12.97 2.40 -35.00 2.40 2 GCS9 UGCS J034620.27+235818.8 0.146 0.047 0.023 0.017 0.018 0.012 +03 44 18.230 +24 05 47.20 0 20.411 19.235 17.772 16.984 16.316 20.09 3.23 -35.92 3.23 2 GCS9 UGCS J034418.23+240547.1 0.182 0.080 0.035 0.038 0.023 +03 34 34.800 +27 40 36.50 0 12.822 12.654 12.246 11.697 11.560 11.604 19.82 3.74 -37.63 3.74 0.83 1 GCS9 UGCS J033434.79+274036.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 52 14.060 +28 24 40.90 0 12.345 12.198 11.835 11.446 11.311 11.332 15.22 2.93 -38.30 2.93 0.73 1 GCS9 UGCS J035214.06+282440.8 0.001 0.001 0.001 0.001 0.001 0.001 +03 58 18.450 +25 33 56.00 0 12.415 12.278 11.950 11.653 11.462 11.478 14.63 2.47 -38.40 2.47 0.69 1 GCS9 UGCS J035818.45+253356.0 0.001 0.001 0.001 0.001 0.001 0.001 +03 26 13.750 +25 27 46.80 0 12.564 12.357 11.926 11.507 11.265 20.99 6.87 -37.32 6.87 0.79 1 GCS9 UGCS J032613.74+252746.7 0.001 0.001 0.001 0.001 0.001 +03 33 38.020 +25 20 16.90 0 12.908 12.498 11.930 11.512 11.225 24.95 5.30 -41.14 5.30 0.65 1 GCS9 UGCS J033338.02+252016.8 0.001 0.001 0.001 0.001 0.001 +03 44 30.080 +25 35 46.80 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 0.81 1 GCS9 V* V513 Tau 0.001 0.001 0.001 0.001 0.000 +03 43 05.540 +24 49 28.30 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 0.86 1 GCS9 V* LS Tau 0.001 0.001 0.001 0.001 0.000 +03 43 52.150 +24 50 29.50 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 0.60 1 GCS9 V* MR Tau 0.001 0.001 0.001 0.001 0.000 +03 49 02.350 +25 43 24.10 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 600 0.001 0.001 0.001 0.001 0.001 0.001 +03 42 02.850 +22 22 42.90 0 12.613 12.430 12.021 11.494 11.211 18.81 7.95 -45.14 7.95 0.79 1 GCS9 Cl* Melotte 22 SK 711 0.001 0.001 0.001 0.001 0.001 +03 52 42.510 +25 07 02.70 0 12.809 12.594 12.168 11.644 11.471 11.491 22.89 2.23 -36.88 2.23 0.67 1 GCS9 Cl* Melotte 22 SRS 33701 0.001 0.001 0.001 0.001 0.001 0.001 +03 52 10.580 +29 01 12.10 0 12.489 12.328 11.917 11.584 11.201 11.269 21.59 3.07 -35.82 3.07 0.64 1 GCS9 UGCS J035210.58+290112.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 41 57.740 +23 40 05.80 0 12.352 12.196 11.846 11.578 11.384 11.414 14.80 2.12 -38.46 2.12 0.71 1 GCS9 Cl* Melotte 22 SRS 83446 0.001 0.001 0.001 0.001 0.001 0.001 +03 55 32.840 +23 19 08.00 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 0.61 1 GCS9 Cl* Melotte 22 DH 816 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 10.170 +23 00 03.90 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 554 0.001 0.001 0.001 0.001 0.001 0.001 +03 56 08.590 +21 45 47.70 0 12.574 11.870 11.320 11.082 11.146 18.71 2.62 -38.21 2.62 0.85 1 GCS9 V* V581 Tau 0.001 0.001 0.001 0.001 0.001 +03 53 45.760 +21 48 52.50 0 12.830 12.662 12.296 11.818 11.689 11.729 19.80 2.50 -39.40 2.50 0.88 1 GCS9 UGCS J035345.75+214852.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 54 34.800 +21 53 01.70 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 0.87 1 GCS9 V* V577 Tau 0.001 0.001 0.001 0.001 0.001 +03 43 42.140 +24 34 23.10 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 0.87 1 GCS9 V* MO Tau 0.001 0.001 0.001 0.001 0.001 +04 00 59.070 +24 08 32.20 0 12.785 12.566 12.116 11.564 11.384 13.85 3.54 -39.58 3.54 0.66 1 GCS9 UGCS J040059.07+240832.2 0.001 0.001 0.001 0.001 0.001 +03 46 12.880 +24 03 15.60 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 0.81 1 GCS9 V* V1189 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 44.080 +24 04 26.60 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 0.78 1 GCS9 V* V787 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 42 10.930 +24 05 08.40 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 0.85 1 GCS9 V* LM Tau 0.001 0.001 0.001 0.001 0.001 +04 00 21.250 +28 16 49.70 0 13.141 12.934 12.512 12.011 11.880 11.905 21.55 3.86 -37.30 3.86 0.64 1 GCS9 UGCS J040021.24+281649.7 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 47.460 +28 26 24.60 0 13.148 12.951 12.542 11.990 11.840 11.865 13.41 2.93 -47.11 2.93 0.68 1 GCS9 UGCS J035147.45+282624.6 0.001 0.001 0.001 0.001 0.001 0.001 +04 00 02.390 +25 39 34.00 0 13.797 13.476 12.981 12.320 12.070 12.076 20.75 3.31 -46.98 3.31 0.85 1 GCS9 UGCS J040002.38+253933.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 18.450 +25 03 44.90 0 13.853 13.520 13.052 12.497 12.198 12.229 21.92 2.48 -45.60 2.48 0.86 1 GCS9 UGCS J033718.45+250344.8 0.002 0.002 0.002 0.002 0.001 0.001 +03 36 47.500 +26 38 27.60 0 13.112 12.943 12.541 12.080 11.931 11.931 18.09 3.30 -37.78 3.30 0.79 1 GCS9 UGCS J033647.50+263827.5 0.002 0.001 0.001 0.001 0.001 0.001 +04 00 49.330 +25 12 10.60 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.89 1 GCS9 Cl* Melotte 22 DH 895 0.002 0.002 0.002 0.001 0.002 0.001 +03 54 48.000 +25 12 30.20 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.12 2.23 0.87 1 GCS9 Cl* Melotte 22 BPL 318 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 44.280 +22 27 09.70 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.90 1 GCS9 Cl* Melotte 22 DH 47 0.002 0.002 0.002 0.001 0.001 0.001 +03 33 40.840 +20 09 48.00 0 13.488 13.221 12.781 12.282 12.017 12.032 21.02 6.09 -45.62 6.09 0.89 1 GCS9 UGCS J033340.83+200948.0 0.002 0.001 0.002 0.001 0.001 0.001 +03 36 29.050 +20 11 19.60 0 13.927 13.706 13.216 12.603 12.461 12.464 16.96 3.85 -40.17 3.85 0.90 1 GCS9 UGCS J033629.05+201119.6 0.002 0.002 0.002 0.001 0.002 0.001 +03 59 25.170 +24 14 56.40 0 13.930 13.724 13.271 12.606 12.467 12.483 16.99 2.96 -44.63 2.96 0.93 1 GCS9 UGCS J035925.17+241456.3 0.002 0.002 0.002 0.002 0.002 0.001 +03 37 17.950 +22 28 18.00 0 13.830 13.597 13.115 12.467 12.257 12.268 20.09 2.94 -37.15 2.94 0.70 1 GCS9 UGCS J033717.94+222817.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 54.520 +23 33 31.30 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 0.70 1 GCS9 V* V389 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 03.830 +22 50 37.90 0 13.298 13.029 12.569 11.989 11.797 11.789 15.03 2.13 -40.74 2.13 0.87 1 GCS9 UGCS J035103.83+225037.9 0.002 0.001 0.002 0.001 0.001 0.001 +03 43 24.400 +23 13 30.20 0 13.528 12.672 12.085 11.771 11.765 20.41 2.30 -41.63 2.30 0.92 1 GCS9 UGCS J034324.40+231330.1 0.002 0.002 0.001 0.001 0.001 +03 32 15.300 +20 47 41.70 0 13.718 13.478 13.020 12.433 12.215 12.228 17.60 6.09 -40.17 6.09 0.91 1 GCS9 UGCS J033215.29+204741.6 0.002 0.002 0.002 0.001 0.001 0.001 +04 03 24.940 +22 52 17.00 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.92 1 GCS9 Cl* Melotte 22 DH 907 0.002 0.002 0.002 0.001 0.001 0.001 +03 56 10.400 +23 02 23.70 0 13.356 12.519 11.944 11.625 11.650 15.48 3.06 -38.16 3.06 0.74 1 GCS9 UGCS J035610.39+230223.7 0.002 0.002 0.001 0.001 0.001 +03 52 25.930 +21 50 31.50 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 0.83 1 GCS9 Cl* Melotte 22 DH 752 0.002 0.002 0.002 0.001 0.001 0.001 +03 55 35.350 +27 46 29.00 0 13.589 13.393 12.952 12.392 12.253 12.273 20.70 3.30 -40.12 3.30 0.89 1 GCS9 UGCS J035535.35+274628.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 38.230 +21 51 25.40 0 13.279 13.079 12.659 12.077 11.911 11.936 19.33 2.93 -40.04 2.93 0.90 1 GCS9 UGCS J033538.23+215125.4 0.002 0.001 0.002 0.001 0.001 0.001 +03 47 21.730 +19 18 28.70 0 13.854 13.603 13.121 12.524 12.312 12.319 17.93 5.04 -48.08 5.04 0.82 1 GCS9 UGCS J034721.73+191828.6 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 51.900 +21 49 21.70 0 13.872 13.425 12.851 12.281 12.493 12.027 24.20 2.51 -40.53 2.51 0.69 1 GCS9 UGCS J035151.89+214921.6 0.002 0.002 0.002 0.001 0.002 0.001 +03 54 00.710 +23 58 59.80 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.02 2.24 0.73 1 GCS9 Cl* Melotte 22 BPL 298 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 31.610 +29 15 13.10 0 14.394 14.080 13.598 12.909 12.733 12.731 19.57 3.07 -37.15 3.07 0.76 1 GCS9 UGCS J035131.60+291513.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 29 19.780 +27 12 30.00 0 14.890 14.572 14.060 13.447 13.169 16.72 6.94 -44.58 6.94 0.93 1 GCS9 UGCS J032919.78+271229.9 0.003 0.003 0.003 0.003 0.002 +04 00 28.350 +27 20 54.50 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.81 1 GCS9 Cl* Melotte 22 DH 893 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 47.710 +28 15 59.30 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.90 1 GCS9 Cl* Melotte 22 DH 289 0.003 0.002 0.002 0.002 0.002 0.001 +03 53 49.640 +27 30 40.10 0 14.731 14.415 13.869 13.241 12.976 12.976 24.57 3.30 -41.14 3.30 0.74 1 GCS9 UGCS J035349.63+273040.0 0.003 0.003 0.003 0.002 0.002 0.003 +03 59 05.730 +26 24 26.60 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.93 1 GCS9 Cl* Melotte 22 DH 871 0.002 0.002 0.002 0.002 0.002 0.001 +03 39 43.060 +28 31 56.40 0 14.162 13.895 13.403 12.759 12.563 12.544 20.48 4.93 -36.37 4.93 0.64 1 GCS9 UGCS J033943.05+283156.4 0.002 0.002 0.003 0.002 0.002 0.001 +03 58 28.350 +26 12 55.90 0 14.361 14.017 13.521 12.968 12.697 12.687 20.39 2.48 -41.41 2.48 0.93 1 GCS9 UGCS J035828.34+261255.8 0.002 0.002 0.002 0.002 0.002 0.001 +03 49 56.770 +25 22 22.60 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 0.93 1 GCS9 Cl* Melotte 22 DH 643 0.003 0.002 0.002 0.002 0.002 0.001 +03 30 22.480 +26 24 32.00 0 14.792 14.449 13.980 13.319 13.102 18.94 6.94 -40.18 6.94 0.92 1 GCS9 UGCS J033022.47+262432.0 0.003 0.003 0.003 0.003 0.002 +03 47 45.920 +24 38 01.30 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 1 GCS9 V* QZ Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 54 03.200 +25 54 51.90 0 14.437 14.189 13.723 13.123 12.856 12.865 12.69 2.94 -39.94 2.94 0.66 1 GCS9 UGCS J035403.20+255451.9 0.003 0.003 0.003 0.002 0.002 0.002 +03 52 06.670 +22 01 14.30 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 0.62 1 GCS9 Cl* Melotte 22 DH 741 0.004 0.003 0.003 0.003 0.003 0.002 +03 45 02.710 +21 18 39.70 0 14.934 14.640 14.136 13.464 13.315 13.317 22.56 2.65 -46.52 2.65 0.82 1 GCS9 UGCS J034502.71+211839.6 0.003 0.003 0.003 0.002 0.003 0.002 +03 51 33.720 +29 00 34.70 0 14.788 14.525 14.036 13.372 13.189 13.178 12.05 3.07 -41.43 3.07 0.65 1 GCS9 UGCS J035133.72+290034.7 0.003 0.003 0.003 0.002 0.002 0.002 +03 42 19.290 +23 31 54.20 0 14.725 14.361 13.832 13.208 12.974 12.935 25.07 2.13 -45.62 2.13 0.64 1 GCS9 UGCS J034219.28+233154.1 0.003 0.003 0.003 0.002 0.003 0.001 +04 02 22.620 +24 48 24.00 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.89 1 GCS9 Cl* Melotte 22 DH 903 0.003 0.003 0.003 0.002 0.002 0.002 +04 02 26.020 +20 01 20.20 0 14.492 14.212 13.752 13.090 12.861 12.872 11.66 3.13 -43.18 3.13 0.62 1 GCS9 UGCS J040226.01+200120.2 0.003 0.003 0.003 0.002 0.002 0.002 +04 00 14.110 +24 48 51.40 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.77 1 GCS9 Cl* Melotte 22 DH 889 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 21.710 +19 08 03.30 0 14.153 13.952 13.488 12.848 12.624 12.659 13.07 3.04 -47.13 3.04 0.63 1 GCS9 UGCS J035721.71+190803.2 0.002 0.002 0.002 0.002 0.002 0.001 +03 42 27.850 +20 36 34.50 0 14.470 14.181 13.664 13.020 12.780 12.791 20.91 3.42 -38.69 3.42 0.85 1 GCS9 UGCS J034227.84+203634.5 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 44.440 +22 55 51.50 0 14.304 13.961 13.438 12.776 12.510 12.514 17.50 2.24 -39.82 2.24 0.91 1 GCS9 UGCS J034444.43+225551.4 0.003 0.002 0.002 0.002 0.002 0.001 +03 38 58.120 +21 39 37.00 0 14.879 14.515 13.971 13.347 13.097 13.072 12.89 3.41 -45.84 3.41 0.70 1 GCS9 UGCS J033858.11+213936.9 0.003 0.003 0.003 0.002 0.002 0.002 +03 39 53.530 +25 46 46.20 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.92 1 GCS9 Cl* Melotte 22 DH 119 0.004 0.003 0.004 0.002 0.003 0.002 +03 37 22.250 +24 54 16.00 0 14.602 14.248 13.722 13.181 12.852 12.843 21.94 2.49 -40.27 2.49 0.88 1 GCS9 UGCS J033722.24+245415.9 0.003 0.003 0.003 0.002 0.002 0.001 +04 04 06.690 +24 06 38.90 0 14.458 14.219 13.754 13.172 13.023 13.019 18.45 3.38 -41.12 3.38 0.93 1 GCS9 UGCS J040406.69+240638.9 0.003 0.002 0.003 0.002 0.002 0.002 +03 47 59.670 +29 33 38.30 0 15.313 14.132 13.593 13.251 13.270 19.41 5.91 -43.49 5.91 0.89 1 GCS9 UGCS J034759.67+293338.3 0.004 0.003 0.002 0.003 0.002 +03 37 39.560 +27 58 59.00 0 15.396 15.042 14.537 13.913 13.660 13.707 17.33 4.96 -37.63 4.96 0.72 1 GCS9 UGCS J033739.56+275858.9 0.004 0.004 0.005 0.004 0.003 0.002 +04 01 54.700 +28 42 17.70 0 15.596 15.171 14.595 13.980 13.674 13.692 12.35 2.97 -40.08 2.97 0.60 1 GCS9 UGCS J040154.70+284217.7 0.005 0.004 0.004 0.003 0.004 0.003 +03 57 41.460 +28 16 35.80 0 15.528 15.146 14.578 13.949 13.678 13.674 15.59 3.87 -48.50 3.87 0.62 1 GCS9 UGCS J035741.46+281635.8 0.004 0.004 0.004 0.003 0.003 0.002 +03 51 29.350 +28 30 49.30 0 15.859 15.469 14.923 14.340 14.053 14.035 20.59 2.97 -37.30 2.97 0.63 1 GCS9 UGCS J035129.34+283049.2 0.005 0.004 0.005 0.004 0.005 0.003 +03 50 32.900 +26 42 57.20 0 15.534 15.123 14.574 13.937 13.670 13.669 21.11 2.97 -41.24 2.97 0.84 1 GCS9 UGCS J035032.90+264257.2 0.005 0.005 0.005 0.003 0.004 0.003 +04 05 57.780 +26 14 28.00 0 15.023 14.686 14.199 13.587 13.362 13.356 14.79 3.39 -37.90 3.39 0.65 1 GCS9 UGCS J040557.77+261428.0 0.004 0.003 0.003 0.003 0.003 0.002 +03 49 12.180 +25 20 31.70 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 0.89 1 GCS9 UGCS J034912.17+252031.7 0.006 0.005 0.005 0.004 0.004 0.003 +03 47 10.210 +24 43 35.30 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 0.90 1 GCS9 UGCS J034710.20+244335.3 0.005 0.004 0.004 0.003 0.004 0.002 +03 42 52.360 +26 29 09.40 0 15.538 15.176 14.662 14.108 13.827 13.821 18.20 2.95 -44.40 2.95 0.89 1 GCS9 UGCS J034252.35+262909.3 0.006 0.005 0.005 0.004 0.004 0.003 +03 45 36.120 +25 16 30.30 0 15.683 15.221 14.656 14.111 13.784 13.795 15.41 2.22 -40.16 2.22 0.83 1 GCS9 UGCS J034536.12+251630.2 0.005 0.004 0.004 0.003 0.004 0.003 +03 46 13.000 +27 02 11.70 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 0.82 1 GCS9 Cl* Melotte 22 DH 430 0.004 0.004 0.004 0.003 0.003 0.002 +04 01 12.640 +23 50 20.20 0 15.772 15.257 14.698 14.117 13.795 17.28 3.56 -46.77 3.56 0.81 1 GCS9 UGCS J040112.64+235020.2 0.005 0.004 0.004 0.005 0.003 +03 38 10.270 +25 11 32.90 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 0.88 1 GCS9 Cl* Melotte 22 HHJ 35 0.005 0.004 0.004 0.004 0.004 0.003 +03 56 11.670 +22 05 52.60 0 15.610 15.181 14.631 14.105 13.807 13.799 13.87 2.52 -42.35 2.52 0.81 1 GCS9 UGCS J035611.66+220552.5 0.005 0.005 0.005 0.003 0.004 0.003 +04 10 08.800 +25 45 52.30 0 15.752 14.719 14.169 13.826 13.848 15.83 3.05 -42.20 3.05 0.88 1 GCS9 UGCS J041008.80+254552.3 0.006 0.005 0.003 0.004 0.003 +04 04 11.640 +22 15 20.70 0 15.315 14.979 14.419 13.706 13.470 13.485 22.99 3.41 -38.88 3.41 0.60 1 GCS9 UGCS J040411.64+221520.6 0.004 0.003 0.004 0.003 0.003 0.002 +03 33 39.010 +22 21 08.50 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.87 1 GCS9 Cl* Melotte 22 DH 29 0.004 0.003 0.003 0.003 0.003 0.002 +03 57 55.850 +21 16 10.80 0 15.354 14.971 14.436 13.805 13.526 13.536 16.86 3.37 -36.80 3.37 0.62 1 GCS9 Cl* Melotte 22 DH 860 0.004 0.004 0.004 0.003 0.003 0.003 +03 56 55.470 +22 08 24.30 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.80 1 GCS9 Cl* Melotte 22 DH 847 0.004 0.003 0.003 0.003 0.002 0.002 +03 45 15.270 +28 49 08.30 0 15.861 15.413 14.849 14.234 13.940 13.925 16.55 5.87 -46.40 5.87 0.82 1 GCS9 UGCS J034515.27+284908.2 0.005 0.005 0.005 0.004 0.004 0.003 +03 54 16.800 +22 00 16.60 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 0.67 1 GCS9 Cl* Melotte 22 DH 799 0.007 0.005 0.005 0.004 0.005 0.003 +03 38 06.920 +24 14 55.50 0 15.747 15.267 14.652 14.133 13.761 13.766 22.57 2.51 -42.72 2.51 0.78 1 GCS9 UGCS J033806.92+241455.5 0.005 0.005 0.004 0.004 0.004 0.003 +03 44 47.310 +19 55 41.40 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.85 1 GCS9 Cl* Melotte 22 DH 354 0.005 0.004 0.004 0.004 0.005 0.003 +03 39 05.600 +24 12 41.20 0 15.799 15.460 14.905 14.352 14.029 14.047 14.71 2.51 -42.46 2.51 0.85 1 GCS9 UGCS J033905.60+241241.2 0.006 0.005 0.005 0.004 0.005 0.003 +03 58 23.360 +24 41 57.20 0 15.291 14.922 14.431 13.755 13.554 13.562 17.18 2.49 -38.00 2.49 0.76 1 GCS9 UGCS J035823.36+244157.2 0.005 0.004 0.004 0.003 0.003 0.002 +03 57 06.210 +23 13 00.70 0 15.484 14.319 13.773 13.434 13.441 15.53 3.07 -41.00 3.07 0.86 1 GCS9 UGCS J035706.21+231300.7 0.006 0.004 0.003 0.003 0.002 +04 08 59.010 +24 26 26.40 0 15.392 15.053 14.539 13.891 13.594 13.605 17.10 2.99 -46.14 2.99 0.84 1 GCS9 UGCS J040859.01+242626.3 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 48.620 +24 30 15.40 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 0.70 1 GCS9 UGCS J034848.61+243015.3 0.005 0.004 0.004 0.003 0.004 0.002 +03 34 41.090 +22 42 27.00 0 15.091 14.772 14.252 13.508 13.294 13.320 12.65 3.05 -42.76 3.05 0.72 1 GCS9 UGCS J033441.08+224226.9 0.004 0.003 0.003 0.002 0.003 0.002 +03 44 05.630 +23 03 42.40 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 0.90 1 GCS9 UGCS J034405.62+230342.4 0.004 0.004 0.004 0.003 0.003 0.002 +03 29 48.420 +22 57 51.60 0 15.744 15.377 14.828 14.156 13.922 13.928 17.79 3.43 -41.86 3.43 0.90 1 GCS9 UGCS J032948.41+225751.6 0.005 0.004 0.005 0.004 0.004 0.003 +03 44 24.000 +21 24 20.80 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 0.89 1 GCS9 Cl* Melotte 22 DH 332 0.004 0.003 0.003 0.003 0.004 0.002 +03 38 25.720 +21 39 49.20 0 15.288 14.944 14.425 13.874 13.590 13.648 16.06 3.42 -43.01 3.42 0.89 1 GCS9 UGCS J033825.71+213949.1 0.004 0.003 0.004 0.003 0.004 0.002 +03 54 06.960 +19 19 14.30 0 15.335 14.930 14.318 13.814 13.497 13.503 18.13 6.06 -42.85 6.06 0.90 1 GCS9 UGCS J035406.96+191914.2 0.004 0.003 0.003 0.003 0.003 0.002 +04 03 16.560 +24 35 19.60 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.83 1 GCS9 Cl* Melotte 22 DH 906 0.004 0.003 0.003 0.003 0.003 0.002 +04 10 46.420 +24 32 13.90 0 15.786 15.329 14.767 14.099 13.762 13.766 15.88 2.99 -46.34 2.99 0.81 1 GCS9 UGCS J041046.41+243213.8 0.006 0.005 0.005 0.003 0.004 0.003 +03 45 36.750 +25 58 55.50 0 15.934 15.514 14.978 14.442 14.152 14.150 14.21 2.25 -38.02 2.25 0.62 1 GCS9 UGCS J034536.74+255855.5 0.006 0.006 0.006 0.005 0.006 0.004 +03 57 23.790 +24 08 50.00 0 15.309 14.982 14.445 13.865 13.611 13.611 13.28 2.97 -45.28 2.97 0.72 1 GCS9 UGCS J035723.79+240850.0 0.004 0.004 0.004 0.003 0.004 0.002 +03 27 27.940 +24 04 58.90 0 15.259 14.831 14.285 13.791 13.497 13.490 19.47 3.85 -48.39 3.85 0.66 1 GCS9 UGCS J032727.94+240458.8 0.004 0.003 0.003 0.003 0.003 0.002 +03 56 28.160 +28 03 48.20 0 16.015 15.511 14.948 14.304 14.050 14.022 16.65 3.90 -45.48 3.90 0.68 1 GCS9 UGCS J035628.16+280348.2 0.005 0.005 0.005 0.004 0.005 0.003 +03 39 03.310 +25 48 18.80 0 16.452 16.052 15.461 14.883 14.588 14.571 16.29 3.03 -46.33 3.03 0.62 1 GCS9 UGCS J033903.30+254818.8 0.010 0.008 0.008 0.006 0.007 0.005 +04 00 38.150 +19 49 22.40 0 16.562 16.095 15.532 15.014 14.678 14.708 14.99 3.08 -42.23 3.08 0.68 1 GCS9 UGCS J040038.14+194922.3 0.009 0.007 0.007 0.006 0.008 0.005 +03 45 03.180 +23 06 58.40 0 16.937 15.492 14.946 14.495 14.527 20.81 2.32 -40.36 2.32 0.62 1 GCS9 2MASS J03450316+2306586 0.012 0.007 0.006 0.008 0.005 +03 52 34.560 +24 25 22.20 0 16.121 15.669 15.096 14.512 14.246 14.236 16.91 2.25 -44.16 2.25 0.73 1 GCS9 UGCS J035234.55+242522.2 0.007 0.006 0.006 0.005 0.006 0.004 +03 30 35.390 +23 03 07.90 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.63 1 GCS9 Cl* Melotte 22 DH 12 0.007 0.006 0.006 0.005 0.005 0.004 +03 40 28.370 +21 33 06.70 0 16.564 16.082 15.471 14.901 14.509 14.509 15.69 3.66 -45.19 3.66 0.66 1 GCS9 UGCS J034028.36+213306.7 0.008 0.007 0.007 0.009 0.006 0.005 +03 42 30.350 +25 34 26.80 0 17.540 16.951 16.269 15.675 15.305 15.293 15.12 2.38 -41.28 2.38 0.62 1 GCS9 UGCS J034230.35+253426.7 0.018 0.015 0.015 0.012 0.015 0.011 +03 51 17.870 +25 29 25.40 0 17.777 18.391 15.245 14.674 14.519 14.519 20.51 2.28 -39.66 2.28 0.65 1 GCS9 UGCS J035117.86+252925.4 0.022 0.048 0.006 0.006 0.008 0.005 +03 53 00.280 +25 40 08.50 0 17.276 16.722 16.169 15.583 15.273 15.241 18.78 3.13 -41.94 3.13 0.73 1 GCS9 UGCS J035300.28+254008.4 0.013 0.011 0.011 0.012 0.013 0.010 +04 06 09.290 +26 15 33.50 0 17.463 16.942 16.310 15.716 15.358 15.337 18.62 3.15 -39.15 3.15 0.64 1 GCS9 UGCS J040609.29+261533.5 0.016 0.014 0.013 0.010 0.013 0.010 +04 03 43.310 +23 56 52.90 0 17.197 16.623 15.998 15.428 15.061 15.045 15.09 3.44 -41.73 3.44 0.63 1 GCS9 UGCS J040343.31+235652.9 0.012 0.009 0.009 0.009 0.010 0.007 +03 36 57.480 +24 19 47.70 0 17.070 16.465 15.833 15.324 14.922 14.937 21.72 2.60 -40.73 2.60 0.64 1 GCS9 UGCS J033657.47+241947.7 0.012 0.010 0.009 0.010 0.012 0.008 +03 44 02.270 +28 51 32.30 0 17.372 16.793 16.123 15.540 15.176 15.169 19.81 6.04 -41.64 6.04 0.72 1 GCS9 UGCS J034402.26+285132.2 0.013 0.011 0.010 0.008 0.011 0.008 +04 03 04.580 +23 33 10.70 0 17.246 16.654 16.054 15.455 15.146 19.09 3.78 -42.76 3.78 0.73 1 GCS9 UGCS J040304.58+233310.6 0.013 0.010 0.010 0.014 0.009 +03 46 23.720 +22 50 16.40 0 17.310 15.710 15.189 14.722 14.694 20.26 2.35 -45.42 2.35 0.64 1 GCS9 2MASS J03462371+2250167 0.015 0.008 0.008 0.008 0.006 +04 11 03.840 +23 15 48.90 0 17.766 17.254 16.599 15.986 15.599 15.623 18.36 6.81 -46.34 6.81 0.61 1 GCS9 UGCS J041103.83+231548.9 0.017 0.014 0.014 0.017 0.021 0.012 +03 50 07.500 +19 37 06.20 0 17.466 16.910 16.332 15.761 15.401 15.371 17.29 4.51 -41.20 4.51 0.70 1 GCS9 UGCS J035007.50+193706.2 0.013 0.011 0.013 0.014 0.016 0.010 +03 37 45.480 +21 49 49.20 0 17.574 16.949 16.292 15.711 15.375 15.341 15.76 2.70 -40.52 2.70 0.63 1 GCS9 UGCS J033745.47+214949.2 0.019 0.014 0.014 0.011 0.016 0.010 +03 49 01.600 +24 54 30.20 0 17.757 17.163 16.566 16.016 15.638 15.597 20.82 2.38 -42.11 2.38 0.70 1 GCS9 UGCS J034901.60+245430.2 0.020 0.015 0.014 0.014 0.021 0.013 +03 43 53.770 +21 58 28.20 0 17.955 17.227 16.574 15.985 15.582 15.564 17.05 2.89 -42.45 2.89 0.71 1 GCS9 UGCS J034353.76+215828.1 0.024 0.018 0.015 0.017 0.019 0.017 +03 43 53.960 +21 58 24.40 0 17.255 16.562 15.936 15.332 14.986 14.994 20.06 2.65 -43.75 2.65 0.70 1 GCS9 UGCS J034353.95+215824.3 0.014 0.011 0.010 0.010 0.012 0.010 +03 59 40.860 +27 04 35.50 0 19.361 18.308 17.418 16.792 16.276 16.196 23.02 3.86 -48.45 3.86 0.63 1 GCS9 UGCS J035940.86+270435.4 0.083 0.044 0.038 0.029 0.032 0.026 +04 00 48.820 +28 32 53.60 0 19.819 18.841 18.112 17.507 16.848 16.975 23.80 5.55 -42.37 5.55 0.64 1 GCS9 UGCS J040048.81+283253.6 0.096 0.064 0.054 0.049 0.058 0.048 +04 10 54.540 +26 01 42.40 0 19.642 17.748 17.002 16.417 17.38 5.99 -47.11 5.99 0.66 1 GCS9 UGCS J041054.54+260142.3 0.091 0.036 0.027 0.022 +03 46 17.010 +27 55 27.80 0 19.334 18.660 17.847 17.282 17.030 17.052 16.50 7.11 -47.80 7.11 0.63 1 GCS9 UGCS J034617.01+275527.7 0.060 0.046 0.038 0.074 0.082 0.054 +03 49 04.260 +21 30 48.40 0 19.535 18.513 17.649 16.945 16.333 16.414 16.76 4.01 -42.72 4.01 0.65 1 GCS9 UGCS J034904.25+213048.3 0.082 0.042 0.035 0.027 0.032 0.026 +03 40 44.530 +28 27 22.00 0 20.858 19.520 18.607 17.739 17.353 17.491 13.92 12.21 -49.16 12.21 0.62 1 GCS9 UGCS J034044.53+282721.9 0.209 0.090 0.071 0.092 0.111 0.074 +04 03 33.870 +23 19 28.20 0 20.446 19.748 18.418 18.040 17.402 17.405 12.29 11.98 -53.62 11.98 0.60 1 GCS9 UGCS J040333.86+231928.2 0.145 0.111 0.065 0.114 0.117 0.063 +03 43 03.110 +19 55 13.30 0 20.210 19.510 18.492 18.006 17.591 17.575 17.22 11.92 -47.33 11.92 0.60 1 GCS9 UGCS J034303.11+195513.2 0.123 0.101 0.067 0.090 0.119 0.087 +03 25 30.920 +24 51 39.90 0 20.316 19.478 18.600 18.210 18.170 10.72 31.33 -45.76 31.33 0.60 1 GCS9 UGCS J032530.91+245139.8 0.126 0.096 0.079 0.160 0.120 +04 08 03.700 +25 03 18.60 0 20.585 19.668 18.717 17.693 17.074 7.72 8.93 -49.54 8.93 0.60 1 GCS9 UGCS J040803.69+250318.5 0.223 0.132 0.089 0.057 0.042 +03 41 51.050 +24 10 06.60 0 20.465 19.418 18.729 18.205 17.578 12.60 6.35 -49.41 6.35 0.62 1 GCS9 UGCS J034151.04+241006.5 0.190 0.108 0.093 0.133 0.082 +03 32 30.600 +27 09 39.20 0 20.343 18.971 18.172 16.968 17.036 7.52 10.85 -21.23 10.85 3 GCS9 UGCS J033230.59+270939.2 0.163 0.090 0.168 0.053 0.044 +03 29 01.870 +27 16 23.30 0 20.590 19.126 18.101 17.366 96.07 32.77 -88.72 32.77 3 GCS9 UGCS J032901.87+271623.2 0.208 0.117 0.144 0.049 +03 42 59.310 +25 37 38.90 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 3 GCS9 UGCS J034259.30+253738.9 0.160 0.074 0.054 0.050 0.033 +03 35 28.030 +25 17 23.50 0 19.445 18.046 17.289 16.458 41.24 10.18 -21.88 10.18 3 GCS9 UGCS J033528.02+251723.4 0.094 0.048 0.072 0.025 +03 40 03.140 +25 40 05.50 0 19.571 18.217 17.364 16.488 16.514 13.38 5.06 -31.42 5.06 3 GCS9 UGCS J034003.14+254005.5 0.136 0.072 0.043 0.042 0.031 +03 33 24.060 +25 50 16.00 0 20.543 18.806 17.811 16.808 11.92 13.52 -32.48 13.52 3 GCS9 UGCS J033324.06+255015.9 0.245 0.094 0.114 0.036 +03 35 13.620 +25 07 52.90 0 19.550 18.161 17.249 16.645 16.523 24.66 4.39 -42.83 4.39 3 GCS9 UGCS J033513.62+250752.9 0.092 0.045 0.049 0.057 0.027 +03 48 15.650 +25 50 08.90 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 3 GCS9 UGCS J034815.64+255008.9 0.161 0.074 0.071 0.053 0.039 +03 47 38.470 +23 56 27.70 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 3 GCS9 UGCS J034738.47+235627.6 0.152 0.054 0.045 0.049 0.031 +03 28 30.710 +23 54 01.20 0 19.736 18.362 17.562 16.807 16.867 9.65 6.31 -32.88 6.31 3 GCS9 UGCS J032830.71+235401.2 0.108 0.055 0.054 0.051 0.038 +03 50 15.960 +24 23 28.60 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 3 GCS9 UGCS J035015.96+242328.6 0.153 0.068 0.053 0.056 0.036 +03 45 33.300 +23 34 34.30 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 3 GCS9 UGCS J034533.30+233434.2 0.120 0.044 0.034 0.039 0.026 +03 52 27.190 +23 12 08.00 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 3 GCS9 UGCS J035227.18+231208.0 0.106 0.057 0.044 0.041 0.031 +03 45 14.520 +22 29 28.60 0 19.756 18.383 17.410 16.493 16.629 14.00 4.20 -53.10 4.20 3 GCS9 Cl* Melotte 22 IPL 69 0.132 0.063 0.064 0.043 0.030 +03 52 39.150 +24 46 29.40 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 3 GCS9 UGCS J035239.15+244629.4 0.100 0.055 0.042 0.042 0.025 +03 46 29.110 +22 59 47.60 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 3 GCS9 UGCS J034629.11+225947.6 0.074 0.040 0.027 0.025 0.017 +03 50 08.100 +23 00 16.60 0 20.272 18.537 17.648 16.732 16.829 12.67 6.26 -29.23 6.26 3 GCS9 UGCS J035008.10+230016.5 0.214 0.075 0.073 0.043 0.040 +03 53 18.940 +19 24 24.20 0 20.069 18.680 17.810 16.861 16.872 27.44 8.91 -38.90 8.91 3 GCS9 UGCS J035318.93+192424.1 0.147 0.084 0.085 0.069 0.036 +03 50 39.550 +25 02 54.60 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 3 GCS9 UGCS J035039.54+250254.6 0.112 0.044 0.038 0.043 0.027 +03 51 29.470 +24 00 37.40 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3 GCS9 Cl* Melotte 22 PLIZ 161 0.107 0.052 0.041 0.047 0.031 +03 45 58.480 +23 41 53.90 0 18.591 17.562 16.741 16.714 19.16 3.22 -40.46 3.22 3 GCS9 Cl* Melotte 22 NPNPL 4 0.065 0.047 0.048 0.032 \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit b/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit new file mode 100644 index 00000000..95854e38 --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit @@ -0,0 +1,174 @@ +SIMPLE = T / Standard FITS Format BITPIX = 8 / Character data NAXIS = 0 / No Image --- just extension(s) EXTEND = T / There are standard extensions ORIGIN = 'xml2fits_v1.95' / Converted from XML-Astrores to FITS e-mail: question@simbad.u-strasbg.fr LONGSTRN= 'OGIP 1.0' / Long string convention (&/CONTINUE) may be usedDATE = '2026-04-09' / Written on 2026-04-09:20:08:38 (GMT) by: www-data@vizier.cds.unistra.fr ********************************************************** EXCERPT from catalogues stored in VizieR (CDS) with the following conditions: ********************************************************** VizieR Astronomical Server vizier.cds.unistra.fr Date: 2026-04-09T20:08:38 [V7.5.6] In case of problem, please report to: cds-question@unistra.fr INFO = 'service_protocol=ASU' / IVOID of the protocol through which the data was retrieved # INFO = 'request_date=2026-04-09T20:08:38' / Query execution date # INFO = 'request=https://vizier.cds.unistra.fr/viz-bin/asu-fits?-oc.form=dec&'CONTINUE '&-out.max=unlimited&#out.form=FITS (ascii) Table&-order=I&-out.src=&'CONTINUE 'J/MNRAS/374/372/tables&-c.eq=J2000&-c.r= 2&-c.u=arcmin&-c.geom=r&-&'CONTINUE 'source=J/MNRAS/374/372/tables&-out=M&-out=RAJ2000&-out=DEJ2000&-out&'CONTINUE '=Zmag&-out=e_Zmag&-out=Ymag&-out=e_Ymag&-out=Jmag&-out=e_Jmag&-out=&'CONTINUE 'Hmag&-out=e_Hmag&-out=Kmag&-out=e_Kmag&-out=pmRA&-out=pmDE&' / Full request URL (POST) # INFO = 'contact=cds-question@unistra.fr' / Email or URL to contact publisher # INFO = 'server_software=VizieR/7.5.6' / Software version # INFO = 'publisher=CDS' / Data centre that produced the VOTable # INFO = 'ivoid=ivo://cds.vizier/j/mnras/374/372' / IVOID of underlying data collection # INFO = 'data_ivoid=ivo://cds.vizier/j/mnras/374/372' / IVOID of underlying data collection # INFO = 'creator=Lodieu N.' / First author or institution # INFO = 'cites=bibcode:2007MNRAS.374..372L' / Article or Data origin sources # INFO = 'journal=MNRA' / Journal name # INFO = 'original_date=2007' / Year of the article publication # INFO = 'reference_url=https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/374&'CONTINUE '/372 ' / Dataset landing page # INFO = 'citation=doi:10.26093/cds/vizier.73740372' / Dataset identifier that can be used for citation # INFO = 'publication_date=2024-07-04' / Date of first publication in the data centre # INFO = 'rights_uri=https://cds.unistra.fr/vizier-org/licences_vizier.html' / Licence URI # END XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 112 / Number of bytes per record NAXIS2 = 173 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 15 / Number of data fields (columns) CDS-CAT = 'J/MNRAS/374/372' / Catalogue designation in CDS nomenclature ZYJHK photometry in Upper Sco (Lodieu+, 2007) EXTNAME = 'J_MNRAS_374_372_tables' / Identification of the table CDS-NAME= 'J/MNRAS/374/372/tables' / Table name in METAtab Near-infrared (ZY JHK) photometry for member candidates and non-members in Upper Sco TBCOL1 = 2 / UCD=meta.code.member char:2 .. offset=1 TFORM1 = 'A2 ' / Fortran Format TTYPE1 = 'M ' / Simbad column added by the CDS TBCOL2 = 5 / UCD=pos.eq.ra;meta.main char:11 offset=4 TFORM2 = 'A11 ' / Fortran Format TTYPE2 = 'RAJ2000 ' / Right ascension (J2000) TBCOL3 = 17 / UCD=pos.eq.dec;meta.main char:11 offset=16 TFORM3 = 'A11 ' / Fortran Format TTYPE3 = 'DEJ2000 ' / Declination (J2000) TBCOL4 = 29 / UCD=phot.mag;em.opt.I float: . offset=28 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? Z (0.88um) magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 36 / UCD=stat.error;phot.mag;em.opt.I f offset=35 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'e_Zmag ' / ? rms uncertainty on Zmag TUNIT5 = 'mag ' / magnitude TBCOL6 = 43 / UCD=phot.mag;em.opt.I float: . offset=42 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Ymag ' / Y (1.03um) magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 50 / UCD=stat.error;phot.mag;em.opt.B f offset=49 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'e_Ymag ' / rms uncertainty on Ymag TUNIT7 = 'mag ' / magnitude TBCOL8 = 57 / UCD=phot.mag;em.IR.J float: .. offset=56 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'Jmag ' / J (1.25um) magnitude TUNIT8 = 'mag ' / magnitude TBCOL9 = 64 / UCD=stat.error;phot.mag;em.IR.J fl offset=63 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'e_Jmag ' / rms uncertainty on Jmag TUNIT9 = 'mag ' / magnitude TBCOL10 = 71 / UCD=phot.mag;em.IR.H float: .. offset=70 TFORM10 = 'F6.3 ' / Fortran Format TTYPE10 = 'Hmag ' / H (1.65um) magnitude TUNIT10 = 'mag ' / magnitude TBCOL11 = 78 / UCD=stat.error;phot.mag;em.IR.H fl offset=77 TFORM11 = 'F6.3 ' / Fortran Format TTYPE11 = 'e_Hmag ' / rms uncertainty on Hmag TUNIT11 = 'mag ' / magnitude TBCOL12 = 85 / UCD=phot.mag;em.IR.K float: .. offset=84 TFORM12 = 'F6.3 ' / Fortran Format TTYPE12 = 'Kmag ' / K (2.20um) magnitude TUNIT12 = 'mag ' / magnitude TBCOL13 = 92 / UCD=stat.error;phot.mag;em.IR.K fl offset=91 TFORM13 = 'F6.3 ' / Fortran Format TTYPE13 = 'e_Kmag ' / rms uncertainty on Kmag TUNIT13 = 'mag ' / magnitude TBCOL14 = 99 / UCD=pos.pm;pos.eq.ra float: .. offset=98 TFORM14 = 'F6.1 ' / Fortran Format TTYPE14 = 'pmRA ' / ? Proper motion along RA, muRA*cosDE TUNIT14 = 'mas/yr ' / milli-second of arc per year TBCOL15 = 106 / UCD=pos.pm;pos.eq.dec float: . offset=105 TFORM15 = 'F6.1 ' / Fortran Format TTYPE15 = 'pmDE ' / ? Proper motion along DE, muDE TUNIT15 = 'mas/yr ' / milli-second of arc per year END +m 16 06 03.75 -22 19 30.0 18.169 0.036 16.825 0.014 15.853 0.009 15.096 0.009 14.438 0.009 13.7 -26.5 +m 16 06 06.29 -23 35 13.3 18.430 0.041 17.150 0.018 16.204 0.012 15.540 0.012 14.973 0.012 +m 16 06 15.95 -22 18 28.0 14.307 0.003 13.705 0.002 13.166 0.002 12.570 0.001 12.248 0.002 -5.0 -15.2 +m 16 06 26.37 -23 06 11.4 14.477 0.003 13.770 0.002 13.205 0.002 12.616 0.001 12.271 0.002 0.7 -22.0 +m 16 06 34.61 -22 55 04.4 13.049 0.002 12.366 0.001 11.835 0.001 11.243 0.001 10.873 0.001 -13.4 -8.8 +m 16 06 38.24 -23 43 03.8 11.867 0.001 11.413 0.001 10.915 0.001 10.438 0.000 10.041 0.000 -13.5 -25.6 +m 16 06 39.22 -22 48 34.2 13.520 0.002 12.874 0.001 12.322 0.001 11.742 0.001 11.422 0.001 -9.5 -19.6 +m 16 06 48.18 -22 30 40.1 16.818 0.013 15.697 0.007 14.926 0.005 14.353 0.005 13.839 0.006 -22.7 -14.8 +m 16 06 49.10 -22 16 38.4 15.094 0.004 14.339 0.003 13.735 0.002 13.199 0.002 12.881 0.003 -6.8 -26.5 +m 16 06 50.18 -23 09 54.0 12.971 0.001 12.388 0.001 11.816 0.001 11.241 0.001 10.902 0.001 -22.9 -10.9 +m 16 07 06.33 -22 48 28.2 12.644 0.001 12.129 0.001 11.567 0.001 10.967 0.001 10.686 0.001 -11.4 -21.6 +m 16 07 08.81 -23 39 59.9 12.653 0.001 12.127 0.001 11.624 0.001 11.044 0.000 10.714 0.001 -4.7 -14.5 +m 16 07 14.79 -23 21 01.2 19.085 0.076 17.649 0.029 16.556 0.017 15.829 0.017 15.072 0.015 +m 16 07 21.96 -23 58 45.3 14.241 0.003 13.543 0.002 12.985 0.001 12.422 0.001 12.077 0.001 -8.3 -18.7 +m 16 07 23.82 -22 11 02.0 17.239 0.016 16.015 0.009 15.202 0.006 14.564 0.004 14.009 0.005 -11.0 -30.7 +m 16 07 26.41 -21 44 17.1 16.171 0.008 15.289 0.006 14.601 0.004 14.059 0.003 13.615 0.004 -13.2 -31.4 +m 16 07 27.82 -22 39 04.0 19.358 0.100 18.236 0.048 16.810 0.022 16.091 0.023 15.388 0.023 +m 16 07 37.99 -22 42 47.0 19.240 0.090 17.713 0.029 16.757 0.019 16.001 0.020 15.327 0.020 +m 16 07 45.21 -22 22 57.6 13.589 0.002 12.935 0.001 12.348 0.001 11.811 0.001 11.485 0.001 -17.4 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08.90 -23 08 28.3 11.664 0.001 11.361 0.001 10.783 0.000 10.499 0.000 10.051 0.000 -16.6 -32.0 +n1 16 11 24.88 -22 52 53.3 17.715 0.023 16.735 0.016 16.007 0.011 15.315 0.008 14.875 0.011 +n1 16 12 05.64 -22 24 12.4 19.915 0.157 18.568 0.063 17.546 0.038 16.990 0.033 16.500 0.045 +n1 16 12 34.14 -21 44 50.2 13.461 0.002 12.831 0.002 12.387 0.001 11.704 0.001 11.205 0.001 -6.0 -26.7 +n1 16 13 01.40 -21 42 54.5 18.135 0.035 17.201 0.020 16.325 0.011 15.613 0.010 15.065 0.013 +n1 16 13 39.04 -22 33 14.4 19.558 0.109 18.399 0.061 17.418 0.042 16.836 0.033 16.227 0.039 +n1 16 14 50.26 -23 32 39.9 12.758 0.001 12.218 0.001 11.605 0.001 10.803 0.000 10.067 0.000 -18.2 -16.4 +n1 16 15 03.04 -23 52 50.3 17.473 0.019 16.650 0.013 15.932 0.009 15.286 0.010 14.842 0.012 +n1 16 15 27.63 -23 52 34.2 14.833 0.004 13.989 0.003 13.271 0.002 12.442 0.001 11.808 0.001 0.1 -27.6 +n1 16 16 22.51 -23 38 42.6 20.331 0.220 18.841 0.080 17.991 0.058 17.247 0.060 16.989 0.083 +n2 16 06 57.53 -23 03 27.1 11.759 0.001 11.404 0.001 10.901 0.000 10.341 0.000 9.970 0.000 -11.8 9.7 +n2 16 07 03.05 -23 31 46.2 12.024 0.001 11.539 0.001 10.982 0.001 10.393 0.000 9.792 0.000 10.2 -24.9 +n2 16 07 27.57 -21 54 42.7 17.010 0.015 16.198 0.010 15.588 0.007 14.990 0.006 14.564 0.009 -83.1 -51.3 +n2 16 07 48.27 -21 39 03.9 13.962 0.002 13.300 0.002 12.686 0.001 12.118 0.001 11.778 0.001 -2.8 -4.5 +n2 16 08 27.33 -22 17 29.3 12.756 0.001 12.183 0.001 11.630 0.001 10.991 0.000 10.586 0.001 0.4 -8.1 +n2 16 08 28.69 -21 37 20.0 12.758 0.001 12.154 0.001 11.598 0.001 11.073 0.000 10.685 0.001 -31.4 -20.5 +n2 16 08 34.55 -22 11 55.9 13.772 0.002 13.067 0.002 12.500 0.001 11.943 0.001 11.556 0.001 -7.1 -5.2 +n2 16 09 05.67 -22 45 16.7 17.341 0.019 16.049 0.008 15.212 0.006 14.558 0.006 13.987 0.007 -25.7 -9.9 +n2 16 09 07.75 -23 39 54.5 13.438 0.002 12.629 0.001 12.032 0.001 11.476 0.001 11.107 0.001 -17.1 -3.7 +n2 16 09 08.83 -22 17 46.9 14.124 0.003 13.466 0.002 12.903 0.001 12.403 0.001 12.046 0.001 -36.5 -29.6 +n2 16 09 32.29 -22 29 36.0 12.421 0.001 11.930 0.001 11.383 0.001 10.764 0.000 10.445 0.001 -6.1 8.2 +n2 16 09 37.85 -21 23 19.0 12.638 0.001 12.170 0.001 11.700 0.001 11.187 0.001 10.877 0.001 -3.6 -241.2 +n2 16 10 00.18 -23 12 19.3 15.947 0.007 15.204 0.005 14.530 0.004 13.977 0.004 13.545 0.004 -33.5 -14.2 +n2 16 10 02.67 -23 44 39.5 12.413 0.001 11.758 0.001 11.204 0.001 10.682 0.000 10.265 0.001 -177.8 89.7 +n2 16 10 02.86 -23 44 40.9 11.900 0.001 11.395 0.001 10.862 0.000 10.427 0.000 9.949 0.000 252.5 -145.7 +n2 16 10 19.43 -23 31 08.9 14.842 0.004 14.123 0.003 13.532 0.002 12.967 0.002 12.571 0.002 -5.1 -2.5 +n2 16 10 51.82 -23 33 26.3 16.359 0.009 15.581 0.006 14.969 0.005 14.343 0.005 13.985 0.006 -67.9 -110.7 +n2 16 14 01.73 -22 58 48.7 13.654 0.002 12.981 0.002 12.422 0.001 11.882 0.001 11.515 0.001 312.7 -410.9 +n2 16 14 12.36 -22 19 13.2 12.469 0.001 11.982 0.001 11.446 0.001 11.057 0.000 10.568 0.001 8.8 0.2 +n2 16 14 25.25 -23 50 15.1 13.520 0.002 12.875 0.001 12.341 0.001 11.804 0.001 11.422 0.001 -14.2 -5.0 +n2 16 14 46.84 -23 41 57.2 16.314 0.009 15.494 0.006 14.835 0.004 14.251 0.004 13.815 0.005 -34.6 -14.9 +n2 16 15 16.02 -23 45 10.4 13.587 0.002 12.844 0.001 12.230 0.001 11.656 0.001 11.272 0.001 -11.0 3.9 +n2 16 15 16.66 -23 40 46.3 17.968 0.030 16.552 0.013 15.621 0.008 14.911 0.007 14.269 0.008 -35.1 -11.1 +n2 16 15 38.43 -23 41 56.0 13.399 0.002 12.760 0.001 12.208 0.001 11.584 0.001 11.267 0.001 -32.1 -16.2 +n2 16 16 20.11 -23 44 14.3 12.083 0.001 11.504 0.001 10.976 0.001 10.533 0.000 10.144 0.000 -33.2 -11.3 +n2 16 16 26.20 -23 50 48.8 12.051 0.001 11.601 0.001 11.018 0.001 10.474 0.000 9.968 0.000 -10.0 1.1 \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/asu.fit b/docs/paper_examples/Begbie+26/cluster_data/asu.fit new file mode 100644 index 00000000..ec95b30a --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/asu.fit @@ -0,0 +1,2694 @@ +SIMPLE = T / Standard FITS Format BITPIX = 8 / Character data NAXIS = 0 / No Image --- just extension(s) EXTEND = T / There are standard extensions ORIGIN = 'xml2fits_v1.95' / Converted from XML-Astrores to FITS e-mail: question@simbad.u-strasbg.fr LONGSTRN= 'OGIP 1.0' / Long string convention (&/CONTINUE) may be usedDATE = '2026-01-16' / Written on 2026-01-16:19:27:04 (GMT) by: www-data@vizier.cds.unistra.fr ********************************************************** EXCERPT from catalogues stored in VizieR (CDS) with the following conditions: ********************************************************** VizieR Astronomical Server vizier.cds.unistra.fr Date: 2026-01-16T19:27:04 [V7.5.3] In case of problem, please report to: cds-question@unistra.fr INFO = 'service_protocol=ASU' / IVOID of the protocol through which the data was retrieved # INFO = 'request_date=2026-01-16T19:27:04' / Query execution date # INFO = 'request=https://vizier.cds.unistra.fr/viz-bin/asu-fits?-oc.form=dec&'CONTINUE '&-out.max=unlimited&#out.form=FITS (ascii) Table&-c.eq=J2000&-c.r= &'CONTINUE ' 2&-c.u=arcmin&-c.geom=r&-x.rs=10&-source=J/MNRAS/422/1495/tablea1,&'CONTINUE 'J/MNRAS/422/1495/tablec1&-order=I&-out=RAJ2000&-out=DEJ2000&-out=M&&'CONTINUE '-out=Zmag&-out=Ymag&-out=Jmag&-out=Hmag&-out=K1mag&-out=K2mag&-out=&'CONTINUE 'pmRA&-out=e_pmRA&-out=pmDE&-out=e_pmDE&-out=chi2&-out=Mmb&-out=n_Mm&'CONTINUE 'b&-out=OName&-out=GCS9&GCS9=GCS9&-out=SimbadName&-out=n_Mmb&' / Full request URL (POST) # INFO = 'contact=cds-question@unistra.fr' / Email or URL to contact publisher # INFO = 'server_software=7.5.3' / Software version # INFO = 'publisher=CDS' / Data centre that produced the VOTable # INFO = 'CatalogsExamined=2' 2 catalogues with potential matches were examined. INFO = 'ivoid=ivo://cds.vizier/j/mnras/422/1495' / IVOID of underlying data collection # INFO = 'creator=Lodieu N.' / First author or institution # INFO = 'cites=bibcode:2012MNRAS.422.1495L' / Article or Data origin sources # INFO = 'original_date=2012' / Year of the article publication # INFO = 'reference_url=https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/422&'CONTINUE '/1495 ' / Dataset landing page # INFO = 'citation=doi:10.26093/cds/vizier.74221495' / Dataset identifier that can be used for citation # INFO = 'publication_date=2024-07-17' / Date of first publication in the data centre # INFO = 'rights_uri=https://cds.unistra.fr/vizier-org/licences_vizier.html' / Licence URI # END XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 224 / Number of bytes per record NAXIS2 = 1379 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 19 / Number of data fields (columns) CDS-CAT = 'J/MNRAS/422/1495' / Catalogue designation in CDS nomenclature UKIDSS Galactic Clusters Survey Pleiades members (Lodieu+ 2012) EXTNAME = 'J_MNRAS_422_1495_tablea1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablea1' / Table name in METAtab Sample of 1379 known Pleiades member candidates previously published in the literature and recovered in GCS DR9 TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:11 offset=1 TFORM1 = 'A11 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 14 / UCD=pos.eq.dec;meta.main char:11 offset=13 TFORM2 = 'A11 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 26 / UCD=meta.number short: ....... offset=25 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 30 / UCD=phot.mag;em.opt.I float: . offset=29 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 37 / UCD=phot.mag;em.IR.J float: .. offset=36 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 44 / UCD=phot.mag;em.IR.J float: .. offset=43 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 51 / UCD=phot.mag;em.IR.H float: .. offset=50 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 58 / UCD=phot.mag;em.IR.K float: .. offset=57 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude at first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 65 / UCD=phot.mag;em.IR.K float: .. offset=64 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude at second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 72 / UCD=pos.pm;pos.eq.ra float: .. offset=71 TFORM10 = 'F7.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 80 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=79 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 86 / UCD=pos.pm;pos.eq.dec float: . offset=85 TFORM12 = 'F7.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.fit.chi2;stat.fit.param;m offset=99 TFORM14 = 'F6.2 ' / Fortran Format TTYPE14 = 'chi2 ' / ? Reduced chi^2^ statistic of the astrometric fit (only in tablea1.dat) TBCOL15 = 107 / UCD=stat.probability float: .. offset=106 TFORM15 = 'F5.2 ' / Fortran Format TTYPE15 = 'Mmb ' / [0/1]? Membership probability (tablea1.dat) TBCOL16 = 113 / UCD=meta.note char:4 ......... offset=112 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'n_Mmb ' / Note on Mmb (tablea1.dat) (1) (link) TBCOL17 = 118 / UCD=meta.id;meta.main char:77* offset=117 TFORM17 = 'A77 ' / Fortran Format TTYPE17 = 'OName ' / Other name(s) (2) (link) TBCOL18 = 196 / UCD=meta.ref.url char:4 ...... offset=195 TFORM18 = 'A4 ' / Fortran Format TTYPE18 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL19 = 201 / UCD=meta.id char:23 .......... offset=200 TFORM19 = 'A23 ' / Fortran Format TTYPE19 = 'SimbadName' / Simbad column added by the CDS END +03 27 54.26 +24 56 10.9 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.47 0.93 DH003 GCS9 Cl* Melotte 22 DH 003 +03 29 58.76 +23 22 18.3 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.47 0.81 DH009 GCS9 Cl* Melotte 22 DH 009 +03 30 08.28 +22 38 38.3 0 15.346 14.741 13.680 13.020 12.895 12.805 16.33 3.42 -72.94 3.42 74.29 0.00 DH010 GCS9 Cl* Melotte 22 DH 010 +03 30 35.39 +23 03 07.9 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.44 0.63 DH012 GCS9 Cl* Melotte 22 DH 012 +03 31 14.64 +25 58 51.7 0 12.750 12.443 11.925 11.381 11.076 11.107 33.12 3.36 -39.84 3.36 2.35 0.00 DH015 GCS9 Cl* Melotte 22 DH 015 +03 31 29.60 +26 30 12.1 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 0.26 0.42 DH016 GCS9 Cl* Melotte 22 DH 016 +03 32 07.87 +23 13 57.2 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.46 0.84 DH017 GCS9 Cl* Melotte 22 DH 017 +03 32 28.30 +22 28 06.3 0 17.598 17.092 16.403 15.754 15.410 15.409 10.61 3.53 -31.53 3.53 0.64 0.00 DH019 GCS9 Cl* Melotte 22 DH 019 +03 32 32.97 +22 18 12.1 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 0.54 0.36 DH020 GCS9 Cl* Melotte 22 DH 020 +03 32 57.57 +27 17 19.4 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.87 0.80 DH024 GCS9 Cl* Melotte 22 DH 024 +03 33 10.51 +22 31 19.0 0 12.839 12.556 12.064 11.566 11.249 11.279 31.96 3.41 -37.15 3.41 2.39 0.00 DH027 GCS9 Cl* Melotte 22 DH 027 +03 33 39.01 +22 21 08.5 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.58 0.87 DH029 GCS9 Cl* Melotte 22 DH 029 +03 33 46.65 +23 48 19.4 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 5.20 0.94 DH030 GCS9 Cl* Melotte 22 DH 030 +03 33 49.81 +22 56 19.6 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.39 0.83 DH031 GCS9 Cl* Melotte 22 DH 031 +03 34 19.15 +22 27 59.6 0 14.056 13.606 13.051 12.550 12.227 12.213 27.04 2.94 -46.83 2.94 0.19 0.23 DH033 GCS9 Cl* Melotte 22 DH 033 +03 34 19.37 +22 48 42.2 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 0.22 0.12 DH034 GCS9 Cl* Melotte 22 DH 034 +03 34 41.55 +26 09 27.1 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 1.01 0.04 DH036 GCS9 Cl* Melotte 22 DH 036 +03 34 47.54 +26 22 13.4 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.12 0.60 DH039 GCS9 Cl* Melotte 22 DH 039 +03 34 54.95 +22 04 46.2 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.10 0.66 DH040 GCS9 Cl* Melotte 22 DH 040 +03 35 04.72 +25 50 48.0 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.30 0.85 DH041 GCS9 Cl* Melotte 22 DH 041 +03 35 09.41 +24 14 19.8 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 1.67 0.83 DH042 GCS9 Cl* Melotte 22 DH 042 +03 35 10.44 +24 31 54.4 0 14.645 14.122 13.531 12.972 12.661 12.629 17.82 2.63 -32.24 2.63 0.87 0.06 DH043 GCS9 Cl* Melotte 22 DH 043 +03 35 16.28 +23 47 57.8 0 15.198 14.649 14.056 13.523 13.179 13.195 21.38 2.61 -33.42 2.61 1.99 0.09 DH044 GCS9 Cl* Melotte 22 DH 044 +03 35 39.80 +25 38 45.2 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.31 0.68 DH046 GCS9 Cl* Melotte 22 DH 046 +03 35 44.28 +22 27 09.7 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.15 0.90 DH047 GCS9 Cl* Melotte 22 DH 047 +03 35 49.68 +25 04 48.0 0 13.372 13.029 12.506 11.959 11.707 11.697 32.03 2.62 -31.99 2.62 9.53 0.00 DH048 GCS9 Cl* Melotte 22 DH 048 +03 35 50.29 +25 42 20.5 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 0.36 0.02 DH049 GCS9 Cl* Melotte 22 DH 049 +03 35 56.06 +25 21 59.3 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.21 0.87 DH050 GCS9 Cl* Melotte 22 DH 050 +03 36 05.33 +25 21 03.4 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.66 0.94 DH051 GCS9 Cl* Melotte 22 DH 051 +03 36 11.07 +23 48 23.0 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 1.57 0.61 DH053 GCS9 Cl* Melotte 22 DH 053 +03 36 16.32 +25 08 48.8 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.50 0.91 DH054 GCS9 Cl* Melotte 22 DH 054 +03 36 22.33 +22 44 32.7 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 1.33 0.85 DH055 GCS9 Cl* Melotte 22 DH 055 +03 36 24.18 +22 37 24.3 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 1.67 0.93 DH056 GCS9 Cl* Melotte 22 DH 056 +03 36 26.17 +22 14 45.4 0 14.225 13.898 13.373 12.799 12.515 12.522 26.10 2.94 -40.80 2.94 0.12 0.51 DH057 GCS9 Cl* Melotte 22 DH 057 +03 36 27.37 +24 41 17.1 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 0.63 0.35 DH058 GCS9 Cl* Melotte 22 DH 058 +03 36 42.67 +28 21 05.9 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.46 0.78 DH062 GCS9 Cl* Melotte 22 DH 062 +03 36 44.12 +22 01 38.8 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.74 0.85 DH063 GCS9 Cl* Melotte 22 DH 063 +03 36 46.48 +28 15 05.9 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 0.80 0.06 DH064 GCS9 Cl* Melotte 22 DH 064 +03 37 03.48 +24 44 35.3 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 1.44 0.46 HCG6_HHJ392_DH066 GCS9 Cl* Melotte 22 DH 066 +03 37 10.51 +25 17 34.6 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 0.86 0.16 DH067 GCS9 Cl* Melotte 22 DH 067 +03 37 11.98 +26 46 28.7 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.54 0.65 HHJ242_DH068 GCS9 Cl* Melotte 22 HHJ 242 +03 37 15.61 +26 29 29.8 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.75 0.85 HHJ19 GCS9 Cl* Melotte 22 HHJ 19 +03 37 16.53 +23 11 04.2 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.52 0.84 HHJ179_DH069 GCS9 Cl* Melotte 22 HHJ 179 +03 37 16.71 +25 44 11.1 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 0.91 0.22 HHJ154_DH070 GCS9 Cl* Melotte 22 HHJ 154 +03 37 26.39 +24 34 01.2 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.79 0.93 DH071 GCS9 Cl* Melotte 22 DH 071 +03 37 26.62 +26 41 27.5 0 12.870 12.622 12.135 11.531 11.320 11.360 40.30 3.30 -47.71 3.30 0.63 0.00 HHJ422 GCS9 Cl* Melotte 22 HHJ 422 +03 37 30.28 +28 32 26.4 0 15.565 15.067 14.516 13.880 13.587 13.594 6.71 4.97 -40.29 4.97 0.50 0.02 DH072 GCS9 Cl* Melotte 22 DH 072 +03 37 30.69 +24 50 54.3 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 6.17 0.34 HHJ110 GCS9 Cl* Melotte 22 HHJ 110 +03 37 34.18 +24 42 15.1 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2.46 0.51 DH074 GCS9 Cl* Melotte 22 DH 074 +03 37 36.01 +26 32 48.4 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 1.02 0.48 DH075 GCS9 Cl* Melotte 22 DH 075 +03 37 37.70 +26 21 04.0 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.33 0.83 HHJ172_DH076 GCS9 Cl* Melotte 22 HHJ 172 +03 37 40.23 +24 42 58.0 0 13.786 13.370 12.837 12.338 12.034 12.043 27.94 2.48 -39.53 2.48 1.40 0.09 HHJ283 GCS9 Cl* Melotte 22 HHJ 283 +03 37 47.51 +24 53 46.1 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 1.67 0.86 HHJ72_DH078 GCS9 Cl* Melotte 22 HHJ 72 +03 37 48.93 +26 51 45.1 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.33 0.86 HHJ137_DH079 GCS9 Cl* Melotte 22 HHJ 137 +03 37 54.79 +25 26 31.3 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.58 0.83 HHJ402 GCS9 Cl* Melotte 22 HHJ 402 +03 37 56.42 +23 22 56.6 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.16 0.65 HHJ268_DH080 GCS9 Cl* Melotte 22 HHJ 268 +03 38 02.05 +24 20 15.1 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 2.15 0.92 HHJ248_DH081 GCS9 Cl* Melotte 22 HHJ 248 +03 38 08.81 +21 14 49.0 0 12.497 12.220 11.788 11.506 10.905 11.259 29.15 3.41 -36.90 3.41 0.74 0.04 DH082 GCS9 Cl* Melotte 22 DH 082 +03 38 10.27 +25 11 32.9 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 3.31 0.88 HHJ35 GCS9 Cl* Melotte 22 HHJ 35 +03 38 13.04 +23 37 20.7 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 2.58 0.84 HHJ32 GCS9 Cl* Melotte 22 HHJ 32 +03 38 13.04 +24 38 16.8 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 0.97 0.42 HHJ149_DH084 GCS9 Cl* Melotte 22 HHJ 149 +03 38 24.22 +25 19 49.2 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.46 0.92 DH086 GCS9 Cl* Melotte 22 DH 086 +03 38 24.80 +26 15 23.5 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 0.30 0.27 HHJ295_DH087 GCS9 Cl* Melotte 22 HHJ 295 +03 38 27.52 +25 30 18.1 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 1.94 0.69 HHJ2 GCS9 Cl* Melotte 22 HHJ 2 +03 38 27.83 +26 51 25.4 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.31 0.81 HHJ121_DH089 GCS9 Cl* Melotte 22 HHJ 121 +03 38 34.21 +23 43 07.3 0 14.985 14.547 13.940 13.424 13.106 13.105 8.23 2.50 -53.41 2.50 12.39 0.00 HHJ98_DH090 GCS9 Cl* Melotte 22 HHJ 98 +03 38 34.49 +23 40 22.3 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 3.34 0.48 HHJ97_DH091 GCS9 Cl* Melotte 22 HHJ 97 +03 38 43.30 +25 22 26.9 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 1.32 0.79 HCG11_HHJ378_DH092 GCS9 Cl* Melotte 22 HCG 378 +03 38 45.75 +24 28 03.7 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 2.42 0.84 HHJ91_DH094 GCS9 Cl* Melotte 22 HHJ 91 +03 38 53.89 +24 25 07.7 0 13.542 13.293 12.817 12.269 11.999 11.985 12.60 2.48 -33.86 2.48 1.14 0.03 HCG15 GCS9 Cl* Melotte 22 HCG 15 +03 38 54.16 +24 42 15.6 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 1.14 0.50 HHJ63 GCS9 Cl* Melotte 22 HHJ 63 +03 39 03.31 +26 29 30.9 0 13.943 13.592 13.088 12.528 12.249 12.283 35.52 3.00 -41.84 3.00 0.41 0.00 HHJ317 GCS9 Cl* Melotte 22 HHJ 317 +03 39 04.06 +27 00 04.7 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 0.70 0.22 DH098 GCS9 Cl* Melotte 22 DH 098 +03 39 08.13 +24 46 14.4 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.74 0.91 HCG16_HHJ398_DH099 GCS9 Cl* Melotte 22 HCG 16 +03 39 13.33 +25 43 49.5 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.43 0.90 HHJ349_DH100 GCS9 Cl* Melotte 22 HHJ 349 +03 39 15.56 +26 48 03.6 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.54 0.74 HHJ66_DH102 GCS9 Cl* Melotte 22 HHJ 66 +03 39 16.75 +24 57 38.5 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 3.81 0.85 DH103 GCS9 Cl* Melotte 22 DH 103 +03 39 17.05 +22 27 10.9 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 2.56 0.69 IPMBD43 GCS9 Cl* Melotte 22 IPMBD 43 +03 39 22.07 +24 02 39.1 0 15.165 14.761 14.179 13.633 13.370 13.330 9.68 2.60 -31.09 2.60 1.34 0.00 BPL1 GCS9 Cl* Melotte 22 BPL 1 +03 39 22.42 +26 11 36.0 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 1.11 0.93 HHJ224_DH104_Moraux2003_51 GCS9 Cl* Melotte 22 HHJ 224 +03 39 28.99 +25 34 55.8 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 1.61 0.41 HHJ359 GCS9 Cl* Melotte 22 HHJ 359 +03 39 31.49 +20 04 40.4 0 13.372 13.019 12.545 11.954 11.659 11.704 32.73 3.42 -49.02 3.42 0.24 0.00 DH106 GCS9 Cl* Melotte 22 DH 106 +03 39 32.32 +24 16 01.1 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.97 0.91 BPL2_DH107 GCS9 Cl* Melotte 22 BPL 2 +03 39 35.46 +24 07 06.1 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 2.91 0.87 HHJ407_DH108 GCS9 Cl* Melotte 22 HHJ 407 +03 39 41.12 +23 28 23.3 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.78 0.70 HHJ83 GCS9 Cl* Melotte 22 HHJ 83 +03 39 42.72 +23 54 27.6 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 1.07 0.88 HCG22_T2B_BPL3_HHJ230_DH109 GCS9 Cl* Melotte 22 HCG 22 +03 39 43.32 +23 12 25.3 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.27 0.67 DH110 GCS9 Cl* Melotte 22 DH 110 +03 39 44.19 +22 07 45.5 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.96 0.92 HHJ141_DH111 GCS9 Cl* Melotte 22 HHJ 141 +03 39 44.37 +26 18 18.8 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 0.49 0.59 Moraux2003_82 GCS9 Cl* Melotte 22 MBSC 82 +03 39 44.79 +22 39 15.3 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.93 0.94 DH2004_112 GCS9 Cl* Melotte 22 DH 112 +03 39 46.35 +23 58 52.9 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 3.77 0.69 HHJ324_BPL4_DH113 GCS9 Cl* Melotte 22 HHJ 324 +03 39 47.02 +26 21 14.3 0 14.554 14.117 13.546 12.997 12.694 12.688 28.81 3.00 -44.81 3.00 0.48 0.10 HHJ178_DH114_Moraux2003_57 GCS9 Cl* Melotte 22 HHJ 178 +03 39 47.99 +23 50 56.6 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 1.60 0.92 HHJ291_BPL5_DH115 GCS9 Cl* Melotte 22 HHJ 291 +03 39 48.51 +23 46 03.8 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 1.27 0.83 HHJ74_BPL6 GCS9 Cl* Melotte 22 HHJ 74 +03 39 49.72 +23 03 26.2 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.30 0.89 HHJ138_DH117 GCS9 Cl* Melotte 22 HHJ 138 +03 39 50.68 +23 45 52.3 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 1.25 0.89 BPL7_DH118 GCS9 Cl* Melotte 22 BPL 7 +03 39 53.53 +25 46 46.2 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.24 0.92 DH2004_119 GCS9 Cl* Melotte 22 DH 119 +03 39 55.21 +24 12 54.1 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 1.49 0.86 BPL8 GCS9 Cl* Melotte 22 BPL 8 +03 39 57.15 +26 07 00.1 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.32 0.82 Moraux2003_94 GCS9 Cl* Melotte 22 MBSC 94 +03 39 57.35 +23 19 42.2 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.39 0.61 HHJ103 GCS9 Cl* Melotte 22 HHJ 103 +03 39 57.85 +25 55 29.7 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 0.41 0.40 DH120_Moraux2003_92 GCS9 Cl* Melotte 22 DH 120 +03 40 00.19 +23 26 05.7 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 1.85 0.85 HCG26_SK802_HHJ403_DH121 GCS9 Cl* Melotte 22 HCG 26 +03 40 01.82 +22 59 20.2 0 12.400 12.141 11.716 11.468 10.921 11.098 22.71 2.97 -65.69 2.97 2.24 0.00 HHJ426 GCS9 Cl* Melotte 22 HHJ 426 +03 40 01.84 +25 04 19.5 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 3.13 0.93 HHJ102 GCS9 Cl* Melotte 22 HHJ 102 +03 40 01.87 +24 46 25.9 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.70 0.94 HCG24_T35B_HHJ226_DH122 GCS9 Cl* Melotte 22 HCG 24 +03 40 03.61 +24 30 02.7 0 13.674 13.270 12.776 12.243 11.963 11.937 2.13 2.48 -31.06 2.48 34.28 0.00 BPL9 GCS9 Cl* Melotte 22 BPL 9 +03 40 05.05 +25 31 36.6 0 13.648 13.372 12.896 12.232 12.056 12.038 42.65 2.95 -35.50 2.95 0.66 0.00 HHJ356 GCS9 Cl* Melotte 22 HHJ 356 +03 40 05.98 +25 40 20.8 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.51 0.89 DH124 GCS9 Cl* Melotte 22 DH 124 +03 40 06.21 +28 08 32.0 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.84 0.62 DH125 GCS9 Cl* Melotte 22 DH 125 +03 40 07.01 +22 38 47.5 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.63 0.89 HHJ114 GCS9 Cl* Melotte 22 HHJ 114 +03 40 07.11 +24 13 03.3 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.93 0.90 HHJ38_BPL10_DH126 GCS9 Cl* Melotte 22 HHJ 38 +03 40 09.69 +23 10 32.4 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 0.76 0.41 HCG31_DH127 GCS9 Cl* Melotte 22 HCG 31 +03 40 10.93 +26 06 40.8 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.32 0.67 HCG28_HHJ177_DH128 GCS9 Cl* Melotte 22 HCG 28 +03 40 11.04 +25 23 26.6 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.26 0.87 HHJ147_DH129 GCS9 Cl* Melotte 22 HHJ 147 +03 40 11.93 +25 52 32.3 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 0.57 0.39 HHJ29 GCS9 Cl* Melotte 22 HHJ 29 +03 40 12.74 +23 09 35.6 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 0.50 0.08 HCG32_SK794_HHJ280_DH130 GCS9 Cl* Melotte 22 HCG 32 +03 40 14.80 +25 50 05.5 0 13.923 13.506 12.914 12.433 12.114 12.129 8.07 2.95 -22.54 2.95 0.30 0.00 SK781 GCS9 Cl* Melotte 22 SK 781 +03 40 14.91 +25 19 18.7 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.67 0.87 SK785_HHJ397_DH131 GCS9 Cl* Melotte 22 SK 785 +03 40 15.93 +24 22 31.3 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 4.56 0.34 BPL11 GCS9 Cl* Melotte 22 BPL 11 +03 40 23.06 +25 29 47.6 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.50 0.93 HCG33_HHJ343_DH134 GCS9 Cl* Melotte 22 HCG 33 +03 40 23.84 +23 04 09.0 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.58 0.90 HHJ162_DH135 GCS9 Cl* Melotte 22 HHJ 162 +03 40 24.20 +24 35 04.0 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.86 0.91 HCG34_SK777_BPL12_DH136 GCS9 Cl* Melotte 22 HCG 34 +03 40 25.62 +24 06 00.0 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 1.37 0.94 HHJ135_BPL13_DH138 GCS9 Cl* Melotte 22 HHJ 135 +03 40 26.27 +23 21 29.1 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.57 0.71 HHJ167 GCS9 Cl* Melotte 22 HHJ 167 +03 40 26.41 +24 05 23.5 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 1.79 0.84 SK778_HHJ338_BPL14_DH139 GCS9 Cl* Melotte 22 SK 778 +03 40 26.95 +24 14 14.2 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 1.17 0.94 HHJ169_BPL15_DH140 GCS9 Cl* Melotte 22 HHJ 169 +03 40 27.93 +24 12 09.3 0 19.730 18.501 17.352 16.700 16.088 0.100 15.91 3.22 -42.54 3.22 5.78 0.62 int-pl-IZ-84;IPLJ0340279+241209_Y GCS9 Cl* Melotte 22 IPL 84 +03 40 31.17 +25 08 52.8 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 2.05 0.93 HCG36_T102_HHJ329_DH142 GCS9 Cl* Melotte 22 HCG 36 +03 40 31.50 +23 33 02.0 0 12.655 12.386 11.899 11.459 11.074 11.141 21.40 2.12 -35.26 2.12 8.58 0.58 SK773_DH143 GCS9 Cl* Melotte 22 SK 773 +03 40 32.59 +25 28 40.6 0 15.026 14.501 13.946 13.420 13.114 13.099 10.36 2.23 -47.38 2.23 1.93 0.21 HHJ101_DH144 GCS9 Cl* Melotte 22 HHJ 101 +03 40 35.33 +20 57 56.6 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 0.48 0.01 DH146 GCS9 Cl* Melotte 22 DH 146 +03 40 35.50 +23 13 07.4 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 5.06 0.44 L07_A1_1 GCS9 +03 40 39.46 +23 26 34.8 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 2.27 0.69 DH147_L07_A1_2 GCS9 Cl* Melotte 22 DH 147 +03 40 40.32 +25 50 48.1 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 1.34 0.94 DH148 GCS9 Cl* Melotte 22 DH 148 +03 40 43.20 +22 49 53.8 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 2.33 0.90 DH151_L07_193 GCS9 Cl* Melotte 22 DH 151 +03 40 43.27 +25 11 55.4 0 12.877 12.623 12.124 11.589 11.329 41.44 2.97 -27.65 2.97 3.03 0.00 SK754 GCS9 Cl* Melotte 22 SK 754 +03 40 49.40 +21 12 55.3 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 0.41 0.39 DH152 GCS9 Cl* Melotte 22 DH 152 +03 40 51.08 +20 41 17.2 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.28 0.75 DH155 GCS9 Cl* Melotte 22 DH 155 +03 40 51.46 +19 32 45.8 0 14.044 13.716 13.240 12.620 12.349 12.346 27.56 5.01 -46.86 5.01 1.30 0.17 DH157 GCS9 Cl* Melotte 22 DH 157 +03 40 51.82 +23 13 50.5 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 1.21 0.93 HCG43_HHJ234_DH158_L07_169 GCS9 Cl* Melotte 22 HCG 43 +03 40 54.48 +22 54 25.5 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 1.48 0.93 HHJ123_DH159_L07_182 GCS9 Cl* Melotte 22 HHJ 123 +03 40 55.05 +22 20 58.7 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 1.06 0.40 HCG44_SK758_HHJ355_DH160 GCS9 Cl* Melotte 22 HCG 44 +03 40 55.30 +25 34 57.4 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 7.86 0.69 L07_A1_4 GCS9 +03 40 56.06 +28 43 39.0 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 0.85 0.21 DH161 GCS9 Cl* Melotte 22 DH 161 +03 40 59.26 +25 11 55.2 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.63 0.86 HHJ41_DH162 GCS9 Cl* Melotte 22 HHJ 41 +03 41 00.39 +24 13 35.0 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.81 0.89 BPL17 GCS9 Cl* Melotte 22 BPL 17 +03 41 01.99 +24 55 21.2 0 14.847 14.451 13.855 13.287 13.009 14.19 2.97 -32.57 2.97 1.14 0.03 HHJ126 GCS9 Cl* Melotte 22 HHJ 126 +03 41 02.99 +23 43 21.4 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.61 0.71 HCG45_HHJ290_BPL18_DH163 GCS9 Cl* Melotte 22 HCG 45 +03 41 05.23 +23 50 14.9 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 1.49 0.85 BPL19 GCS9 Cl* Melotte 22 BPL 19 +03 41 10.27 +25 45 55.9 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 1.32 0.94 SK733_DH164 GCS9 Cl* Melotte 22 SK 733 +03 41 13.21 +24 05 25.6 0 20.248 19.168 17.791 16.898 16.325 0.166 14.11 3.48 -46.41 3.48 1.51 0.61 int-pl-IZ-81;IPLJ0341131+240525_Y GCS9 Cl* Melotte 22 IPL 81 +03 41 19.35 +23 51 41.7 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 1.15 0.90 HCG48_DH167 GCS9 Cl* Melotte 22 HCG 48 +03 41 19.86 +25 06 49.0 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.45 0.87 HHJ191 GCS9 Cl* Melotte 22 HHJ 191 +03 41 20.00 +22 37 53.7 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 6.59 0.30 SK739_DH168 GCS9 Cl* Melotte 22 SK 739 +03 41 22.45 +24 23 51.6 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 1.05 0.90 DH169 GCS9 Cl* Melotte 22 DH 169 +03 41 22.88 +23 55 48.1 0 14.135 13.702 13.150 12.615 12.323 12.335 24.42 2.12 -38.30 2.12 3.58 0.57 HCG49_HHJ233_BPL20_DH170 GCS9 Cl* Melotte 22 HCG 49 +03 41 23.63 +27 24 16.9 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 0.33 0.40 DH171 GCS9 Cl* Melotte 22 DH 171 +03 41 24.65 +24 15 13.9 0 16.242 15.768 15.135 14.504 14.192 14.176 32.40 2.16 -41.17 2.16 1.95 0.00 DH172 GCS9 Cl* Melotte 22 DH 172 +03 41 24.69 +25 23 05.8 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 3.60 0.78 HHJ117_L07_48 GCS9 Cl* Melotte 22 HHJ 117 +03 41 25.33 +22 32 55.8 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.81 0.89 HCG51_SK732_HHJ345_DH173 GCS9 Cl* Melotte 22 HCG 51 +03 41 25.97 +23 15 35.8 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 2.57 0.94 L07_170 GCS9 +03 41 26.36 +23 08 02.7 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 11.80 0.92 DH174_L07_168 GCS9 Cl* Melotte 22 DH 174 +03 41 26.90 +24 01 02.3 0 13.364 12.912 12.305 11.845 11.430 11.454 25.17 2.12 -47.48 2.12 4.23 0.38 SK729_HHJ340_BPL21_DH175 GCS9 Cl* Melotte 22 SK 729 +03 41 29.39 +24 53 40.1 0 14.735 14.330 13.741 13.182 12.863 33.87 2.97 -32.81 2.97 1.30 0.00 Moraux2003_63 GCS9 Cl* Melotte 22 MBSC 63 +03 41 29.70 +24 32 26.5 0 13.241 12.967 12.496 11.906 11.680 23.06 2.97 -37.00 2.97 0.84 0.46 BPL22 GCS9 Cl* Melotte 22 BPL 22 +03 41 30.35 +25 17 05.9 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 104.59 0.60 L07_A1_5_L07_214 GCS9 +03 41 31.21 +24 03 24.7 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 2.29 0.89 HHJ90_BPL23_DH177 GCS9 Cl* Melotte 22 HHJ 90 +03 41 33.58 +27 09 50.1 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.47 0.89 DH178 GCS9 Cl* Melotte 22 DH 178 +03 41 33.90 +23 11 44.9 0 16.186 15.643 15.029 14.509 14.159 14.137 30.84 2.27 -37.70 2.27 1.56 0.00 HHJ12_L07_A1_6 GCS9 Cl* Melotte 22 HHJ 12 +03 41 34.80 +23 38 15.6 0 15.153 14.666 14.080 13.522 13.242 13.186 15.91 2.13 -49.58 2.13 0.83 0.48 DH180 GCS9 Cl* Melotte 22 DH 180 +03 41 36.55 +22 41 01.7 0 16.280 15.724 15.122 14.591 14.266 14.232 32.02 2.27 -36.94 2.27 19.82 0.00 L07_photNM_21 GCS9 +03 41 37.26 +25 08 32.6 0 14.630 14.170 13.596 13.084 12.736 28.66 2.97 -44.60 2.97 1.75 0.12 HHJ148_DH182 GCS9 Cl* Melotte 22 HHJ 148 +03 41 38.81 +24 23 09.2 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2.29 0.59 BPL25 GCS9 Cl* Melotte 22 BPL 25 +03 41 38.87 +22 16 40.3 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 1.28 0.00 HHJ176_DH183 GCS9 Cl* Melotte 22 HHJ 176 +03 41 39.84 +24 52 30.0 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 1.71 0.88 HHJ69_Moraux2003_84 GCS9 Cl* Melotte 22 HHJ 69 +03 41 40.91 +25 54 24.1 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 4.70 0.74 L07_A1_8_L07_248 GCS9 +03 41 41.46 +22 58 10.0 0 15.253 14.805 14.250 13.646 13.328 13.331 2.08 2.26 -35.29 2.26 3.25 0.00 L07_184 GCS9 +03 41 42.41 +23 54 57.1 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 0.56 0.41 HHJ6_BPL26 GCS9 Cl* Melotte 22 HHJ 6 +03 41 43.72 +23 07 59.7 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 5.75 0.60 L07_A1_9 GCS9 +03 41 45.07 +22 28 01.8 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2.91 0.59 DH185 GCS9 Cl* Melotte 22 DH 185 +03 41 46.44 +24 55 33.1 0 15.937 15.504 14.923 14.371 14.019 10.89 2.99 -39.19 2.99 1.45 0.34 DH186 GCS9 Cl* Melotte 22 DH 186 +03 41 47.09 +25 00 21.9 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.91 0.92 HHJ125 GCS9 Cl* Melotte 22 HHJ 125 +03 41 48.05 +26 47 20.7 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 0.69 0.53 HHJ357_DH188 GCS9 Cl* Melotte 22 HHJ 357 +03 41 49.60 +22 29 36.7 0 15.602 14.992 14.388 13.890 13.505 13.492 29.63 2.51 -46.82 2.51 3.79 0.01 DH189 GCS9 Cl* Melotte 22 DH 189 +03 41 51.50 +24 19 27.3 0 16.617 15.976 15.244 14.667 14.283 0.009 16.53 2.33 -37.48 2.33 3.69 0.46 int-pl-IZ-88;IPLJ0341515+241927_BPL27_Y GCS9 Cl* Melotte 22 IPL 88 +03 41 52.32 +24 41 57.6 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 0.69 0.50 HHJ120_BPL28_DH190 GCS9 Cl* Melotte 22 HHJ 120 +03 41 52.94 +24 07 24.7 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 2.22 0.82 HHJ108_BPL29_DH191 GCS9 Cl* Melotte 22 HHJ 108 +03 41 53.05 +27 04 42.9 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.25 0.92 DH192 GCS9 Cl* Melotte 22 DH 192 +03 41 54.16 +23 05 04.7 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 4.74 0.69 L07_A1_10 GCS9 +03 41 54.21 +25 43 47.1 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 1.80 0.89 HCG55_B346_HHJ342_DH194 GCS9 Cl* Melotte 22 HCG 55 +03 41 56.49 +27 02 57.9 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.17 0.86 DH195 GCS9 Cl* Melotte 22 DH 195 +03 41 56.72 +23 58 43.2 0 15.234 14.770 14.109 13.584 13.195 14.88 2.31 -33.12 2.31 0.99 0.07 BPL30 GCS9 Cl* Melotte 22 BPL 30 +03 41 58.66 +22 57 01.7 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 10.25 0.90 HCG66_HHJ312_DH196_L07_16 GCS9 Cl* Melotte 22 HCG 66 +03 41 58.86 +26 12 21.1 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 0.27 0.08 HCG56_B117_HHJ351_DH197_Moraux2003_16 GCS9 Cl* Melotte 22 HCG 56 +03 41 59.67 +24 42 18.3 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 1.19 0.65 BPL31_Moraux2003_102 GCS9 Cl* Melotte 22 BPL 31 +03 41 59.75 +26 27 39.6 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.32 0.64 HHJ213_DH200 GCS9 Cl* Melotte 22 HHJ 213 +03 42 00.02 +25 01 47.1 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 1.15 0.88 SK702_HHJ277_DH201 GCS9 Cl* Melotte 22 SK 702 +03 42 00.05 +26 01 12.1 0 14.722 14.226 13.612 13.052 12.712 12.728 13.09 2.23 -31.95 2.23 3.88 0.01 L07_18 GCS9 +03 42 00.37 +24 10 12.6 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 1.87 0.73 BPL32 GCS9 Cl* Melotte 22 BPL 32 +03 42 01.68 +22 23 26.6 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 0.89 0.00 HCG67_HHJ201_DH202 GCS9 Cl* Melotte 22 HCG 67 +03 42 02.87 +24 12 36.0 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.93 0.61 HCG63_HHJ350_BPL33_DH203 GCS9 Cl* Melotte 22 HCG 63 +03 42 02.93 +23 55 53.7 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.46 0.87 HCG64_HHJ406_BPL34_DH204 GCS9 Cl* Melotte 22 HCG 64 +03 42 03.30 +24 32 13.3 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 1.39 0.79 SK701_HHJ354_BPL35_DH205_Moraux2003_11 GCS9 Cl* Melotte 22 SK 701 +03 42 03.42 +25 22 39.1 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 5.58 0.68 HHJ212_DH206_L07_41 GCS9 Cl* Melotte 22 HHJ 212 +03 42 04.16 +22 16 51.0 0 14.270 13.988 13.501 12.878 12.617 40.26 7.97 -17.56 7.97 1.17 0.00 SK708 GCS9 Cl* Melotte 22 SK 708 +03 42 05.74 +23 07 14.4 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2.73 0.41 L07_A1_11 GCS9 +03 42 08.29 +25 36 59.9 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 2.38 0.77 HHJ270_DH211_L07_36 GCS9 Cl* Melotte 22 HHJ 270 +03 42 08.84 +23 35 16.8 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 1.62 0.72 SK699_HHJ384_DH212 GCS9 Cl* Melotte 22 SK 699 +03 42 09.86 +27 57 25.2 0 13.631 13.340 12.849 12.206 11.986 11.978 33.43 3.68 -33.78 3.68 0.13 0.00 DH213 GCS9 Cl* Melotte 22 DH 213 +03 42 10.60 +22 18 05.5 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 0.41 0.00 HHJ55 GCS9 Cl* Melotte 22 HHJ 55 +03 42 10.93 +24 05 08.4 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 1.14 0.85 HCG68_T36_HHJ396_DH214 GCS9 Cl* Melotte 22 HCG 68 +03 42 10.99 +25 44 35.0 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 3.83 0.83 L07_25 GCS9 +03 42 12.62 +26 49 44.9 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.84 0.91 HHJ236_DH215 GCS9 Cl* Melotte 22 HHJ 236 +03 42 13.49 +24 18 49.6 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 0.83 0.48 HHJ34_BPL36 GCS9 Cl* Melotte 22 HHJ 34 +03 42 13.90 +20 21 43.6 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.53 0.76 DH216 GCS9 Cl* Melotte 22 DH 216 +03 42 15.37 +23 11 30.9 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 1.60 0.81 HHJ109_L07_167 GCS9 Cl* Melotte 22 HHJ 109 +03 42 17.90 +24 06 57.6 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.77 0.94 HHJ129_BPL37_DH217 GCS9 Cl* Melotte 22 HHJ 129 +03 42 18.88 +23 59 22.0 0 12.658 12.428 11.944 11.452 11.259 25.66 2.30 -50.92 2.30 2.16 0.01 SK687_HHJ437 GCS9 Cl* Melotte 22 SK 687 +03 42 22.13 +25 03 56.3 0 15.564 15.206 14.659 14.050 13.781 24.49 2.98 -48.94 2.98 8.93 0.19 DH219 GCS9 Cl* Melotte 22 DH 219 +03 42 26.28 +23 51 38.5 0 15.456 14.895 14.283 13.744 13.347 13.357 10.50 2.13 -43.07 2.13 2.55 0.42 HHJ49_BPL38_DH221 GCS9 Cl* Melotte 22 HHJ 49 +03 42 26.29 +24 14 07.7 0 14.155 13.741 13.173 12.659 12.325 12.320 10.47 2.12 -47.50 2.12 2.98 0.21 HCG73_SK680_HHJ262_BPL39_DH223 GCS9 Cl* Melotte 22 HCG 73 +03 42 27.31 +22 34 24.6 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 1.87 0.90 HCG76_HHJ294_DH224 GCS9 Cl* Melotte 22 HCG 76 +03 42 28.43 +27 12 58.1 0 16.484 16.050 15.500 14.978 14.679 14.670 9.87 3.37 -42.08 3.37 0.41 0.18 DH226 GCS9 Cl* Melotte 22 DH 226 +03 42 28.66 +25 01 00.2 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 1.06 0.93 SK676_HHJ362_DH227 GCS9 Cl* Melotte 22 SK 676 +03 42 29.43 +22 47 25.9 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 7.62 0.90 HCG77_A80_DH228 GCS9 Cl* Melotte 22 HCG 77 +03 42 29.51 +22 23 45.7 0 12.961 12.706 12.271 11.802 11.535 25.41 7.95 -63.14 7.95 0.89 0.00 SK682 GCS9 Cl* Melotte 22 SK 682 +03 42 29.71 +20 48 39.6 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.39 0.82 DH229 GCS9 Cl* Melotte 22 DH 229 +03 42 31.21 +24 49 21.2 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 8.89 0.56 HCG74_HHJ82_DH230_Moraux2003_76 GCS9 Cl* Melotte 22 HCG 74 +03 42 33.97 +24 11 00.5 0 15.453 14.926 14.319 13.774 13.408 13.422 11.59 2.13 -52.78 2.13 8.09 0.02 BPL40_DH231 GCS9 Cl* Melotte 22 BPL 40 +03 42 36.27 +23 22 04.7 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 1.08 0.73 HCG79_HHJ241_HHJ241_DH232 GCS9 Cl* Melotte 22 HCG 79 +03 42 36.95 +25 13 57.5 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 2.57 0.70 DH233 GCS9 Cl* Melotte 22 DH 233 +03 42 40.23 +23 59 21.5 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 1.30 0.59 HCG80_T3_HHJ409_DH234 GCS9 Cl* Melotte 22 HCG 80 +03 42 41.18 +24 01 42.7 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.54 0.93 HCG82_HHJ115_BPL41_DH235 GCS9 Cl* Melotte 22 HCG 82 +03 42 41.85 +24 00 15.5 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.31 0.94 BPL42_DH236 GCS9 Cl* Melotte 22 BPL 42 +03 42 42.12 +25 11 48.7 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 1.90 0.82 HHJ45_DH237 GCS9 Cl* Melotte 22 HHJ 45 +03 42 42.41 +23 20 21.5 0 13.380 12.965 12.389 11.853 11.534 11.549 20.26 2.25 -20.48 2.25 1.34 0.00 HCG86_DH238 GCS9 Cl* Melotte 22 HCG 86 +03 42 43.81 +25 32 06.2 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 2.28 0.93 SK665_HHJ341_DH239 GCS9 Cl* Melotte 22 SK 665 +03 42 44.37 +23 06 16.1 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 5.30 0.75 HHJ119_DH240_L07_176 GCS9 Cl* Melotte 22 HHJ 119 +03 42 44.46 +23 58 15.2 0 15.186 14.686 14.062 13.536 13.187 13.188 15.12 2.13 -48.66 2.13 4.84 0.57 BPL43_DH241 GCS9 Cl* Melotte 22 BPL 43 +03 42 45.65 +23 49 22.6 0 15.473 14.898 14.286 13.714 13.361 13.361 22.64 2.13 -48.82 2.13 2.57 0.38 HHJ52_BPL44 GCS9 Cl* Melotte 22 HHJ 52 +03 42 47.30 +23 00 40.3 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 5.33 0.59 L07_A1_13 GCS9 +03 42 48.17 +24 04 01.2 0 17.739 16.847 16.038 15.466 14.936 0.020 19.17 2.24 -33.98 2.24 3.95 0.16 int-pl-IZ-29;IPLJ0342481+240401_BPL45_Y GCS9 Cl* Melotte 22 IPL 29 +03 42 48.91 +23 34 48.4 0 14.623 14.157 13.593 13.067 12.741 12.742 24.86 2.13 -47.62 2.13 1.71 0.49 HHJ153_DH242 GCS9 Cl* Melotte 22 HHJ 153 +03 42 49.11 +24 10 15.4 0 13.648 13.221 12.670 12.134 11.801 11.828 31.63 2.12 -28.60 2.12 1.47 0.00 SK663_BPL46_DH243 GCS9 Cl* Melotte 22 SK 663 +03 42 51.68 +23 08 43.7 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 6.70 0.92 L07_177 GCS9 +03 42 54.00 +26 08 16.1 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 4.13 0.94 HHJ146_DH244_L07_22 GCS9 Cl* Melotte 22 HHJ 146 +03 42 55.88 +22 38 00.4 0 16.581 16.069 15.450 14.845 14.501 0.009 25.97 2.55 -3.49 2.55 1.79 0.00 int-pl-IZ-78;2MASSJ0342558+223800_Y GCS9 Cl* Melotte 22 IPL 78 +03 42 56.55 +22 51 17.9 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 4.45 0.78 L07_191 GCS9 +03 42 56.55 +24 04 57.8 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 1.08 0.82 HCG93_SK658_HHJ401_BPL48_DH245 GCS9 Cl* Melotte 22 HCG 93 +03 42 56.57 +24 13 45.4 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.76 0.92 HCG92_BPL47 GCS9 Cl* Melotte 22 HCG 92 +03 42 58.60 +20 12 45.0 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 0.30 0.10 DH247 GCS9 Cl* Melotte 22 DH 247 +03 42 59.92 +22 42 51.5 0 16.085 15.349 14.650 14.118 13.712 13.703 25.82 2.27 -43.19 2.27 33.85 0.17 L07_A1_14_L07_245 GCS9 +03 43 00.17 +24 43 52.3 0 17.791 16.841 16.022 15.425 14.969 32.72 3.09 -51.43 3.09 12.65 0.00 BPL49_CFHT-Pl-17_M7.9 GCS9 Cl* Melotte 22 BPL 49 +03 43 01.27 +24 14 52.2 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 2.12 0.68 BPL50 GCS9 Cl* Melotte 22 BPL 50 +03 43 01.40 +23 29 30.4 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 1.47 0.94 L07_158 GCS9 +03 43 03.83 +23 54 19.6 0 18.852 17.703 16.751 16.052 15.562 0.046 18.37 2.48 -38.49 2.48 2.92 0.66 int-pl-IZ-25;2MASSJ0343038+235420_Y GCS9 Cl* Melotte 22 IPL 25 +03 43 04.20 +22 48 03.3 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 6.22 0.90 HCG101_B173_HHJ428_DH251 GCS9 Cl* Melotte 22 HCG 101 +03 43 04.37 +25 26 12.0 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 8.13 0.56 HHJ107 GCS9 Cl* Melotte 22 HHJ 107 +03 43 05.54 +24 49 28.3 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 2.77 0.86 HCG97_HHJ432_DH252 GCS9 Cl* Melotte 22 HCG 97 +03 43 06.50 +22 17 49.2 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 4.64 0.67 HHJ18 GCS9 Cl* Melotte 22 HHJ 18 +03 43 07.58 +25 34 29.0 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 2.69 0.92 HCG96_DH253_L07_34 GCS9 Cl* Melotte 22 HCG 96 +03 43 09.75 +24 41 32.7 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 2.88 0.67 HCG100_SK654_T40_HHJ383_BPL51_DH254_Moraux2003_2 GCS9 Cl* Melotte 22 HCG 100 +03 43 11.57 +25 25 22.9 0 13.868 13.412 12.861 12.355 12.020 12.040 6.10 2.23 -53.18 2.23 6.99 0.00 SK650 GCS9 Cl* Melotte 22 SK 650 +03 43 11.65 +24 06 52.4 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 3.13 0.74 HHJ71_BPL52 GCS9 Cl* Melotte 22 HHJ 71 +03 43 11.77 +25 31 31.9 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 12.60 0.72 L07_A1_15 GCS9 +03 43 12.12 +24 44 45.1 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 1.92 0.61 HCG102_SK653_HHJ328_BPL53_DH255 GCS9 Cl* Melotte 22 HCG 102 +03 43 13.07 +24 39 19.3 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 1.16 0.91 HCG103_SK652_HHJ395_BPL54_DH256 GCS9 Cl* Melotte 22 HCG 103 +03 43 15.74 +25 20 29.5 0 13.094 12.779 12.277 11.779 11.507 11.524 15.79 2.23 -24.92 2.23 0.89 0.00 SK646 GCS9 Cl* Melotte 22 SK 646 +03 43 16.61 +23 50 01.5 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.89 0.92 HHJ206_BPL56_DH258 GCS9 Cl* Melotte 22 HHJ 206 +03 43 16.88 +23 59 56.4 0 18.673 17.896 17.104 16.502 16.126 0.042 31.62 2.69 -8.78 2.69 3.68 0.00 int-pl-IZ-24;IPLJ0343168+235956_N GCS9 Cl* Melotte 22 IPL 24 +03 43 18.47 +26 40 24.3 0 13.001 12.604 12.085 11.533 11.239 11.287 49.10 2.94 -45.26 2.94 0.27 0.00 HHJ413 GCS9 Cl* Melotte 22 HHJ 413 +03 43 18.56 +26 30 18.9 0 15.438 14.940 14.415 13.971 13.634 13.654 24.39 2.95 -50.80 2.95 0.64 0.07 HHJ89 GCS9 Cl* Melotte 22 HHJ 89 +03 43 19.01 +22 47 10.4 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 2.58 0.89 HCG110_HHJ143_DH259 GCS9 Cl* Melotte 22 HCG 110 +03 43 19.06 +26 04 43.9 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.67 0.77 SK642_HHJ279_DH260 GCS9 Cl* Melotte 22 SK 642 +03 43 20.58 +24 26 34.9 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.91 0.88 HCG106_HHJ88_BPL57_DH261_Moraux2003_79 GCS9 Cl* Melotte 22 HCG 106 +03 43 21.12 +24 11 09.0 0 15.279 14.803 14.182 13.683 13.346 13.361 11.26 2.13 -42.71 2.13 2.07 0.55 HHJ65 GCS9 Cl* Melotte 22 HHJ 65 +03 43 22.55 +23 00 56.5 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 2.24 0.70 L07_A1_16 GCS9 +03 43 24.99 +23 52 01.2 0 16.867 16.116 15.368 14.820 14.365 0.011 14.16 2.34 -46.02 2.34 1.59 0.53 int-pl-IZ-6;2MASSJ0343249+235201_BPL58_Y GCS9 Cl* Melotte 22 IPL 6 +03 43 25.16 +22 53 44.3 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 1.39 0.85 HHJ134_DH263_L07_179 GCS9 Cl* Melotte 22 HHJ 134 +03 43 26.20 +26 02 30.6 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 3.41 0.92 HCG105_SK633_HHJ327_DH264 GCS9 Cl* Melotte 22 HCG 105 +03 43 26.44 +22 42 42.5 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 1.69 0.56 HCG114_SK644_HHJ261_DH265_L07_189 GCS9 Cl* Melotte 22 HCG 114 +03 43 26.98 +24 27 09.6 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.43 0.92 SK638_HHJ368_BPL59_DH267_Moraux2003_6 GCS9 Cl* Melotte 22 SK 638 +03 43 27.44 +22 37 41.0 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 1.02 0.67 HHJ70 GCS9 Cl* Melotte 22 HHJ 70 +03 43 28.21 +24 53 30.9 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.59 0.87 HCG109_SK635_DH268 GCS9 Cl* Melotte 22 HCG 109 +03 43 28.74 +24 09 06.1 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.61 0.90 HHJ76 GCS9 Cl* Melotte 22 HHJ 76 +03 43 29.90 +24 39 23.4 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 4.29 0.52 HHJ50_DH269_Moraux2003_87 GCS9 Cl* Melotte 22 HHJ 50 +03 43 34.14 +25 35 25.8 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 1.22 0.93 HCG112_DH270_Moraux2003_19 GCS9 Cl* Melotte 22 HCG 112 +03 43 34.49 +25 57 30.6 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 16.35 0.40 L07_A1_17 GCS9 +03 43 35.22 +25 24 30.8 0 13.655 13.247 12.728 12.188 11.914 11.926 10.07 2.23 -45.01 2.23 1.09 0.30 SK622_HHJ337_DH272 GCS9 Cl* Melotte 22 SK 622 +03 43 36.57 +23 12 34.1 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 6.15 0.79 HCG122_T6_HHJ205_DH274_L07_174 GCS9 Cl* Melotte 22 HCG 122 +03 43 36.68 +25 47 00.5 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 1.31 0.85 SK619_DH276_Moraux2003_22 GCS9 Cl* Melotte 22 SK 619 +03 43 37.11 +23 38 31.9 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.60 0.91 SK630_DH278 GCS9 Cl* Melotte 22 SK 630 +03 43 37.32 +25 24 32.0 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 1.32 0.69 HCG115_SK620_HHJ377_DH279 GCS9 Cl* Melotte 22 HCG 115 +03 43 39.05 +23 44 05.2 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 5.74 0.93 HCG124_SK624_BPL61_DH281 GCS9 Cl* Melotte 22 HCG 124 +03 43 39.72 +23 41 32.4 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.60 0.86 HHJ40 GCS9 Cl* Melotte 22 HHJ 40 +03 43 40.31 +24 30 11.2 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2.96 0.36 BPL62_Roque7_CFHT-Pl-24_M8.3 GCS9 Cl* Melotte 22 BPL 62 +03 43 42.14 +24 34 23.1 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 3.04 0.87 HCG123_SK616_MT41_DH282 GCS9 Cl* Melotte 22 HCG 123 +03 43 42.90 +25 51 37.0 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 7.04 0.78 HHJ77_Moraux2003_81_L07_27 GCS9 Cl* Melotte 22 HHJ 77 +03 43 43.15 +24 32 56.0 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.40 0.69 DH283_Moraux2003_77 GCS9 Cl* Melotte 22 DH 283 +03 43 43.53 +24 12 50.0 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 1.15 0.89 HHJ43_DH284 GCS9 Cl* Melotte 22 HHJ 43 +03 43 43.71 +24 29 15.5 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2.25 0.55 HHJ374_BPL63_DH285_Moraux2003_5 GCS9 Cl* Melotte 22 HHJ 374 +03 43 44.09 +25 39 49.5 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 2.38 0.90 Moraux2003_72_L07_32 GCS9 Cl* Melotte 22 MBSC 72 +03 43 44.97 +23 03 21.1 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 2.21 0.84 SK618_HHJ321_DH286 GCS9 Cl* Melotte 22 SK 618 +03 43 46.29 +23 58 37.9 0 19.124 17.930 16.876 16.248 15.628 0.057 20.13 2.62 -45.01 2.62 6.23 0.69 int-pl-IZ-20;IPLJ0343462+235838_Y GCS9 Cl* Melotte 22 IPL 20 +03 43 47.08 +26 04 35.3 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 9.19 0.93 SK607_HHJ311_DH287 GCS9 Cl* Melotte 22 SK 607 +03 43 47.71 +28 15 59.3 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.74 0.90 DH2004_289 GCS9 Cl* Melotte 22 DH 289 +03 43 48.45 +25 02 36.7 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.33 0.93 HCG125_HHJ298_DH292 GCS9 Cl* Melotte 22 HCG 125 +03 43 50.56 +23 05 47.7 0 16.501 15.963 15.297 14.681 14.285 0.009 24.32 2.28 -51.66 2.28 5.95 0.02 int-pl-IZ-53_2MASSJ0343505+230548_Y_L07_241 GCS9 Cl* Melotte 22 IPL 53 +03 43 51.38 +21 09 03.1 0 14.541 14.138 13.586 13.020 12.740 12.734 30.15 2.65 -36.85 2.65 1.50 0.01 DH295 GCS9 Cl* Melotte 22 DH 295 +03 43 51.76 +24 14 15.9 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 1.45 0.90 HCG128_BPL65_DH296 GCS9 Cl* Melotte 22 HCG 128 +03 43 52.15 +24 50 29.5 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 4.37 0.60 HII191_HCG127_SK606_DH297 GCS9 HII191 +03 43 52.79 +25 29 30.3 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 4.71 0.83 HHJ218_DH298Moraux2003_37 GCS9 Cl* Melotte 22 HHJ 218 +03 43 53.55 +24 31 11.4 0 18.970 17.709 16.649 15.942 15.298 13.04 3.29 -53.30 3.29 6.40 0.02 BPL66_Roque4 GCS9 Cl* Melotte 22 BPL 66 +03 43 53.88 +25 28 30.0 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 3.97 0.76 HCG126_SK600_T69_HHJ391_DH299 GCS9 Cl* Melotte 22 HCG 126 +03 43 56.00 +25 36 25.2 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 11.61 0.36 PLZJ50_L07_A1_18_L07_249 GCS9 Cl* Melotte 22 PlZJ 50 +03 43 56.70 +25 15 43.9 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.51 0.86 SK596_DH300 GCS9 Cl* Melotte 22 SK 596 +03 43 56.71 +24 59 36.5 0 13.167 12.845 12.319 11.756 11.484 27.73 2.97 -45.73 2.97 1.35 0.13 HCG129_SK598_HHJ382_DH301 GCS9 Cl* Melotte 22 HCG 129 +03 43 57.00 +23 57 05.7 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 1.37 0.90 HCG135_HHJ229_DH302 GCS9 Cl* Melotte 22 HCG 135 +03 43 57.29 +24 13 20.3 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 1.14 0.72 HCG133_SK601_BPL67_DH303 GCS9 Cl* Melotte 22 HCG 133 +03 44 02.29 +25 03 53.5 0 12.928 12.605 12.098 11.550 11.827 17.46 2.97 -49.03 2.97 1.22 0.26 HCG134_SK591_T150_DH306 GCS9 Cl* Melotte 22 HCG 134 +03 44 02.50 +21 13 15.8 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.41 0.85 DH307 GCS9 Cl* Melotte 22 DH 307 +03 44 05.25 +22 50 13.5 0 20.066 18.839 17.666 16.895 16.134 0.147 19.70 3.13 -45.22 3.13 3.08 0.57 int-pl-IZ-57;IPLJ0344052+225014_N GCS9 Cl* Melotte 22 IPL 57 +03 44 05.63 +23 03 42.4 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 1.94 0.90 L07_180 GCS9 +03 44 08.81 +23 04 47.5 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 1.05 0.39 HCG141_SK590_T71_DH312 GCS9 Cl* Melotte 22 HCG 141 +03 44 09.32 +23 08 46.8 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 4.90 0.91 HHJ186_DH313_L07_172 GCS9 Cl* Melotte 22 HHJ 186 +03 44 09.61 +24 35 22.1 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.60 0.89 SK586_DH314 GCS9 Cl* Melotte 22 SK 586 +03 44 09.92 +24 16 03.9 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 0.27 0.57 HCG140_DH315 GCS9 Cl* Melotte 22 HCG 140 +03 44 10.76 +25 37 38.2 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 6.39 0.62 HHJ235_DH316_Moraux2003_36 GCS9 Cl* Melotte 22 HHJ 235 +03 44 11.28 +24 52 34.2 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 3.05 0.09 HHJ59 GCS9 Cl* Melotte 22 HHJ 59 +03 44 11.90 +22 53 36.9 0 15.170 14.818 14.272 13.668 13.416 13.423 12.90 2.26 -60.48 2.26 6.74 0.00 L07_178 GCS9 +03 44 11.92 +19 18 19.1 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 2.20 0.77 DH318 GCS9 Cl* Melotte 22 DH 318 +03 44 12.14 +23 52 37.3 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 1.87 0.87 HHJ217_DH319 GCS9 Cl* Melotte 22 HHJ 217 +03 44 13.97 +25 32 15.3 0 13.648 13.147 12.531 11.991 11.643 11.691 -5.25 2.23 -36.15 2.23 5.79 0.00 HCG138_SK579_DH320_Moraux2003_25 GCS9 Cl* Melotte 22 HCG 138 +03 44 16.46 +23 37 04.0 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 1.22 0.93 HCG144_SK580_HHJ301_DH322 GCS9 Cl* Melotte 22 HCG 144 +03 44 17.76 +24 26 46.7 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.14 0.88 HCG145_SK576_HHJ352_DH323 GCS9 Cl* Melotte 22 HCG 145 +03 44 19.06 +24 35 18.2 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.54 0.90 HCG146_T18B_HHJ228_DH324 GCS9 Cl* Melotte 22 HCG 146 +03 44 20.66 +24 15 10.7 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 0.51 0.59 HHJ57 GCS9 Cl* Melotte 22 HHJ 57 +03 44 20.87 +23 33 39.8 0 13.825 13.404 12.863 12.282 11.993 3.71 2.30 -36.55 2.30 6.76 0.00 HCG155_SK575_HHJ300_DH327 GCS9 Cl* Melotte 22 HCG 155 +03 44 22.14 +23 10 54.8 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 1.84 0.88 DH329_L07_173 GCS9 Cl* Melotte 22 DH 329 +03 44 22.45 +23 39 01.3 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 3.20 0.61 Roque5_L07_A1_19_L07_261 GCS9 Cl* Melotte 22 Roque 5 +03 44 23.24 +25 38 44.9 0 16.319 15.457 14.698 14.154 13.731 13.721 18.31 2.25 -50.40 2.25 6.34 0.20 BRB4_PLZJ29_L07_A1_78 GCS9 Cl* Melotte 22 BRB 4 +03 44 23.40 +25 21 29.9 0 13.413 12.983 12.404 11.920 11.628 11.662 8.92 2.23 -42.15 2.23 9.13 0.15 HCG143_SK568_T19B_HHJ348_DH331 GCS9 Cl* Melotte 22 HCG 143 +03 44 24.00 +21 24 20.8 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 3.40 0.89 DH332 GCS9 Cl* Melotte 22 DH 332 +03 44 24.68 +24 51 53.2 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 1.21 0.86 HCG148_SK569_HHJ307_DH333 GCS9 Cl* Melotte 22 HCG 148 +03 44 24.82 +24 46 06.0 0 12.688 12.372 11.878 11.507 11.202 17.32 2.97 -50.88 2.97 2.49 0.07 HCG149_SK570_T42B_DH334 GCS9 Cl* Melotte 22 HCG 149 +03 44 25.08 +25 34 03.9 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 8.95 0.88 HHJ100_Moraux2003_75_L07_31 GCS9 Cl* Melotte 22 HHJ 100 +03 44 25.51 +22 27 49.8 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 1.49 0.35 DH335_int-pl-IZ-72;2MASSJ0344255+222749_Y GCS9 Cl* Melotte 22 DH 335 +03 44 25.58 +22 40 07.9 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 34.57 0.28 L07_A1_21_L07_246 GCS9 +03 44 25.60 +24 40 52.6 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 1.86 0.62 HCG150_SK567_B179_HHJ364_DH336 GCS9 Cl* Melotte 22 HCG 150 +03 44 26.54 +24 29 11.3 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 1.10 0.91 HHJ243_DH338 GCS9 Cl* Melotte 22 HHJ 243 +03 44 26.89 +24 24 31.5 0 13.155 12.822 12.306 11.723 11.468 3.74 2.97 -30.56 2.97 12.86 0.00 HCG156_HHJ400_DH339 GCS9 Cl* Melotte 22 HCG 156 +03 44 27.50 +24 14 17.0 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.91 0.92 HHJ190_DH341_L07_97 GCS9 Cl* Melotte 22 HHJ 190 +03 44 27.92 +23 59 59.6 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 2.29 0.89 DH342_L07_113 GCS9 Cl* Melotte 22 DH 342 +03 44 30.08 +25 35 46.8 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 7.54 0.81 HCG152 GCS9 Cl* Melotte 22 HCG 152 +03 44 31.04 +22 15 14.9 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2.16 0.26 SK571_DH344 GCS9 Cl* Melotte 22 SK 571 +03 44 31.72 +23 35 26.0 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 1.12 0.94 SK564_HHJ265_DH345 GCS9 Cl* Melotte 22 SK 564 +03 44 32.17 +25 08 12.3 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 7.50 0.90 HHJ68_Moraux2003_85_L07_51 GCS9 Cl* Melotte 22 HHJ 68 +03 44 32.33 +25 25 17.9 0 16.994 16.209 15.450 14.883 14.476 14.883 14.24 2.27 -43.51 2.27 5.78 0.64 CFHT-Pl-10_M6.5_PLZJ60_L07_A1_22_L07_250 GCS9 Cl* Melotte 22 CFHT 10 +03 44 33.08 +25 45 09.5 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.96 0.71 HCG157_DH346_Moraux2003_40 GCS9 Cl* Melotte 22 HCG 157 +03 44 33.49 +26 14 53.1 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.25 0.93 HHJ334_DH347 GCS9 Cl* Melotte 22 HHJ 334 +03 44 34.30 +23 51 24.6 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 3.77 0.74 L07_A1_23 GCS9 +03 44 35.16 +25 13 42.8 1 17.656 16.584 15.662 14.985 14.448 14.985 19.33 2.26 -44.97 2.26 16.37 0.68 CFHT-Pl-16_M9.3_L07_A1_24_L07_251 GCS9 Cl* Melotte 22 CFHT 16 +03 44 35.90 +23 34 41.9 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 1.71 0.72 HHJ5_L07_A1_25_L07_236 GCS9 Cl* Melotte 22 HHJ 5 +03 44 35.92 +22 50 42.9 0 14.238 13.940 13.439 12.803 12.544 12.552 34.09 2.24 -47.63 2.24 0.63 0.00 L07_197 GCS9 +03 44 36.28 +23 30 10.9 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 7.84 0.93 HCG161_SK559_A23_HHJ344_DH348 GCS9 Cl* Melotte 22 HCG 161 +03 44 37.78 +22 55 15.5 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.64 0.88 HCG164 GCS9 Cl* Melotte 22 HCG 164 +03 44 38.95 +23 02 25.3 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 2.32 0.92 HHJ189_DH351 GCS9 Cl* Melotte 22 HHJ 189 +03 44 40.31 +25 19 24.6 0 15.831 15.228 14.599 14.042 13.700 13.703 -10.25 2.24 -47.03 2.24 7.09 0.00 L07_45 GCS9 +03 44 46.14 +24 23 02.8 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 6.36 0.86 HHJ24_L07_99 GCS9 Cl* Melotte 22 HHJ 24 +03 44 47.31 +19 55 41.4 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.63 0.85 DH354 GCS9 Cl* Melotte 22 DH 354 +03 44 47.33 +24 00 37.7 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 4.41 0.94 HHJ276_HCG167_DH355_L07_9 GCS9 Cl* Melotte 22 HHJ 276 +03 44 47.84 +24 12 52.5 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 2.19 0.94 HCG166_HHJ239_DH357_L07_96 GCS9 Cl* Melotte 22 HCG 166 +03 44 51.51 +25 05 16.5 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 1.69 0.91 L07_49 GCS9 +03 44 53.12 +23 34 22.8 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 3.81 0.67 L07_A1_26_L07_235 GCS9 +03 44 53.21 +24 01 06.5 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 3.95 0.80 DH2004_359 GCS9 Cl* Melotte 22 DH 359 +03 44 53.53 +25 36 19.2 0 16.354 15.837 15.249 14.663 14.358 14.353 25.93 2.25 -40.81 2.25 4.48 0.14 PLZJ56_None GCS9 Cl* Melotte 22 PlZJ 56 +03 44 56.01 +23 55 53.4 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 1.00 0.91 HCG171_HHJ304 GCS9 Cl* Melotte 22 HCG 171 +03 44 56.69 +23 36 23.5 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 8.34 0.85 HHJ269_DH361 GCS9 Cl* Melotte 22 HHJ 269 +03 44 58.02 +23 24 31.0 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 2.38 0.79 HHJ223_DH362 GCS9 Cl* Melotte 22 HHJ 223 +03 44 58.59 +23 55 40.9 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 2.02 0.87 HHJ274_DH363 GCS9 Cl* Melotte 22 HHJ 274 +03 44 59.48 +23 21 18.1 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 1.03 0.77 HHJ54_DH365_L07_156 GCS9 Cl* Melotte 22 HHJ 54 +03 45 01.14 +24 46 40.9 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 3.65 0.71 HCG172_SK538_HHJ216_DH366_L07_74 GCS9 Cl* Melotte 22 HCG 172 +03 45 01.21 +25 21 05.6 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 5.45 0.89 DH367_Moraux2003_74_L07_46 GCS9 Cl* Melotte 22 DH 367 +03 45 02.88 +25 05 19.6 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 1.84 0.75 HHJ139_DH368_Moraux2003_65_L07_50 GCS9 Cl* Melotte 22 HHJ 139 +03 45 03.18 +23 06 58.4 0 16.937 15.492 14.946 14.495 0.012 20.81 2.32 -40.36 2.32 5.52 0.62 int-pl-IZ-60;2MASSJ0345031+230658_Y GCS9 Cl* Melotte 22 IPL 60 +03 45 04.41 +24 15 16.6 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 0.80 0.45 L07_A1_27 GCS9 +03 45 04.99 +23 46 06.4 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 2.75 0.91 HHJ113_DH372 GCS9 Cl* Melotte 22 HHJ 113 +03 45 05.32 +25 29 10.9 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 4.32 0.92 SK534_HHJ381_DH373 GCS9 Cl* Melotte 22 SK 534 +03 45 06.25 +28 42 19.1 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.12 0.85 DH374 GCS9 Cl* Melotte 22 DH 374 +03 45 06.56 +24 40 42.7 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 6.44 0.71 HHJ48_L07_73 GCS9 Cl* Melotte 22 HHJ 48 +03 45 06.79 +23 36 51.4 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 3.68 0.92 HCG176_HHJ136 GCS9 Cl* Melotte 22 HCG 176 +03 45 08.41 +23 25 00.9 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 3.78 0.79 L07_157 GCS9 +03 45 08.69 +22 38 30.3 0 14.314 13.895 13.322 12.787 12.483 12.496 27.40 2.51 -36.47 2.51 19.46 0.06 HHJ204_BPL70_DH376_L07_194 GCS9 Cl* Melotte 22 HHJ 204 +03 45 08.69 +24 24 09.3 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 1.73 0.74 L07_A1_28 GCS9 +03 45 09.04 +25 22 29.7 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 3.96 0.86 HHJ81_L07_47 GCS9 Cl* Melotte 22 HHJ 81 +03 45 09.04 +25 32 49.0 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 1.78 0.94 HHJ164_DH377_Moraux2003_61 GCS9 Cl* Melotte 22 HHJ 164 +03 45 09.46 +23 58 44.7 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 13.16 0.72 L07_A1_29 GCS9 +03 45 10.82 +23 02 58.0 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 4.56 0.92 HCG186_SK535 GCS9 Cl* Melotte 22 HCG 186 +03 45 12.16 +23 21 52.9 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 5.28 0.93 HCG185_SK532_HHJ255_DH379 GCS9 Cl* Melotte 22 HCG 185 +03 45 12.44 +22 41 50.8 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 1.91 0.87 BPL71_SK533_HHJ254_DH380_L07_196 GCS9 Cl* Melotte 22 BPL 71 +03 45 12.62 +23 53 45.0 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 3.02 0.71 HHJ14_PPL7_L07_133 GCS9 Cl* Melotte 22 HHJ 14 +03 45 13.14 +24 15 23.6 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 1.18 0.86 HCG180_HHJ118_DH381_L07_98 GCS9 Cl* Melotte 22 HCG 180 +03 45 14.21 +25 05 19.4 0 12.232 11.987 11.572 11.411 10.770 11.068 -49.24 2.21 -120.03 2.21 2.79 0.00 HII566_HCG174 GCS9 HII566 +03 45 15.82 +25 06 36.5 0 12.587 12.238 11.785 11.593 10.986 11.109 16.46 2.21 -62.54 2.21 1.44 0.00 SK526_HHJ425 GCS9 Cl* Melotte 22 SK 526 +03 45 16.13 +24 07 16.0 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 1.79 0.91 HCG183_HHJ394_DH383 GCS9 Cl* Melotte 22 HCG 183 +03 45 16.42 +23 34 01.6 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 1.55 0.89 DH384 GCS9 Cl* Melotte 22 DH 384 +03 45 16.99 +25 15 47.5 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 2.50 0.89 HCG178_SK525_A28_HHJ410_DH386 GCS9 Cl* Melotte 22 HCG 178 +03 45 18.15 +25 05 58.1 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 2.36 0.74 HII590_HCG179_SK524_T45_DH387 GCS9 HII590 +03 45 21.12 +21 46 17.6 0 16.257 15.741 15.178 14.532 14.239 14.299 8.25 2.54 -32.01 2.54 32.48 0.00 L07_PM_NM_49 GCS9 +03 45 21.91 +26 28 41.9 0 14.370 14.041 13.523 12.837 12.615 12.636 32.97 2.94 -40.02 2.94 0.85 0.00 DH389 GCS9 Cl* Melotte 22 DH 389 +03 45 22.14 +21 52 40.0 0 16.265 15.746 15.102 14.554 14.203 14.180 -1.65 2.29 -61.68 2.29 5.14 0.00 L07_PM_NM_50 GCS9 +03 45 24.70 +24 38 46.4 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 1.24 0.89 L07_72 GCS9 +03 45 24.79 +24 20 45.3 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 8.47 0.85 DH392 GCS9 Cl* Melotte 22 DH 392 +03 45 26.56 +22 31 32.0 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 5.52 0.67 HHJ267_BPL73_DH393_L07_199 GCS9 Cl* Melotte 22 HHJ 267 +03 45 26.99 +24 13 26.4 0 15.192 14.757 14.205 13.605 13.284 13.278 23.59 2.22 -49.95 2.22 2.92 0.17 HHJ99 GCS9 Cl* Melotte 22 HHJ 99 +03 45 27.52 +23 37 56.9 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 1.56 0.90 HHJ106_L07_147 GCS9 Cl* Melotte 22 HHJ 106 +03 45 28.90 +27 28 07.2 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.75 0.88 DH394 GCS9 Cl* Melotte 22 DH 394 +03 45 30.23 +24 18 45.3 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 1.47 0.77 HII673 GCS9 HII673 +03 45 31.25 +25 46 33.3 0 14.192 13.836 13.341 12.732 12.466 12.457 29.01 2.23 -50.04 2.23 1.37 0.01 HHJ293 GCS9 Cl* Melotte 22 HHJ 293 +03 45 31.37 +24 52 47.4 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 7.98 0.66 IPMBD29_L07_A1_30 GCS9 Cl* Melotte 22 IPMBD 29 +03 45 35.69 +24 24 34.1 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 51.10 0.74 L07_A1_31 GCS9 +03 45 36.72 +24 39 06.5 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 19.04 0.85 HCG194_HHJ380_DH397 GCS9 Cl* Melotte 22 HCG 194 +03 45 37.76 +23 43 50.1 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2.10 0.59 L07_A1_32_L07_238 GCS9 +03 45 37.79 +24 20 08.1 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 11.15 0.93 HII717_HD23387_Tr215 GCS9 HII717 +03 45 38.99 +23 57 00.9 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 13.30 0.69 L07_130 GCS9 +03 45 39.04 +25 13 27.6 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 5.94 0.82 HCG196_SK510 GCS9 Cl* Melotte 22 HCG 196 +03 45 39.12 +22 04 23.1 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 3.36 0.67 BPL75_L07_210 GCS9 Cl* Melotte 22 BPL 75 +03 45 39.29 +24 08 20.4 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 1.38 0.93 HHJ130_DH398_L07_117 GCS9 Cl* Melotte 22 HHJ 130 +03 45 40.77 +28 32 05.8 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.16 0.75 DH399 GCS9 Cl* Melotte 22 DH 399 +03 45 41.27 +23 54 09.7 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 3.49 0.69 Roque15_L07_A1_33_L07_234 GCS9 Cl* Melotte 22 Roque 15 +03 45 42.33 +24 04 11.1 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 11.53 0.70 L07_A1_34_L07_227 GCS9 +03 45 43.18 +26 02 26.6 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 6.76 0.94 HHJ264_DH401 GCS9 Cl* Melotte 22 HHJ 264 +03 45 44.08 +24 04 26.6 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 10.09 0.78 HII762_Tr228a_HCG200 GCS9 HII762 +03 45 45.41 +22 33 21.5 0 15.557 15.172 14.632 14.051 13.736 13.741 -0.07 2.27 -31.02 2.27 8.08 0.00 L07_200 GCS9 +03 45 46.48 +23 47 43.1 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 2.51 0.89 HHJ421 GCS9 Cl* Melotte 22 HHJ 421 +03 45 46.90 +23 53 00.3 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 3.12 0.88 L07_129 GCS9 +03 45 46.94 +21 44 49.0 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 1.97 0.54 HHJ128 GCS9 Cl* Melotte 22 HHJ 128 +03 45 49.26 +25 24 46.1 0 16.825 16.314 15.704 15.073 14.758 14.754 26.80 2.29 -31.90 2.29 3.20 0.00 DH403 GCS9 Cl* Melotte 22 DH 403 +03 45 49.37 +24 25 07.1 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 5.99 0.93 BPL77_L07_7 GCS9 Cl* Melotte 22 BPL 77 +03 45 49.94 +23 19 44.7 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 2.39 0.66 L07_155 GCS9 +03 45 50.42 +22 36 05.6 0 17.653 16.839 16.051 15.524 15.034 0.021 14.53 2.39 -32.89 2.39 8.07 0.05 int-pl-IZ-44;2MASSJ0345504+223606_BPL78_Y_L07_A1_35_L07_247 GCS9 Cl* Melotte 22 IPL 44 +03 45 50.66 +24 09 03.5 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 1.10 0.65 BPL79_Roque13_L07_A1_36 GCS9 Cl* Melotte 22 BPL 79 +03 45 51.09 +24 26 10.9 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 1.18 0.84 HHJ25_L07_86 GCS9 Cl* Melotte 22 HHJ 25 +03 45 51.33 +24 17 44.3 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 8.20 0.75 L07_92 GCS9 +03 45 51.64 +24 02 19.7 0 12.518 12.529 11.248 11.845 10.016 30.83 2.22 -25.84 2.22 9.31 0.00 HII804_HD23409_Tr235 GCS9 HII804 +03 45 51.95 +25 10 01.7 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 7.90 0.92 HHJ166_BPL80_DH404_Moraux2003_54_L07_54 GCS9 Cl* Melotte 22 HHJ 166 +03 45 52.60 +25 54 59.8 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 1.59 0.92 SK499_DH405 GCS9 Cl* Melotte 22 SK 499 +03 45 52.76 +23 27 54.0 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 4.63 0.49 HCG202_SK504_HHJ227_DH407 GCS9 Cl* Melotte 22 HCG 202 +03 45 54.96 +23 33 57.8 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 5.98 0.72 L07_A1_38_L07_237 GCS9 +03 45 54.99 +24 13 26.0 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.61 0.94 BPL82 GCS9 Cl* Melotte 22 BPL 82 +03 45 56.97 +23 01 29.0 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 1.23 0.90 HCG205_HHJ199_DH410 GCS9 Cl* Melotte 22 HCG 205 +03 45 57.09 +24 21 00.9 0 15.058 14.437 13.779 13.215 12.854 12.842 8.36 2.22 -35.88 2.22 11.01 0.01 L07_94 GCS9 +03 45 57.28 +25 11 12.7 0 13.246 12.837 12.272 12.049 11.431 11.482 11.26 2.21 -43.67 2.21 7.13 0.55 SK497_HHJ379_BPL83_DH411_Moraux2003_7 GCS9 Cl* Melotte 22 SK 497 +03 45 57.71 +24 03 04.9 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 4.80 0.59 HHJ27_L07_116 GCS9 Cl* Melotte 22 HHJ 27 +03 45 57.78 +26 18 44.4 0 13.541 13.217 12.726 12.101 11.875 11.872 36.22 2.94 -32.09 2.94 0.34 0.00 Moraux2003_14 GCS9 Cl* Melotte 22 MBSC 14 +03 45 57.91 +24 08 40.9 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 2.70 0.90 DH412_L07_118 GCS9 Cl* Melotte 22 DH 412 +03 45 58.80 +26 20 01.7 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.40 0.81 HHJ67_DH413_Moraux2003_86 GCS9 Cl* Melotte 22 HHJ 67 +03 45 59.20 +23 48 46.8 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 4.75 0.92 HHJ127 GCS9 Cl* Melotte 22 HHJ 127 +03 46 00.93 +22 12 29.4 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 9.09 0.68 HHJ11_BPL84 GCS9 Cl* Melotte 22 HHJ 11 +03 46 02.26 +25 36 40.4 0 12.980 12.632 12.114 11.635 11.296 11.366 31.82 2.23 -37.17 2.23 6.80 0.00 SK491_HHJ399 GCS9 Cl* Melotte 22 SK 491 +03 46 02.53 +22 28 27.5 0 16.837 16.315 15.700 15.098 14.791 0.012 7.00 2.33 -22.57 2.33 3.08 0.00 int-pl-IZ-64;2MASSJ0346025+222827_N GCS9 Cl* Melotte 22 IPL 64 +03 46 02.96 +24 40 55.6 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 7.46 0.83 BPL85_DH414 GCS9 Cl* Melotte 22 BPL 85 +03 46 03.45 +24 20 57.0 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 3.54 0.46 HCG206_BPL86_DH415_L07_93 GCS9 Cl* Melotte 22 HCG 206 +03 46 03.67 +25 52 28.8 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 3.39 0.94 HHJ182_DH416_L07_26 GCS9 Cl* Melotte 22 HHJ 182 +03 46 03.96 +24 23 08.9 0 15.655 15.066 14.448 13.848 13.511 13.513 31.07 2.23 0.78 2.23 0.19 0.00 BPL87 GCS9 Cl* Melotte 22 BPL 87 +03 46 04.30 +23 55 40.6 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 3.63 0.84 HHJ363 GCS9 Cl* Melotte 22 HHJ 363 +03 46 04.57 +24 09 55.8 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 2.54 0.84 BPL88 GCS9 Cl* Melotte 22 BPL 88 +03 46 05.64 +24 36 44.3 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 3.04 0.93 SK488 GCS9 Cl* Melotte 22 SK 488 +03 46 05.69 +24 36 49.7 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 4.06 0.87 L07_88 GCS9 +03 46 06.05 +23 58 19.1 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 21.77 0.73 L07_114 GCS9 +03 46 06.21 +25 06 45.8 0 14.228 14.006 13.550 12.936 12.838 12.837 12.78 2.21 -34.39 2.21 1.09 0.08 L07_53 GCS9 +03 46 06.46 +25 25 11.5 0 14.674 14.289 13.759 13.117 12.837 12.862 18.32 2.23 -26.35 2.23 1.79 0.00 HHJ194_Moraux2003_59 GCS9 Cl* Melotte 22 HHJ 194 +03 46 06.52 +23 50 20.2 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 3.27 0.81 HHJ435 GCS9 Cl* Melotte 22 HHJ 435 +03 46 06.67 +22 23 33.7 0 12.888 12.512 12.011 11.499 11.168 11.310 42.71 2.26 -18.94 2.26 1.18 0.00 BPL89 GCS9 Cl* Melotte 22 BPL 89 +03 46 07.51 +24 22 27.6 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.96 0.89 HII890_HCG210 GCS9 HII890 +03 46 07.56 +23 44 42.6 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 1.77 0.70 L07_239 GCS9 +03 46 08.70 +24 40 33.1 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 5.02 0.93 HCG209_BPL90_DH422 GCS9 Cl* Melotte 22 HCG 209 +03 46 09.88 +21 05 52.4 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 5.12 0.46 DH425 GCS9 Cl* Melotte 22 DH 425 +03 46 10.10 +26 00 09.0 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.60 0.82 HHJ80_L07_17 GCS9 Cl* Melotte 22 HHJ 80 +03 46 10.23 +21 52 55.8 0 16.174 15.346 14.578 13.994 13.570 13.564 43.56 2.27 -44.28 2.27 1.50 0.00 L07_A1_42 GCS9 +03 46 12.67 +23 35 13.6 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 4.56 0.94 L07_146 GCS9 +03 46 12.88 +24 03 15.6 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 6.72 0.81 HII930_DH429 GCS9 Cl* Melotte 22 DH 429 +03 46 13.00 +27 02 11.7 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 1.60 0.82 DH430 GCS9 Cl* Melotte 22 DH 430 +03 46 14.06 +23 21 56.4 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 7.96 0.68 L07_A1_43 GCS9 +03 46 15.84 +22 02 41.0 0 13.656 13.264 12.746 12.128 11.854 11.872 24.87 2.26 -14.34 2.26 2.45 0.00 BPL93 GCS9 Cl* Melotte 22 BPL 93 +03 46 16.85 +24 26 27.3 0 14.565 14.151 13.599 13.055 12.761 12.781 35.34 2.21 -3.40 2.21 3.16 0.00 BPL94 GCS9 Cl* Melotte 22 BPL 94 +03 46 17.94 +24 41 09.3 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 7.95 0.93 HHJ367_BPL95_DH433 GCS9 Cl* Melotte 22 HHJ 367 +03 46 18.54 +23 59 02.5 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 36.19 0.88 L07_115 GCS9 +03 46 19.41 +23 00 55.7 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 3.01 0.80 HHJ47_BPL96_DH435 GCS9 Cl* Melotte 22 HHJ 47 +03 46 19.43 +26 02 35.5 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 4.18 0.74 SK474_DH436 GCS9 Cl* Melotte 22 SK 474 +03 46 19.86 +24 59 01.3 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 2.73 0.88 HHJ303_BPL97_DH437_Moraux2003_27 GCS9 Cl* Melotte 22 HHJ 303 +03 46 20.65 +23 53 31.7 0 15.607 15.199 14.655 13.992 13.738 13.729 10.57 2.23 -42.96 2.23 9.25 0.44 L07_125 GCS9 +03 46 21.36 +24 33 52.2 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 3.50 0.83 HHJ105_BPL98_DH439 GCS9 Cl* Melotte 22 HHJ 105 +03 46 21.40 +23 05 08.9 0 15.684 15.157 14.548 14.032 13.690 13.664 11.08 2.24 -47.42 2.24 27.35 0.29 HHJ33 GCS9 Cl* Melotte 22 HHJ 33 +03 46 21.62 +23 04 00.9 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 22.02 0.93 DH440 GCS9 Cl* Melotte 22 DH 440 +03 46 22.20 +23 52 41.0 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 4.55 0.91 DH441 GCS9 Cl* Melotte 22 DH 441 +03 46 22.25 +23 52 26.6 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 4.78 0.56 L07_A1_44_L07_232 GCS9 +03 46 23.03 +24 36 17.9 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 19.52 0.93 BPL99_DH442 GCS9 Cl* Melotte 22 BPL 99 +03 46 23.12 +24 20 36.0 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 3.71 0.66 BPL100_Roque9_L07_A1_45 GCS9 Cl* Melotte 22 BPL 100 +03 46 23.47 +24 01 51.2 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 5.91 0.94 HCG218_HHJ195_DH443 GCS9 Cl* Melotte 22 HCG 218 +03 46 23.72 +22 50 16.4 0 17.310 15.710 15.189 14.722 0.015 20.26 2.35 -45.42 2.35 5.66 0.64 int-pl-IZ-43;2MASSJ0346237+225016_Y GCS9 Cl* Melotte 22 IPL 43 +03 46 23.74 +26 34 23.0 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.60 0.73 HHJ175_DH444_Moraux2003_62 GCS9 Cl* Melotte 22 HHJ 175 +03 46 24.12 +24 30 12.7 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 3.56 0.90 BPL101 GCS9 Cl* Melotte 22 BPL 101 +03 46 24.64 +24 28 46.2 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 2.85 0.93 HHJ249_BPL102_DH445_L07_87 GCS9 Cl* Melotte 22 HHJ 249 +03 46 25.18 +21 26 17.4 0 13.441 12.930 12.360 11.842 11.508 11.529 3.09 2.65 -44.47 2.65 1.82 0.00 DH446 GCS9 Cl* Melotte 22 DH 446 +03 46 25.39 +24 09 36.2 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.43 0.64 HCG219_A2_HHJ429 GCS9 Cl* Melotte 22 HCG 219 +03 46 26.09 +24 05 09.5 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 3.37 0.57 IPMBD25_L07_A1_46_L07_230 GCS9 Cl* Melotte 22 IPMBD 25 +03 46 27.01 +24 27 13.9 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 2.66 0.82 HHJ326_BPL103_DH447 GCS9 Cl* Melotte 22 HHJ 326 +03 46 27.10 +21 48 22.6 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 1.27 0.65 L07_A1_47 GCS9 +03 46 27.69 +23 48 45.5 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 1.69 0.94 HHJ161 GCS9 Cl* Melotte 22 HHJ 161 +03 46 28.63 +24 45 32.1 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 8.09 0.81 HII1029_HCG222_SK472_B212_DH451 GCS9 HII1029 +03 46 29.73 +21 02 16.7 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.73 0.92 DH452 GCS9 Cl* Melotte 22 DH 452 +03 46 30.96 +23 00 15.1 0 14.562 14.242 13.760 13.105 12.885 12.870 29.31 2.24 -51.76 2.24 0.62 0.00 L07_181 GCS9 +03 46 31.02 +23 01 34.6 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 5.54 0.83 HHJ30_DH453 GCS9 Cl* Melotte 22 HHJ 30 +03 46 31.32 +22 18 19.6 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 4.04 0.92 HHJ160_BPL105_L07_204 GCS9 Cl* Melotte 22 HHJ 160 +03 46 32.13 +24 23 14.6 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 3.18 0.57 L07_A1_48 GCS9 +03 46 32.69 +22 20 17.7 0 14.509 14.126 13.585 12.902 12.626 12.625 30.13 2.26 -27.71 2.26 1.50 0.00 SK471 GCS9 Cl* Melotte 22 SK 471 +03 46 32.79 +19 17 30.2 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 1.46 0.90 DH455 GCS9 Cl* Melotte 22 DH 455 +03 46 34.16 +23 25 12.5 0 16.303 15.778 15.088 14.398 14.055 14.041 11.49 2.25 -25.44 2.25 5.43 0.00 HHJ9_L07_PM_NM_20 GCS9 Cl* Melotte 22 HHJ 9 +03 46 34.25 +23 50 03.6 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 3.43 0.66 L07_A1_49_L07_260 GCS9 +03 46 34.99 +23 31 14.4 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 4.06 0.70 L07_A1_50 GCS9 +03 46 35.36 +23 57 07.4 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 11.08 0.75 L07_A1_51_L07_233 GCS9 +03 46 35.54 +24 01 35.4 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.77 0.89 HHJ140_DH458 GCS9 Cl* Melotte 22 HHJ 140 +03 46 36.07 +23 04 17.2 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 5.66 0.94 SK465_HHJ282_DH459 GCS9 Cl* Melotte 22 SK 465 +03 46 39.33 +24 06 11.3 0 12.740 12.808 11.163 12.306 9.936 10.900 30.68 2.22 -50.62 2.22 33.59 0.00 HII1122_HD23511_Tr327 GCS9 HII1122 +03 46 40.27 +25 43 53.4 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 10.61 0.91 SK461_HHJ316_DH464_L07_3 GCS9 Cl* Melotte 22 SK 461 +03 46 40.41 +22 50 39.6 0 15.138 14.065 13.514 13.194 13.182 21.74 2.28 -48.62 2.28 4.41 0.50 BPL107 GCS9 Cl* Melotte 22 BPL 107 +03 46 40.60 +22 22 03.5 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 4.92 0.88 L07_206 GCS9 +03 46 43.19 +23 37 21.9 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 6.11 0.86 HHJ104_DH466_L07_138 GCS9 Cl* Melotte 22 HHJ 104 +03 46 43.60 +23 59 42.3 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.97 0.91 DH467 GCS9 Cl* Melotte 22 DH 467 +03 46 44.79 +24 44 58.2 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 4.59 0.90 HHJ75_BPL109_L07_67 GCS9 Cl* Melotte 22 HHJ 75 +03 46 44.88 +23 37 07.3 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 5.80 0.83 L07_137 GCS9 +03 46 45.78 +25 27 30.2 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 2.85 0.88 HCG233_SK458_T25B_HHJ319_Moraux2003_20_DH2004_468 GCS9 Cl* Melotte 22 HCG 233 +03 46 46.48 +23 24 02.8 0 14.716 14.273 13.660 13.004 12.658 12.671 33.44 2.24 -34.98 2.24 7.11 0.00 HHJ159 GCS9 Cl* Melotte 22 HHJ 159 +03 46 47.19 +25 20 53.1 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 8.06 0.66 HHJ208_DH469_Moraux2003_45_L07_39 GCS9 Cl* Melotte 22 HHJ 208 +03 46 48.79 +23 04 07.3 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 3.00 0.90 HCG245_HHJ370_DH470 GCS9 Cl* Melotte 22 HCG 245 +03 46 50.03 +24 00 23.6 0 17.416 16.456 15.617 15.025 14.512 14.503 16.75 2.25 -34.16 2.25 3.06 0.16 L07_A1_53_L07_229 GCS9 +03 46 50.09 +23 31 56.1 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 4.16 0.94 HCG240_HHJ253_DH472_L07_134 GCS9 Cl* Melotte 22 HCG 240 +03 46 50.19 +22 12 42.3 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 3.93 0.87 BPL110_L07_203 GCS9 Cl* Melotte 22 BPL 110 +03 46 51.83 +23 23 09.4 0 19.654 18.417 17.182 16.429 15.651 15.690 19.04 2.55 -23.47 2.55 10.80 0.00 L07_A1_54 GCS9 +03 46 52.29 +21 47 43.0 0 16.720 16.080 15.402 14.797 14.405 14.418 24.38 2.30 -20.65 2.30 1.45 0.00 L07_PM_NM_24 GCS9 +03 46 52.61 +23 38 43.0 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 5.33 0.92 HHJ247_DH473 GCS9 Cl* Melotte 22 HHJ 247 +03 46 52.97 +24 15 07.8 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 5.64 0.33 BPL112_L07_A1_55 GCS9 Cl* Melotte 22 BPL 112 +03 46 53.61 +24 17 14.8 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 6.95 0.85 HCG244_SK454_BPL113_DH474 GCS9 Cl* Melotte 22 HCG 244 +03 46 53.96 +24 07 57.1 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 1.08 0.78 15.76 GCS9 Cl* Melotte 22 MHO 8 +03 46 54.03 +25 14 44.8 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 1.73 0.92 HCG241_SK453_B267_BPL114_Moraux2003_4_DH475 GCS9 Cl* Melotte 22 HCG 241 +03 46 54.39 +22 45 11.9 0 18.716 16.652 16.025 15.444 0.043 17.39 2.54 -35.13 2.54 10.55 0.43 int-pl-IZ-48;IPLJ0346543+224512_Y GCS9 Cl* Melotte 22 IPL 48 +03 46 55.32 +23 22 49.3 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 6.26 0.89 HHJ95_DH479_L07_163 GCS9 Cl* Melotte 22 HHJ 95 +03 46 55.32 +24 11 16.6 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 3.10 0.93 BPL116_DH478_L07_103 GCS9 Cl* Melotte 22 BPL 116 +03 46 55.36 +25 51 29.4 0 15.808 15.294 14.643 14.056 13.716 13.713 9.44 2.24 -34.52 2.24 8.63 0.01 HHJ31_Moraux2003_97_L07_28 GCS9 Cl* Melotte 22 HHJ 31 +03 46 55.49 +23 11 16.0 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 3.33 0.33 L07_A1_56 GCS9 +03 46 55.78 +23 56 24.1 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 2.24 0.91 HHJ257_DH480 GCS9 Cl* Melotte 22 HHJ 257 +03 46 57.10 +23 15 02.2 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 2.12 0.83 HCG247_SK452_HHJ415_DH481 GCS9 Cl* Melotte 22 HCG 247 +03 46 57.83 +23 12 46.2 0 14.826 14.503 14.022 13.469 13.198 13.219 28.31 2.24 -40.75 2.24 3.54 0.15 L07_175 GCS9 +03 46 58.17 +23 33 38.8 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.71 0.94 HHJ174_DH482_L07_136 GCS9 Cl* Melotte 22 HHJ 174 +03 46 58.26 +24 01 41.3 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 4.06 0.79 L07_121 GCS9 +03 46 59.32 +24 01 42.8 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 2.81 0.85 HHJ299_DH484 GCS9 Cl* Melotte 22 HHJ 299 +03 47 01.85 +24 13 28.1 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 3.78 0.52 BPL122_L07_A1_57 GCS9 Cl* Melotte 22 BPL 122 +03 47 02.35 +23 32 36.0 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 10.63 0.89 DH487_L07_135 GCS9 Cl* Melotte 22 DH 487 +03 47 03.78 +23 36 58.6 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 1.39 0.80 HII1286_HCG251_SK444_DH489 GCS9 HII1286 +03 47 04.41 +24 47 27.3 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2.70 0.49 L07_A1_58 GCS9 +03 47 04.75 +25 22 50.0 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 2.99 0.87 HCG248_SK443_B180_HHJ390_DH491 GCS9 Cl* Melotte 22 HCG 248 +03 47 05.71 +24 40 03.6 0 16.900 16.127 15.416 14.847 14.428 14.447 10.79 2.25 -41.09 2.25 2.09 0.26 BPL124_L07_A1_59 GCS9 Cl* Melotte 22 BPL 124 +03 47 05.79 +23 45 34.7 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 18.37 0.33 L07_A1_60_L07_231 GCS9 +03 47 07.88 +24 23 37.8 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 35.17 0.80 BPL125_DH494_Festin98_003 GCS9 Cl* Melotte 22 BPL 125 +03 47 08.15 +24 18 24.5 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 1.55 0.90 HCG253_SK440_BPL126_DH495 GCS9 Cl* Melotte 22 HCG 253 +03 47 09.42 +24 15 34.7 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 3.08 0.68 HHJ37_BPL128_DH496_L07_105 GCS9 Cl* Melotte 22 HHJ 37 +03 47 09.52 +23 25 56.3 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.90 0.93 DH498 GCS9 Cl* Melotte 22 DH 498 +03 47 10.21 +24 43 35.3 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 12.34 0.90 L07_65 GCS9 +03 47 10.65 +23 58 16.4 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 2.39 0.72 L07_A1_61_L07_228 GCS9 +03 47 11.03 +24 13 51.5 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 1.30 0.90 HCG254_BPL129_L07_104 GCS9 Cl* Melotte 22 HCG 254 +03 47 11.79 +24 13 31.3 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.62 0.73 BPL130_L07_A1_62 GCS9 Cl* Melotte 22 BPL 130 +03 47 11.86 +24 13 53.8 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 1.06 0.80 HHJ92_BPL131_DH499 GCS9 Cl* Melotte 22 HHJ 92 +03 47 13.67 +23 49 53.2 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 5.11 0.89 HCG258_SK437_B363_DH500 GCS9 Cl* Melotte 22 HCG 258 +03 47 15.20 +25 24 19.0 0 17.217 16.524 15.790 15.226 14.834 14.844 17.61 2.31 -19.56 2.31 3.87 0.00 IPMBD26 GCS9 Cl* Melotte 22 IPMBD 26 +03 47 15.29 +25 06 55.3 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 2.31 0.89 SK432_HHJ376_BPL133_DH502_Moraux2003_9 GCS9 Cl* Melotte 22 SK 432 +03 47 15.38 +23 26 05.8 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 2.38 0.92 HHJ203_DH503_L07_161 GCS9 Cl* Melotte 22 HHJ 203 +03 47 15.46 +24 23 31.0 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 85.06 0.58 BPL134_Festin98_002 GCS9 Cl* Melotte 22 BPL 134 +03 47 15.75 +22 21 16.8 0 14.199 13.704 13.140 12.641 12.297 12.323 20.22 2.26 -54.43 2.26 2.70 0.03 HCG267_BPL135_HHJ211_L07_205 GCS9 Cl* Melotte 22 HCG 267 +03 47 16.45 +24 44 50.1 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 12.55 0.93 BPL136_L07_66 GCS9 Cl* Melotte 22 BPL 136 +03 47 17.92 +24 22 31.6 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 6.15 0.68 BPL137_Teide1_M8_L07_A1_63 GCS9 Cl* Melotte 22 BPL 137 +03 47 18.10 +24 45 14.6 0 19.068 18.067 17.095 16.314 15.678 15.676 8.16 2.57 -37.97 2.57 4.23 0.15 L07_A1_64 GCS9 +03 47 19.34 +24 08 20.7 0 13.736 13.765 11.395 12.325 9.950 10.831 21.11 2.22 -61.40 2.22 7.54 0.00 HII1362_HD23607_Tr390 GCS9 HII1362 +03 47 20.43 +22 21 56.3 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 3.34 0.80 L07_207 GCS9 +03 47 20.84 +25 05 12.1 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 2.92 0.77 SK428_HHJ417_DH506 GCS9 Cl* Melotte 22 SK 428 +03 47 20.97 +23 48 11.9 0 13.896 13.945 11.841 12.647 10.307 11.499 2.96 2.22 -46.62 2.22 25.98 0.00 HII1380_HD23632_Tr397 GCS9 HII1380 +03 47 21.94 +26 22 47.2 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 1.49 0.71 HHJ219_DH508_Moraux2003_44 GCS9 Cl* Melotte 22 HHJ 219 +03 47 22.03 +23 21 36.3 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 3.13 0.93 L07_159 GCS9 +03 47 22.38 +24 14 18.8 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 3.72 0.81 BPL139 GCS9 Cl* Melotte 22 BPL 139 +03 47 22.46 +22 31 10.8 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 3.97 0.83 BPL140_L07_201 GCS9 Cl* Melotte 22 BPL 140 +03 47 22.68 +23 44 06.7 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 30.63 0.92 HCG266_DH509 GCS9 Cl* Melotte 22 HCG 266 +03 47 22.76 +23 01 58.0 0 15.929 15.403 14.815 14.282 13.961 13.934 36.81 2.25 -28.10 2.25 3.24 0.00 HHJ23 GCS9 Cl* Melotte 22 HHJ 23 +03 47 22.91 +22 55 19.4 0 12.476 12.365 11.050 11.776 9.922 10.773 30.63 2.23 -35.96 2.23 35.83 0.01 HII1407_HD23610_TrS108 GCS9 HII1407 +03 47 25.11 +24 15 17.2 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 3.29 0.93 HHJ198_BPL143 GCS9 Cl* Melotte 22 HHJ 198 +03 47 25.35 +24 02 56.8 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 3.92 0.88 HHJ427_DH512 GCS9 Cl* Melotte 22 HHJ 427 +03 47 25.80 +25 08 32.8 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 4.52 0.93 HCG263_SK423_T6B_HHJ361_BPL144_DH513_Moraux2003_10 GCS9 Cl* Melotte 22 HCG 263 +03 47 25.90 +25 26 26.4 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 3.55 0.91 Moraux2003_49_L07_38 GCS9 Cl* Melotte 22 MBSC 49 +03 47 26.77 +23 38 02.4 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 4.38 0.93 HCG269_HHJ263_DH514 GCS9 Cl* Melotte 22 HCG 269 +03 47 26.84 +23 40 41.8 0 13.124 13.039 11.380 12.020 10.138 13.738 12.70 2.22 -30.72 2.22 12.76 0.00 HII1425_HD23643_Tr410 GCS9 HII1425 +03 47 27.28 +24 49 16.3 0 14.616 14.190 13.656 12.958 12.729 12.717 31.79 2.21 -32.38 2.21 2.21 0.00 15.07 GCS9 Cl* Melotte 22 MHO 13 +03 47 27.72 +22 09 38.5 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 6.30 0.72 L07_A1_65 GCS9 +03 47 28.12 +23 26 53.4 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 3.65 0.92 SK417_HHJ315_DH515 GCS9 Cl* Melotte 22 SK 417 +03 47 28.41 +26 32 05.5 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.39 0.93 DH516_Moraux2003_33 GCS9 Cl* Melotte 22 DH 516 +03 47 28.43 +24 40 33.0 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 4.76 0.87 BPL145_DH517_L07_69 GCS9 Cl* Melotte 22 BPL 145 +03 47 29.59 +23 52 49.3 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 1.74 0.74 L07_A1_66 GCS9 +03 47 29.93 +23 33 15.0 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 2.95 0.74 HHJ16 GCS9 Cl* Melotte 22 HHJ 16 +03 47 30.59 +26 16 44.5 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.21 0.93 HCG260_HHJ310_DH518_Moraux2003_26 GCS9 Cl* Melotte 22 HCG 260 +03 47 30.60 +24 22 13.8 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 2.33 0.94 HCG273_HHJ408 GCS9 Cl* Melotte 22 HCG 273 +03 47 31.13 +21 10 51.1 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 0.64 0.31 DH519 GCS9 Cl* Melotte 22 DH 519 +03 47 31.37 +25 25 11.0 0 15.908 15.366 14.702 14.048 13.679 13.650 15.53 2.24 -17.06 2.24 1.02 0.00 Moraux2003_104 GCS9 Cl* Melotte 22 MBSC 104 +03 47 31.65 +23 52 19.1 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 2.44 0.82 L07_124 GCS9 +03 47 32.00 +24 10 24.7 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 1.69 0.94 BPL147 GCS9 Cl* Melotte 22 BPL 147 +03 47 33.06 +25 38 18.4 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 6.41 0.90 L07_29 GCS9 +03 47 33.46 +23 41 32.8 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.71 0.88 HCG277_SK413_T105_HHJ424_DH520 GCS9 Cl* Melotte 22 HCG 277 +03 47 34.17 +25 43 05.9 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 17.36 0.86 HHJ238_DH522_L07_30 GCS9 Cl* Melotte 22 HHJ 238 +03 47 34.52 +24 02 23.0 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 2.40 0.72 DH523 GCS9 Cl* Melotte 22 DH 523 +03 47 35.86 +24 52 26.7 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 10.09 0.91 HCG279_HHJ157_BPL149 GCS9 Cl* Melotte 22 HCG 279 +03 47 36.04 +23 28 26.7 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 3.74 0.83 HHJ73_DH526_L07_162 GCS9 Cl* Melotte 22 HHJ 73 +03 47 37.35 +25 20 02.3 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 1.96 0.93 Moraux2003_43 GCS9 Cl* Melotte 22 MBSC 43 +03 47 37.66 +24 24 23.1 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 2.31 0.84 BPL150_L07_76 GCS9 Cl* Melotte 22 BPL 150 +03 47 38.05 +24 49 10.8 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 7.15 0.91 SK409_HHJ360_BPL151_DH528 GCS9 Cl* Melotte 22 SK 409 +03 47 38.37 +24 35 59.7 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 8.01 0.85 L07_80 GCS9 +03 47 39.02 +24 36 22.2 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 12.06 0.59 BPL152_Roque16_CFHT-Pl-11_M6.0_BRB12_CFHT-Pl-11_L07_A1_67_L07_219 GCS9 Cl* Melotte 22 BPL 152 +03 47 39.36 +24 27 31.9 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 4.21 0.94 HCG282_HHJ272_BPL153_DH530 GCS9 Cl* Melotte 22 HCG 282 +03 47 39.80 +23 00 04.4 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 13.32 0.88 HHJ94 GCS9 Cl* Melotte 22 HHJ 94 +03 47 40.96 +21 49 05.0 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 5.36 0.80 L07_213 GCS9 +03 47 42.86 +28 18 59.1 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 0.05 0.45 DH533 GCS9 Cl* Melotte 22 DH 533 +03 47 43.88 +26 13 26.8 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.35 0.86 DH534_Moraux2003_66 GCS9 Cl* Melotte 22 DH 534 +03 47 44.05 +24 03 56.3 0 16.061 15.644 15.077 14.512 14.197 14.197 23.29 2.23 -45.96 2.23 10.22 0.35 HHJ26_DH535 GCS9 Cl* Melotte 22 HHJ 26 +03 47 44.66 +23 42 03.1 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 1.38 0.91 HHJ152_DH536_L07_139 GCS9 Cl* Melotte 22 HHJ 152 +03 47 44.67 +22 12 43.9 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 5.26 0.74 HHJ15_BPL154 GCS9 Cl* Melotte 22 HHJ 15 +03 47 44.68 +22 23 53.0 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.33 0.90 HCG299_HHJ163_BPL155_L07_208 GCS9 Cl* Melotte 22 HCG 299 +03 47 45.92 +24 38 01.3 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 0.62 HCG287_HHJ168_BPL156_L07_68 GCS9 Cl* Melotte 22 HCG 287 +03 47 46.40 +24 03 02.3 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 7.93 0.84 HHJ438 GCS9 Cl* Melotte 22 HHJ 438 +03 47 46.78 +25 35 16.6 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 1.25 0.44 L07_A1_68 GCS9 +03 47 47.87 +25 13 34.3 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 2.41 0.94 SK398_HHJ266_BPL157_DH537_Moraux2003_34 GCS9 Cl* Melotte 22 SK 398 +03 47 48.91 +24 17 06.5 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 3.93 0.43 L07_A1_69 GCS9 +03 47 49.45 +23 31 52.8 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 7.37 0.65 L07_A1_70 GCS9 +03 47 49.79 +24 25 43.1 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 2.91 0.87 HCG292_HHJ202_BPL158_DH538_L07_77 GCS9 Cl* Melotte 22 HCG 292 +03 47 50.41 +23 54 47.8 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 5.41 0.63 Festin98_007_L07_A1_71 GCS9 Cl* Melotte 22 NPL 007 +03 47 50.95 +24 30 18.6 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 7.73 0.92 HCG295_HHJ389_BPL159_DH539 GCS9 Cl* Melotte 22 HCG 295 +03 47 51.97 +23 39 48.0 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 6.79 0.94 HHJ122_DH540 GCS9 Cl* Melotte 22 HHJ 122 +03 47 52.88 +22 59 33.8 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 11.92 0.89 SK394_HHJ258_BPL160_DH541 GCS9 Cl* Melotte 22 SK 394 +03 47 55.28 +23 19 05.8 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2.43 0.30 HCG302_SK392_HHJ289_DH542 GCS9 Cl* Melotte 22 HCG 302 +03 47 56.64 +24 15 31.7 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 3.78 0.47 BPL162_L07_100 GCS9 Cl* Melotte 22 BPL 162 +03 47 56.66 +26 31 50.9 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 2.10 0.76 HHJ423_DH543 GCS9 Cl* Melotte 22 HHJ 423 +03 47 58.04 +22 06 50.8 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 4.22 0.74 BPL163_L07_A1_72 GCS9 Cl* Melotte 22 BPL 163 +03 47 59.38 +24 35 37.0 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 3.92 0.87 BPL164_DH544_L07_79 GCS9 Cl* Melotte 22 BPL 164 +03 47 59.74 +22 36 01.8 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 6.65 0.69 int-pl-IZ-33_2MASSJ0347597+223601_Y_L07_A1_73 GCS9 Cl* Melotte 22 IPL 33 +03 47 59.77 +22 38 30.1 0 12.945 12.429 11.820 11.418 11.025 11.072 30.05 2.26 -48.45 2.26 26.02 0.00 HHJ365_BPL165 GCS9 Cl* Melotte 22 HHJ 365 +03 48 04.67 +23 39 30.1 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 5.65 0.66 PPl15_IPMBD23_L07_A1_74 GCS9 PPl15 +03 48 04.98 +23 24 13.4 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 1.67 0.89 HHJ21_DH546_L07_160 GCS9 Cl* Melotte 22 HHJ 21 +03 48 05.72 +22 38 09.3 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 31.26 0.82 HCG314_HHJ210_DH547_L07_202 GCS9 Cl* Melotte 22 HCG 314 +03 48 05.83 +23 02 02.7 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 2.60 0.93 HCG309_SK385_T154_HHJ375_DH548_BPL166 GCS9 Cl* Melotte 22 HCG 309 +03 48 06.41 +24 06 51.7 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.58 0.94 L07_122 GCS9 +03 48 06.64 +24 00 06.7 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 1.28 0.90 HHJ240_DH549 GCS9 Cl* Melotte 22 HHJ 240 +03 48 07.96 +23 44 23.5 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.64 0.92 HCG307_HHJ288_DH551 GCS9 Cl* Melotte 22 HCG 307 +03 48 08.96 +23 42 23.3 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 1.13 0.90 HHJ156_DH552 GCS9 Cl* Melotte 22 HHJ 156 +03 48 09.22 +23 58 40.5 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.91 0.90 HHJ225_DH553_L07_107 GCS9 Cl* Melotte 22 HHJ 225 +03 48 10.17 +23 00 03.9 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 5.94 0.66 DH554 GCS9 Cl* Melotte 22 DH 554 +03 48 10.18 +23 59 20.1 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.99 0.88 DH555_PPL12_L07_109 GCS9 Cl* Melotte 22 DH 555 +03 48 13.31 +23 58 46.8 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 4.05 0.93 HCG311_T155_DH560_Festin98_001_L07_108 GCS9 Cl* Melotte 22 HCG 311 +03 48 13.78 +23 37 59.3 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 2.65 0.94 HCG315_SK374_HHJ281_DH561_L07_10 GCS9 Cl* Melotte 22 HCG 315 +03 48 14.30 +24 15 50.5 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 4.06 0.69 BPL169_L07_A1_75 GCS9 Cl* Melotte 22 BPL 169 +03 48 15.25 +23 26 05.4 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 1.54 0.89 HHJ232 GCS9 Cl* Melotte 22 HHJ 232 +03 48 15.27 +23 42 03.4 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 1.36 0.92 HHJ336_DH562 GCS9 Cl* Melotte 22 HHJ 336 +03 48 15.49 +25 14 36.4 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 3.89 0.74 BPL170_DH563_Moraux2003_78_L07_58 GCS9 Cl* Melotte 22 BPL 170 +03 48 16.09 +23 35 15.2 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.55 0.94 HHJ151_DH564_L07_144 GCS9 Cl* Melotte 22 HHJ 151 +03 48 16.38 +26 20 05.8 0 14.225 13.741 13.161 12.587 12.309 12.297 17.16 2.93 -24.32 2.93 0.43 0.00 HCG305 GCS9 Cl* Melotte 22 HCG 305 +03 48 16.58 +21 57 44.4 0 15.162 14.808 14.229 13.602 13.361 13.347 19.66 2.27 -27.09 2.27 22.21 0.00 L07_212 GCS9 +03 48 16.64 +26 30 11.6 0 14.415 13.953 13.400 12.855 12.541 12.576 17.23 2.93 -51.67 2.93 0.24 0.29 HHJ215_DH565 GCS9 Cl* Melotte 22 HHJ 215 +03 48 17.30 +24 30 15.7 0 12.184 11.939 11.515 11.450 10.723 10.898 26.02 2.21 -46.97 2.21 2.32 0.15 HII1785_HCG312_DH566 GCS9 HII1785 +03 48 17.37 +23 48 23.5 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.63 0.94 HHJ188_L07_132 GCS9 Cl* Melotte 22 HHJ 188 +03 48 17.61 +22 04 00.9 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 2.96 0.94 HHJ187_BPL171_L07_209 GCS9 Cl* Melotte 22 HHJ 187 +03 48 19.02 +24 25 12.7 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 13.03 0.49 BPL172_Festin98_005_Roque12_L07_photNM_22 GCS9 Cl* Melotte 22 BPL 172 +03 48 19.84 +23 36 11.8 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 1.90 0.91 DH568 GCS9 Cl* Melotte 22 DH 568 +03 48 20.29 +24 54 54.9 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 2.97 0.94 HCG313_SK371_T23_BPL173 GCS9 Cl* Melotte 22 HCG 313 +03 48 20.58 +23 31 01.3 0 15.523 14.983 14.358 13.817 13.510 13.517 24.03 2.14 -49.73 2.14 6.53 0.15 HCG321_DH570 GCS9 Cl* Melotte 22 HCG 321 +03 48 21.54 +24 34 43.4 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 3.06 0.73 BPL174_DH571_L07_78 GCS9 Cl* Melotte 22 BPL 174 +03 48 22.66 +22 52 21.3 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 4.73 0.91 HCG323_SK370_A91_BPL175_DH572 GCS9 Cl* Melotte 22 HCG 323 +03 48 22.71 +23 27 42.8 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.89 0.94 HHJ171_DH573 GCS9 Cl* Melotte 22 HHJ 171 +03 48 22.81 +24 48 53.4 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 2.53 0.78 SK368_HHJ309_BPL176_DH574 GCS9 Cl* Melotte 22 SK 368 +03 48 23.92 +23 08 08.1 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 1.68 0.83 DH576 GCS9 Cl* Melotte 22 DH 576 +03 48 25.24 +24 14 25.8 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 5.84 0.94 HCG322_SK363_HHJ314_BPL178 GCS9 Cl* Melotte 22 HCG 322 +03 48 26.05 +25 14 40.8 0 12.786 12.471 11.969 11.411 11.122 11.211 18.96 2.21 -54.45 2.21 33.24 0.00 HCG317_DH578 GCS9 Cl* Melotte 22 HCG 317 +03 48 26.60 +23 11 29.5 0 13.307 12.741 12.122 11.691 11.279 11.326 25.82 2.13 -37.29 2.13 0.76 0.17 HCG332_SK362_HHJ339 GCS9 Cl* Melotte 22 HCG 332 +03 48 27.36 +23 46 16.2 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 1.05 0.57 L07_A1_76 GCS9 +03 48 29.76 +23 58 05.8 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 3.01 0.92 HCG328_WILL3_HHJ132 GCS9 Cl* Melotte 22 HCG 328 +03 48 30.29 +24 18 00.2 0 20.225 19.201 17.793 16.971 16.382 16.374 21.38 2.76 -51.81 2.76 2.78 0.54 L07_A1_77 GCS9 +03 48 30.74 +22 44 50.2 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 1.90 0.47 Roque25 GCS9 Cl* Melotte 22 Roque 25 +03 48 31.06 +24 16 53.1 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 10.39 0.79 HCG324_VM46_HHJ404_BPL180_DH580 GCS9 Cl* Melotte 22 HCG 324 +03 48 31.53 +24 34 37.2 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 3.41 0.49 BRB16_PIZ1_L07_A1_78_L07_258 GCS9 Cl* Melotte 22 BRB 16 +03 48 31.84 +24 01 58.6 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 6.09 0.93 HCG327_HHJ197_DH581_L07_110 GCS9 Cl* Melotte 22 HCG 327 +03 48 33.78 +24 01 58.8 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 4.68 0.87 HHJ184_DH582_L07_111 GCS9 Cl* Melotte 22 HHJ 184 +03 48 35.20 +22 53 42.1 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 1.57 0.62 HHJ10_BPL181_PPL10 GCS9 Cl* Melotte 22 HHJ 10 +03 48 35.49 +24 12 03.0 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 2.04 0.85 HCG335_HHJ96_BPL182_DH583_L07_95 GCS9 Cl* Melotte 22 HCG 335 +03 48 36.34 +25 15 41.2 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 13.10 0.54 BPL183_Moraux2003_105_L07_56 GCS9 Cl* Melotte 22 BPL 183 +03 48 38.37 +22 33 51.8 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 7.70 0.60 L07_A1_79 GCS9 +03 48 39.31 +24 50 19.9 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 1.67 0.84 DH585_L07_75 GCS9 Cl* Melotte 22 DH 585 +03 48 39.91 +24 12 42.7 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 1.22 0.87 HCG337_SK353_HHJ347_BPL184 GCS9 Cl* Melotte 22 HCG 337 +03 48 40.43 +24 36 34.0 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 2.52 0.89 HCG333_SK351_BPL185_DH586 GCS9 Cl* Melotte 22 HCG 333 +03 48 40.60 +25 01 19.8 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 1.98 0.90 BPL186_L07_63 GCS9 Cl* Melotte 22 BPL 186 +03 48 40.99 +23 14 17.2 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 4.91 0.92 HCG343_SK352 GCS9 Cl* Melotte 22 HCG 343 +03 48 42.15 +25 00 28.2 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.66 0.92 HCG339_SK349_B207_BPL187 GCS9 Cl* Melotte 22 HCG 339 +03 48 42.69 +24 27 19.4 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.44 0.85 HHJ44_WILL6_BPL188_DH587_L07_89 GCS9 Cl* Melotte 22 HHJ 44 +03 48 43.14 +26 32 20.9 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 0.52 0.39 HHJ246_DH588 GCS9 Cl* Melotte 22 HHJ 246 +03 48 44.05 +25 06 22.4 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 3.48 0.92 BPL189_DH589_Moraux2003_48_L07_55 GCS9 Cl* Melotte 22 BPL 189 +03 48 44.69 +24 37 23.5 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 1.14 0.75 DH590_CFHT-Pl-5_M5.5_BRB7_CFHT-Pl-5_L07_A1_80_L07_223 GCS9 Cl* Melotte 22 DH 590 +03 48 45.35 +24 37 26.3 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 2.78 0.83 HCG346_HHJ111_BPL190 GCS9 Cl* Melotte 22 HCG 346 +03 48 46.02 +24 10 12.5 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 1.14 0.89 HHJ207_DH59_L07_112 GCS9 Cl* Melotte 22 HHJ 207 +03 48 48.62 +24 30 15.4 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 6.82 0.70 L07_90 GCS9 +03 48 48.79 +23 24 48.0 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 1.51 0.64 HHJ86 GCS9 Cl* Melotte 22 HHJ 86 +03 48 50.45 +22 44 29.8 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 2.49 0.71 HHJ3_BPL191 GCS9 Cl* Melotte 22 HHJ 3 +03 48 50.45 +25 17 54.7 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 2.79 0.67 BPL192_Moraux2003_109 GCS9 Cl* Melotte 22 BPL 192 +03 48 50.50 +24 08 27.2 0 16.669 16.113 15.512 14.949 14.599 14.574 7.25 2.25 -27.47 2.25 3.93 0.00 DH593 GCS9 Cl* Melotte 22 DH 593 +03 48 50.67 +23 04 30.0 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 1.74 0.90 HHJ116 GCS9 Cl* Melotte 22 HHJ 116 +03 48 51.56 +24 12 17.3 0 12.963 12.601 12.088 11.632 11.240 11.331 19.13 2.22 -24.55 2.22 13.96 0.00 BPL193_ALR728 GCS9 Cl* Melotte 22 BPL 193 +03 48 52.29 +25 22 32.3 0 14.846 14.364 13.770 13.224 12.890 12.904 16.02 2.23 -33.87 2.23 4.39 0.17 L07_43 GCS9 +03 48 55.65 +24 21 40.1 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 2.50 0.70 HHJ8_L07_A1_81 GCS9 Cl* Melotte 22 HHJ 8 +03 48 57.36 +24 19 43.6 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 3.88 0.89 HCG349_SK340 GCS9 Cl* Melotte 22 HCG 349 +03 48 58.29 +23 05 39.0 0 13.329 12.999 12.460 11.907 11.630 11.649 20.85 2.13 -49.85 2.13 3.36 0.54 HHJ353_DH595 GCS9 Cl* Melotte 22 HHJ 353 +03 49 00.99 +24 54 10.0 0 13.608 13.216 12.673 12.188 11.812 11.833 24.02 2.20 -54.18 2.20 4.15 0.01 HCG351_SK335_HHJ332_BPL195 GCS9 Cl* Melotte 22 HCG 351 +03 49 01.02 +22 58 49.2 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 2.32 0.84 HCG353_SK336_A55_HHJ414_DH597 GCS9 Cl* Melotte 22 HCG 353 +03 49 01.16 +23 38 15.5 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 6.87 0.86 DH598_L07_145 GCS9 Cl* Melotte 22 DH 598 +03 49 01.50 +24 11 38.3 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 49.18 0.81 HHJ231_BPL196_DH599 GCS9 Cl* Melotte 22 HHJ 231 +03 49 02.35 +25 43 24.1 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 7.61 0.66 DH2004_600 GCS9 Cl* Melotte 22 DH 600 +03 49 02.97 +21 54 46.9 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 3.03 0.75 BPL197_L07_211 GCS9 Cl* Melotte 22 BPL 197 +03 49 04.86 +23 33 39.3 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 23.53 0.70 Roque47_IPMBD20_L07_A1_82 GCS9 Cl* Melotte 22 Roque 47 +03 49 05.18 +22 04 52.7 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 5.97 0.59 L07_A1_83 GCS9 +03 49 05.93 +23 06 21.9 0 13.996 13.617 13.008 12.510 12.202 12.212 21.86 2.13 -55.37 2.13 8.61 0.00 HCG357_SK332_DH602 GCS9 Cl* Melotte 22 HCG 357 +03 49 06.76 +25 24 21.5 0 15.881 15.480 14.942 14.330 14.052 14.083 10.08 2.25 -28.77 2.25 1.80 0.00 L07_44 GCS9 +03 49 08.84 +25 53 48.6 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 2.17 0.89 SK327_HHJ387_DH603_Moraux2003_8 GCS9 Cl* Melotte 22 SK 327 +03 49 11.95 +24 39 26.5 0 19.975 18.858 18.018 17.618 17.140 17.153 33.87 3.66 -38.06 3.66 1.67 0.11 L07_256 GCS9 +03 49 12.18 +25 20 31.7 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 1.32 0.89 L07_42 GCS9 +03 49 15.12 +24 36 22.5 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 9.93 0.64 BPL202_CFHT-Pl-9_M6.5_BRB10_CFHT-Pl-9_L07_A1_85_L07_222 GCS9 Cl* Melotte 22 BPL 202 +03 49 15.63 +23 22 49.1 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 3.40 0.88 HHJ36_L07_152_DH610 GCS9 Cl* Melotte 22 HHJ 36 +03 49 16.18 +26 49 02.9 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 1.06 0.37 IPMBD21 GCS9 Cl* Melotte 22 IPMBD 21 +03 49 16.42 +24 03 49.0 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 7.58 0.71 HCG362 GCS9 Cl* Melotte 22 HCG 362 +03 49 20.31 +25 25 42.4 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 5.46 0.66 HCG366_HHJ251_DH611_Moraux2003_31 GCS9 Cl* Melotte 22 HCG 366 +03 49 20.70 +24 52 35.2 0 15.612 15.094 14.529 14.021 13.731 13.738 44.56 2.21 -41.45 2.21 12.56 0.00 BPL203 GCS9 Cl* Melotte 22 BPL 203 +03 49 20.96 +26 11 33.5 0 16.986 16.399 15.818 15.206 14.866 14.910 12.65 3.05 -35.63 3.05 1.07 0.10 DH614 GCS9 Cl* Melotte 22 DH 614 +03 49 21.17 +23 34 02.0 0 18.421 17.444 16.621 16.028 15.535 15.548 15.81 2.36 -29.43 2.36 3.20 0.03 Roque8_L07_photNM_11 GCS9 Cl* Melotte 22 Roque 8 +03 49 21.25 +24 41 40.8 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 4.32 0.84 HHJ51_BPL204_DH615 GCS9 Cl* Melotte 22 HHJ 51 +03 49 21.50 +23 39 06.3 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 5.34 0.82 HCG363_SK319_HHJ306_DH616 GCS9 Cl* Melotte 22 HCG 363 +03 49 21.93 +24 54 43.2 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 4.87 0.94 SK316_BPL205_DH617_L07_62 GCS9 Cl* Melotte 22 SK 316 +03 49 22.15 +25 47 37.7 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 2.58 0.80 HHJ196_DH618_Moraux2003_52 GCS9 Cl* Melotte 22 HHJ 196 +03 49 22.52 +21 37 56.0 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 1.35 0.15 DH2004_619 GCS9 Cl* Melotte 22 DH 619 +03 49 25.55 +22 18 44.4 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 2.43 0.79 BPL206 GCS9 Cl* Melotte 22 BPL 206 +03 49 25.61 +23 02 49.9 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 3.06 0.87 HHJ79_BPL207_DH620_L07_187 GCS9 Cl* Melotte 22 HHJ 79 +03 49 26.64 +22 50 54.5 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 1.72 0.91 SK315_BPL208_L07_198 GCS9 Cl* Melotte 22 SK 315 +03 49 26.65 +22 52 20.5 0 14.721 14.312 13.776 13.161 12.926 12.924 34.03 2.25 -25.62 2.25 11.59 0.00 L07_185 GCS9 +03 49 27.58 +24 24 13.7 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 90.55 0.54 HHJ192_DH621 GCS9 Cl* Melotte 22 HHJ 192 +03 49 27.65 +24 31 54.1 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 4.32 0.40 HCG370_DH622 GCS9 Cl* Melotte 22 HCG 370 +03 49 28.80 +26 50 51.4 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 0.50 0.37 Moraux2003_95 GCS9 Cl* Melotte 22 MBSC 95 +03 49 29.81 +23 38 55.2 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 7.60 0.86 HHJ158_DH624_L07_149 GCS9 Cl* Melotte 22 HHJ 158 +03 49 31.22 +23 41 19.6 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 5.61 0.93 HHJ142_DH625 GCS9 Cl* Melotte 22 HHJ 142 +03 49 32.16 +23 16 17.9 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 1.24 0.86 DH626 GCS9 Cl* Melotte 22 DH 626 +03 49 32.57 +23 24 41.0 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 6.19 0.91 HHJ245_L07_153 GCS9 Cl* Melotte 22 HHJ 245 +03 49 32.81 +25 47 46.8 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 4.11 0.93 HHJ209_DH628_Moraux2003_60_L07_23 GCS9 Cl* Melotte 22 HHJ 209 +03 49 33.04 +24 32 02.4 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 3.25 0.79 HCG372_SK309_T90_HHJ346_BPL209 GCS9 Cl* Melotte 22 HCG 372 +03 49 35.29 +25 59 34.6 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 3.27 0.86 HHJ155_DH630 GCS9 Cl* Melotte 22 HHJ 155 +03 49 35.46 +22 23 26.1 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.89 0.93 HHJ112_BPL211 GCS9 Cl* Melotte 22 HHJ 112 +03 49 36.32 +26 02 16.3 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 4.90 0.53 HHJ42_DH633 GCS9 Cl* Melotte 22 HHJ 42 +03 49 38.24 +26 51 05.6 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.33 0.94 DH635_Moraux2003_29 GCS9 Cl* Melotte 22 DH 635 +03 49 39.32 +23 34 54.9 0 17.647 17.116 16.453 15.939 15.609 15.592 -2.29 2.32 -44.61 2.32 7.80 0.00 Roque42 GCS9 Cl* Melotte 22 Roque 42 +03 49 41.14 +23 14 59.3 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 3.53 0.83 L07_171 GCS9 +03 49 41.21 +22 56 40.5 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 6.90 0.69 BPL213_L07_A1_86_L07_242 GCS9 Cl* Melotte 22 BPL 213 +03 49 41.34 +26 08 53.2 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 5.88 0.89 HHJ305_DH636 GCS9 Cl* Melotte 22 HHJ 305 +03 49 41.71 +21 56 19.2 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.65 0.72 BPL214 GCS9 Cl* Melotte 22 BPL 214 +03 49 43.17 +24 39 46.4 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 1.42 0.57 BPL215_L07_A1_87_L07_218 GCS9 Cl* Melotte 22 BPL 215 +03 49 47.04 +25 42 36.8 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 2.88 0.94 DH638_L07_5 GCS9 Cl* Melotte 22 DH 638 +03 49 48.16 +26 37 47.5 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.39 0.89 Moraux2003_98 GCS9 Cl* Melotte 22 MBSC 98 +03 49 48.44 +22 10 48.3 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 1.17 0.71 BPL216_DH639 GCS9 Cl* Melotte 22 BPL 216 +03 49 48.77 +23 42 59.3 0 19.186 18.198 17.242 16.647 16.119 16.132 -24.98 2.50 -59.62 2.50 3.68 0.00 L07_photNM_12 GCS9 +03 49 51.43 +25 15 30.4 0 20.207 19.539 18.563 17.881 17.415 17.463 21.41 4.65 -39.77 4.65 0.80 0.46 Roque2 GCS9 Cl* Melotte 22 Roque 2 +03 49 51.80 +21 18 26.1 0 12.546 12.207 11.705 11.428 10.854 11.164 28.74 3.05 -39.12 3.05 1.63 0.10 HCG378_DH640 GCS9 Cl* Melotte 22 HCG 378 +03 49 52.44 +24 03 43.0 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 3.73 0.70 L07_A1_88 GCS9 +03 49 55.49 +24 06 05.0 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 6.13 0.94 DH641 GCS9 Cl* Melotte 22 DH 641 +03 49 55.79 +24 44 31.3 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 7.36 0.92 HCG380_SK294_T92_BPL217_DH642 GCS9 Cl* Melotte 22 HCG 380 +03 49 56.77 +25 22 22.6 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 6.29 0.93 DH643 GCS9 Cl* Melotte 22 DH 643 +03 49 56.81 +24 59 07.1 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 7.78 0.61 BPL218_L07_A1_89_L07_217 GCS9 Cl* Melotte 22 BPL 218 +03 49 57.64 +23 43 28.2 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 32.32 0.87 DH644_L07_150 GCS9 Cl* Melotte 22 DH 644 +03 49 57.85 +23 41 49.6 0 18.821 17.908 17.116 16.554 16.119 16.122 12.13 2.44 -65.77 2.44 10.58 0.00 Roque6 GCS9 Cl* Melotte 22 Roque 6 +03 49 58.33 +25 06 20.9 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 2.86 0.88 BPL219_L07_52 GCS9 Cl* Melotte 22 BPL 219 +03 49 58.34 +23 42 33.7 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 24.46 0.93 SK293 GCS9 Cl* Melotte 22 SK 293 +03 49 58.61 +25 53 46.2 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 2.86 0.89 L07_24 GCS9 +03 49 59.54 +24 11 45.6 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 41.43 0.90 BPL220 GCS9 Cl* Melotte 22 BPL 220 +03 50 01.57 +25 24 01.5 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 3.60 0.83 SK291_DH646 GCS9 Cl* Melotte 22 SK 291 +03 50 01.87 +25 12 40.9 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 5.30 0.85 HCG385_SK289_BPL222_DH647 GCS9 Cl* Melotte 22 HCG 385 +03 50 02.18 +23 51 44.6 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 5.36 0.81 HCG382_DH648 GCS9 Cl* Melotte 22 HCG 382 +03 50 03.94 +24 56 02.8 0 16.234 15.728 15.114 14.515 14.147 14.145 1.53 2.22 -47.12 2.22 1.59 0.00 BPL223_L07_PM_NM_51 GCS9 Cl* Melotte 22 BPL 223 +03 50 04.25 +23 10 44.5 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 5.17 0.84 SK287_DH649 GCS9 Cl* Melotte 22 SK 287 +03 50 05.00 +23 18 17.2 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 48.45 0.77 DH650_L07_151 GCS9 Cl* Melotte 22 DH 650 +03 50 06.43 +26 58 18.7 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.15 0.92 DH652 GCS9 Cl* Melotte 22 DH 652 +03 50 06.56 +24 59 46.3 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 4.23 0.92 BPL224_DH653 GCS9 Cl* Melotte 22 BPL 224 +03 50 08.27 +25 30 51.7 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 0.96 0.58 L07_A1_90 GCS9 +03 50 08.42 +25 32 55.7 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 1.82 0.93 HCG386_DH654_L07_35 GCS9 Cl* Melotte 22 HCG 386 +03 50 08.62 +24 40 17.7 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 9.31 0.90 DH655 GCS9 Cl* Melotte 22 DH 655 +03 50 10.77 +24 28 41.4 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 8.77 0.90 BPL225_DH656_L07_84 GCS9 Cl* Melotte 22 BPL 225 +03 50 12.46 +23 55 35.9 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 2.36 0.92 SK279_DH659 GCS9 Cl* Melotte 22 SK 279 +03 50 12.60 +23 48 48.9 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 4.65 0.77 DH660 GCS9 Cl* Melotte 22 DH 660 +03 50 12.87 +24 21 06.2 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 8.13 0.75 HII2601_HCG390_SK278_DH661 GCS9 HII2601 +03 50 13.40 +23 59 29.8 0 20.475 19.183 17.880 16.935 16.219 16.193 42.39 2.66 -31.73 2.66 1.78 0.02 L07_A1_91 GCS9 +03 50 14.76 +25 25 29.8 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 4.67 0.91 DH662_L07_40 GCS9 Cl* Melotte 22 DH 662 +03 50 15.27 +24 13 36.0 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.48 0.94 HCG394_SK276_B214_BPL226_DH663 GCS9 Cl* Melotte 22 HCG 394 +03 50 15.33 +24 35 40.3 0 16.164 15.638 15.090 14.514 14.208 14.215 15.42 2.22 -15.83 2.22 5.62 0.00 L07_photNM_23 GCS9 +03 50 16.09 +24 08 34.7 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 2.35 0.67 Roque30_L07_A1_92_L07_259 GCS9 Cl* Melotte 22 Roque 30 +03 50 17.59 +22 55 58.8 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 2.13 0.89 DH664 GCS9 Cl* Melotte 22 DH 664 +03 50 18.22 +24 35 15.3 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 7.48 0.85 BPL227_DH665+L07_85 GCS9 Cl* Melotte 22 BPL 227 +03 50 19.15 +24 16 34.0 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 5.08 0.67 BPL228_L07_A1_93_L07_226 GCS9 Cl* Melotte 22 BPL 228 +03 50 22.01 +23 55 30.3 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 1.80 0.65 L07_A1_94 GCS9 +03 50 22.04 +22 37 32.3 0 14.088 13.717 13.128 12.578 12.281 12.280 13.50 2.51 -32.83 2.51 5.52 0.03 HCG399_DH666 GCS9 Cl* Melotte 22 HCG 399 +03 50 23.69 +24 25 43.6 0 17.469 16.705 16.019 15.399 15.045 14.969 9.85 2.30 -45.98 2.30 3.57 0.11 L07_221 GCS9 +03 50 24.96 +20 42 17.8 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 3.00 0.11 DH668 GCS9 Cl* Melotte 22 DH 668 +03 50 25.16 +23 55 41.8 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 1.44 0.90 HCG396_DH669 GCS9 Cl* Melotte 22 HCG 396 +03 50 27.35 +23 22 44.9 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 0.74 SK273_DH670 GCS9 Cl* Melotte 22 SK 273 +03 50 27.83 +23 03 54.9 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.78 0.94 DH671 GCS9 Cl* Melotte 22 DH 671 +03 50 29.30 +24 56 34.6 0 14.639 14.187 13.579 12.980 12.665 12.647 23.93 2.20 -30.32 2.20 2.43 0.00 BPL229_Moraux2003_58_L07_64 GCS9 Cl* Melotte 22 BPL 229 +03 50 29.96 +25 03 06.7 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.61 0.87 HCG403_SK272_B213_BPL230_DH672 GCS9 Cl* Melotte 22 HCG 403 +03 50 31.95 +22 08 47.5 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 1.62 0.75 HCG401_DH673 GCS9 Cl* Melotte 22 HCG 401 +03 50 32.58 +24 08 52.9 0 19.612 18.727 17.919 17.469 16.994 16.954 32.96 3.31 -13.95 3.31 1.26 0.00 Roque31 GCS9 Cl* Melotte 22 Roque 31 +03 50 33.08 +24 20 21.6 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 4.47 0.93 BPL231_DH674 GCS9 Cl* Melotte 22 BPL 231 +03 50 37.42 +22 28 08.0 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 1.02 0.87 HCG404_DH677 GCS9 Cl* Melotte 22 HCG 404 +03 50 38.92 +23 13 02.5 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 1.49 0.79 SK263_DH678 GCS9 Cl* Melotte 22 SK 263 +03 50 39.33 +26 16 03.7 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 0.34 0.09 DH680 GCS9 Cl* Melotte 22 DH 680 +03 50 40.83 +24 40 02.6 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 2.17 0.78 DH681_Moraux2003_83_L07_70 GCS9 Cl* Melotte 22 DH 681 +03 50 42.44 +24 12 55.4 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 2.68 0.85 DH683_Moraux2003_73_L07_101 GCS9 Cl* Melotte 22 DH 683 +03 50 44.35 +25 07 05.1 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 1.71 0.84 L07_60 GCS9 +03 50 45.38 +25 06 10.5 0 14.663 14.213 13.627 13.059 12.768 12.744 12.87 2.20 -29.87 2.20 1.06 0.00 L07_59 GCS9 +03 50 48.97 +22 40 11.8 0 13.084 12.693 12.183 11.610 11.323 11.328 23.79 2.13 -30.12 2.13 1.45 0.00 HCG409_DH684 GCS9 Cl* Melotte 22 HCG 409 +03 50 54.65 +24 21 55.7 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 26.89 0.87 DH687_Moraux2003_55_L07_102 GCS9 Cl* Melotte 22 DH 687 +03 50 54.99 +24 33 03.5 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 8.04 0.85 Moraux2003_69 GCS9 Cl* Melotte 22 MBSC 69 +03 50 57.41 +24 24 44.4 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 3.95 0.83 L07_81 GCS9 +03 50 57.42 +24 06 30.7 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 7.91 0.80 HCG415_SK251_DH689_Moraux2003_15 GCS9 Cl* Melotte 22 HCG 415 +03 51 03.62 +24 32 35.1 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 8.27 0.78 DH691 GCS9 Cl* Melotte 22 DH 691 +03 51 05.08 +26 36 51.2 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 0.51 0.41 DH692_Moraux2003_93 GCS9 Cl* Melotte 22 DH 692 +03 51 05.97 +24 36 16.9 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 5.62 0.34 L07_A1_95_L07_220 GCS9 +03 51 06.13 +22 38 00.6 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 2.59 0.82 DH694 GCS9 Cl* Melotte 22 DH 694 +03 51 07.11 +23 20 57.6 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 10.33 0.90 DH695 GCS9 Cl* Melotte 22 DH 695 +03 51 11.88 +23 44 43.3 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 19.86 0.89 BPL232_Moraux2003_90 GCS9 Cl* Melotte 22 BPL 232 +03 51 17.96 +26 01 22.1 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 7.12 0.91 DH701_L07_20 GCS9 Cl* Melotte 22 DH 701 +03 51 18.72 +26 03 15.4 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 3.57 0.90 L07_21 GCS9 +03 51 18.86 +26 03 08.7 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 6.61 0.92 SK237 GCS9 Cl* Melotte 22 SK 237 +03 51 19.07 +24 10 13.1 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 1.13 0.92 HCG424_SK235_BPL233_DH702_Moraux2003_3 GCS9 Cl* Melotte 22 HCG 424 +03 51 19.48 +23 09 49.4 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 1.60 0.90 DH703_L07_12 GCS9 Cl* Melotte 22 DH 703 +03 51 19.77 +23 04 04.1 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 7.69 0.88 DH704 GCS9 Cl* Melotte 22 DH 704 +03 51 20.68 +19 38 37.8 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.42 0.77 DH705 GCS9 Cl* Melotte 22 DH 705 +03 51 23.82 +22 50 29.1 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 3.94 0.88 DH706 GCS9 Cl* Melotte 22 DH 706 +03 51 23.89 +25 05 52.6 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 32.31 0.88 SK231_DH708 GCS9 Cl* Melotte 22 SK 231 +03 51 24.17 +26 03 11.3 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 2.49 0.93 HCG427_SK232_T29_DH709 GCS9 Cl* Melotte 22 HCG 427 +03 51 25.88 +24 47 38.7 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 11.49 0.46 HCG428_SK230_DH712 GCS9 Cl* Melotte 22 HCG 428 +03 51 30.60 +27 22 49.5 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.73 0.89 DH714 GCS9 Cl* Melotte 22 DH 714 +03 51 34.01 +24 34 08.9 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.67 0.90 DH715_Moraux2003_46 GCS9 Cl* Melotte 22 DH 715 +03 51 36.41 +25 13 40.6 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 2.11 0.91 DH717 GCS9 Cl* Melotte 22 DH 717 +03 51 38.96 +24 30 44.8 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 14.05 0.64 L07_A1_97_L07_253 GCS9 +03 51 39.08 +23 22 04.3 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 10.84 0.85 BPL237_L07_166 GCS9 Cl* Melotte 22 BPL 237 +03 51 42.34 +25 57 25.6 0 16.214 15.645 14.965 14.400 14.005 13.990 13.59 2.25 -34.32 2.25 4.49 0.06 L07_A1_98 GCS9 +03 51 44.95 +23 26 39.3 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 9.87 0.62 BPL240_L07_A1_99_L07_240 GCS9 Cl* Melotte 22 BPL 240 +03 51 46.56 +23 23 34.8 0 13.746 13.429 12.922 12.317 12.066 12.079 28.17 2.25 -4.26 2.25 10.21 0.00 BPL241 GCS9 Cl* Melotte 22 BPL 241 +03 51 47.66 +24 39 58.9 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 4.12 0.52 PLZJ21_PLIZ31 GCS9 Cl* Melotte 22 PlZJ 21 +03 51 50.60 +22 53 44.3 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 2.45 0.72 HCG437_SK212_DH724_L07_14 GCS9 Cl* Melotte 22 HCG 437 +03 51 51.15 +23 17 41.1 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 21.53 0.93 L07_13 GCS9 +03 51 51.55 +23 34 49.1 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 19.31 0.89 BPL242_CFHT-Pl-1_M4.9_Moraux2003_91_BRB1_CFHT-Pl-1_L07_140 GCS9 Cl* Melotte 22 BPL 242 +03 51 54.52 +23 33 31.3 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 31.11 0.70 HCG439_SK210_BPL244_DH727_Moraux2003_23 GCS9 Cl* Melotte 22 HCG 439 +03 51 55.05 +23 57 42.0 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 4.30 0.86 HCG440_BPL245_DH728_L07_119 GCS9 Cl* Melotte 22 HCG 440 +03 51 57.53 +25 48 31.2 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 7.40 0.94 HCG446_SK207_DH731 GCS9 Cl* Melotte 22 HCG 446 +03 51 58.35 +23 58 19.3 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 1.15 0.65 BPL246_DH732_L07_120 GCS9 Cl* Melotte 22 BPL 246 +03 51 59.32 +24 39 58.8 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.80 0.83 Moraux2003_28 GCS9 Cl* Melotte 22 MBSC 28 +03 51 59.93 +23 24 25.6 0 19.672 18.405 17.376 16.709 16.210 16.167 44.15 3.19 6.95 3.19 5.04 0.00 L07_photNM_15 GCS9 +03 52 01.65 +25 01 29.1 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 3.33 0.89 HCG447_BPL248_DH733 GCS9 Cl* Melotte 22 HCG 447 +03 52 02.10 +23 15 45.4 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 16.77 0.44 BPL249_L07_A1_100_L07_255 GCS9 Cl* Melotte 22 BPL 249 +03 52 02.28 +24 21 47.9 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 4.71 0.28 SK204_DH734 GCS9 Cl* Melotte 22 SK 204 +03 52 02.64 +25 06 14.9 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 2.28 0.91 BPL250_DH736_L07_57 GCS9 Cl* Melotte 22 BPL 250 +03 52 03.58 +25 01 13.6 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 3.16 0.90 BPL251_DH737 GCS9 Cl* Melotte 22 BPL 251 +03 52 03.62 +23 17 21.3 0 14.608 14.228 13.716 13.085 12.840 12.838 8.56 2.26 -19.96 2.26 73.82 0.00 SK202 GCS9 Cl* Melotte 22 SK 202 +03 52 04.48 +24 14 39.6 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 2.51 0.92 BPL252_DH739_L07_91 GCS9 Cl* Melotte 22 BPL 252 +03 52 05.58 +22 34 55.1 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 1.01 0.91 SK201_DH740 GCS9 Cl* Melotte 22 SK 201 +03 52 05.83 +24 17 31.0 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 2.70 0.67 BPL253_CFHT-Pl-7_M5.6_Moraux2003_108_BRB8_CFHT-Pl-7_L07_A1_101_L07_225 GCS9 Cl* Melotte 22 BPL 253 +03 52 06.67 +22 01 14.3 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 1.48 0.62 DH741 GCS9 Cl* Melotte 22 DH 741 +03 52 06.72 +24 16 00.4 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 6.89 0.68 BPL254_CFHT-Pl-13_M6.0_BRB11_CFHT-Pl-13,Teide2,CFHT-PLIZ-3_L07_A1_102_L07_224 GCS9 Cl* Melotte 22 BPL 254 +03 52 07.43 +25 53 02.7 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 1.02 0.88 L07_2 GCS9 +03 52 07.96 +25 27 54.6 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.42 0.67 HCG449_BPL255_DH742 GCS9 Cl* Melotte 22 HCG 449 +03 52 11.01 +24 09 21.6 0 14.675 14.287 13.746 13.155 12.875 12.891 19.68 2.24 -25.40 2.24 3.06 0.00 BPL256 GCS9 Cl* Melotte 22 BPL 256 +03 52 11.23 +20 52 33.4 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.93 0.91 DH743 GCS9 Cl* Melotte 22 DH 743 +03 52 12.19 +26 22 08.9 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 1.31 0.86 HCG452_DH744 GCS9 Cl* Melotte 22 HCG 452 +03 52 13.20 +26 11 45.3 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 1.13 0.88 DH745 GCS9 Cl* Melotte 22 DH 745 +03 52 13.32 +26 08 36.8 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.47 0.82 HCG451_SK197_DH746_L07_1 GCS9 Cl* Melotte 22 HCG 451 +03 52 15.01 +24 20 23.7 0 12.845 12.510 11.997 11.537 11.142 11.317 25.52 2.24 -17.36 2.24 2.89 0.00 BPL257 GCS9 Cl* Melotte 22 BPL 257 +03 52 15.20 +27 56 37.5 0 15.781 15.372 14.821 14.296 14.065 14.036 35.34 3.01 -42.14 3.01 0.77 0.00 DH747 GCS9 Cl* Melotte 22 DH 747 +03 52 17.44 +25 06 11.8 0 15.000 14.563 13.990 13.419 13.132 13.135 -0.61 2.20 -35.73 2.20 4.16 0.00 BPL258 GCS9 Cl* Melotte 22 BPL 258 +03 52 17.54 +24 27 19.7 0 14.481 13.947 13.400 12.888 12.573 12.615 5.97 2.20 -33.38 2.20 95.51 0.00 BPL259_DH748_Moraux2003_42 GCS9 Cl* Melotte 22 BPL 259 +03 52 18.46 +22 00 53.3 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.71 0.91 DH749 GCS9 Cl* Melotte 22 DH 749 +03 52 18.64 +24 04 28.1 0 18.327 17.280 16.395 15.738 15.202 15.738 15.37 2.40 -46.07 2.40 9.62 0.50 CFHT-Pl-23_XX GCS9 Cl* Melotte 22 CFHT 23 +03 52 18.72 +23 52 36.6 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 6.65 0.82 BPL260_L07_127 GCS9 Cl* Melotte 22 BPL 260 +03 52 20.66 +24 33 55.5 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 5.83 0.78 HCG454_SK188_T159_DH750 GCS9 Cl* Melotte 22 HCG 454 +03 52 25.93 +21 50 31.5 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 1.83 0.83 DH752 GCS9 Cl* Melotte 22 DH 752 +03 52 30.54 +19 40 39.6 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 0.89 0.55 DH753 GCS9 Cl* Melotte 22 DH 753 +03 52 30.91 +24 32 39.5 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.86 0.89 HCG456_SK178_BPL261_DH754_Moraux2003_12 GCS9 Cl* Melotte 22 HCG 456 +03 52 31.38 +25 15 07.5 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 7.95 0.92 SK179_BPL262_DH755 GCS9 Cl* Melotte 22 SK 179 +03 52 31.60 +25 19 49.5 0 16.266 15.764 15.163 14.645 14.313 14.280 33.73 2.98 -58.22 2.98 0.55 0.00 BPL263 GCS9 Cl* Melotte 22 BPL 263 +03 52 33.34 +23 51 06.7 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 5.90 0.93 BPL264_DH756_L07_126 GCS9 Cl* Melotte 22 BPL 264 +03 52 34.48 +22 30 07.5 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 1.65 0.63 HCG458_SK174_sk174_DH757 GCS9 Cl* Melotte 22 HCG 458 +03 52 35.32 +25 01 04.5 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 2.24 0.94 BPL265_DH758_L07_61 GCS9 Cl* Melotte 22 BPL 265 +03 52 38.91 +25 50 25.3 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.35 0.89 HCG461_HHJ244_DH760_Moraux2003_30 GCS9 Cl* Melotte 22 HCG 461 +03 52 41.82 +26 46 10.5 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.18 0.74 DH761 GCS9 Cl* Melotte 22 DH 761 +03 52 42.21 +25 10 42.2 0 15.719 15.130 14.525 13.956 13.610 13.606 7.18 2.24 -43.36 2.24 13.94 0.05 DH762 GCS9 Cl* Melotte 22 DH 762 +03 52 43.24 +24 27 58.5 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 1.73 0.93 BPL266_DH763_L07_82 GCS9 Cl* Melotte 22 BPL 266 +03 52 43.47 +27 28 20.7 0 15.621 15.237 14.702 14.167 13.881 13.877 19.52 3.32 -29.27 3.32 0.59 0.00 DH764 GCS9 Cl* Melotte 22 DH 764 +03 52 44.29 +23 54 14.9 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 4.54 0.86 BPL267_DH765_CFHT-Pl-2_M4.9_BRB2_CFHT-Pl-2_L07_128 GCS9 Cl* Melotte 22 BPL 267 +03 52 44.48 +24 20 59.3 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 2.44 0.87 BPL268_DH766_Moraux2003_70 GCS9 Cl* Melotte 22 BPL 268 +03 52 46.45 +24 33 41.0 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 1.87 0.76 BPL270_Moraux2003_47_L07_83 GCS9 Cl* Melotte 22 BPL 270 +03 52 51.72 +22 31 32.7 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 1.02 0.94 HCG462_SK150_HHJ302_DH770 GCS9 Cl* Melotte 22 HCG 462 +03 52 51.79 +23 33 47.9 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 4.57 0.88 HHJ22_BPL272_Moraux2003_99_BRB5_CFHT-Pl-3_MBSC99_L07_148 GCS9 Cl* Melotte 22 HHJ 22 +03 52 54.25 +25 17 43.3 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.44 0.89 HCG464_SK151_BPL274_DH771 GCS9 Cl* Melotte 22 HCG 464 +03 52 55.92 +24 57 41.8 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2.34 0.48 BPL275_L07_A1_103_L07_252 GCS9 Cl* Melotte 22 BPL 275 +03 52 56.97 +22 26 01.1 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.86 0.92 HCG463_SK143_HHJ358_DH772 GCS9 Cl* Melotte 22 HCG 463 +03 52 58.78 +25 26 19.0 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.26 0.90 BPL277 GCS9 Cl* Melotte 22 BPL 277 +03 52 59.48 +22 46 59.9 0 16.051 15.626 15.081 14.514 14.235 14.263 6.77 2.28 -42.20 2.28 15.58 0.02 L07_192 GCS9 +03 53 01.63 +22 58 48.2 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 16.24 0.94 HHJ181_DH776_L07_183 GCS9 Cl* Melotte 22 HHJ 181 +03 53 05.13 +25 04 15.6 0 15.954 15.369 14.759 14.236 13.861 13.829 7.81 2.24 -28.58 2.24 19.42 0.00 HHJ13_BPL278 GCS9 Cl* Melotte 22 HHJ 13 +03 53 07.29 +25 47 21.4 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 1.34 0.32 HHJ53_DH778_Moraux2003_89 GCS9 Cl* Melotte 22 HHJ 53 +03 53 07.48 +25 18 03.4 0 15.833 15.247 14.597 14.025 13.647 13.618 2.66 2.95 -37.03 2.95 0.59 0.00 BPL279 GCS9 Cl* Melotte 22 BPL 279 +03 53 09.63 +23 33 47.7 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.56 0.63 BPL280_CFHT-Pl-4_XX_Moraux2003_101_BRB6_CFHT-Pl-4,MBSC101 GCS9 Cl* Melotte 22 BPL 280 +03 53 10.16 +23 03 10.6 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.47 0.93 HHJ330_DH779 GCS9 Cl* Melotte 22 HHJ 330 +03 53 12.52 +25 48 17.1 0 16.111 15.665 15.069 14.499 14.163 14.185 20.43 2.98 -8.43 2.98 0.61 0.00 Moraux2003_106 GCS9 Cl* Melotte 22 MBSC 106 +03 53 14.00 +19 42 59.3 0 15.849 15.386 14.792 14.189 13.888 13.898 9.11 4.02 -47.37 4.02 0.59 0.10 DH780 GCS9 Cl* Melotte 22 DH 780 +03 53 15.71 +22 52 14.3 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 20.38 0.93 HCG467_SK134_B369_HHJ323_DH781_L07_15 GCS9 Cl* Melotte 22 HCG 467 +03 53 16.44 +23 20 58.1 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 2.72 0.80 BPL281_L07_154 GCS9 Cl* Melotte 22 BPL 281 +03 53 21.68 +24 03 26.6 0 15.326 14.768 14.128 13.552 13.218 7.55 2.31 -30.24 2.31 2.14 0.00 BPL282 GCS9 Cl* Melotte 22 BPL 282 +03 53 24.12 +23 47 58.4 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 4.05 0.89 HHJ173_BPL285_DH784_Moraux2003_39_L07_131 GCS9 Cl* Melotte 22 HHJ 173 +03 53 24.24 +25 14 37.7 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 18.74 0.71 L07_A1_105 GCS9 +03 53 26.54 +24 46 44.8 0 16.063 15.580 14.980 14.429 14.130 14.105 -15.85 2.25 -65.72 2.25 2.82 0.00 L07_photNM_24 GCS9 +03 53 30.76 +19 54 24.1 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.52 0.91 DH787 GCS9 Cl* Melotte 22 DH 787 +03 53 35.52 +26 07 08.0 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.36 0.87 HHJ124_DH790_Moraux2003_64 GCS9 Cl* Melotte 22 HHJ 124 +03 53 44.53 +24 00 42.4 0 16.464 15.928 15.311 14.743 14.396 21.83 2.33 -19.33 2.33 2.88 0.00 BPL289 GCS9 Cl* Melotte 22 BPL 289 +03 53 47.58 +23 44 30.9 0 14.156 13.695 13.118 12.583 12.291 25.83 2.30 -36.52 2.30 12.91 0.18 HCG474_SK115_HHJ237_BPL290_Moraux2003_88 GCS9 Cl* Melotte 22 HCG 474 +03 53 48.04 +23 49 09.6 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 7.95 0.81 HHJ28_BPL291 GCS9 Cl* Melotte 22 HHJ 28 +03 53 48.14 +23 58 12.3 0 16.128 15.626 15.014 14.480 14.122 45.32 2.32 -23.11 2.32 16.02 0.00 BPL292 GCS9 Cl* Melotte 22 BPL 292 +03 53 48.49 +23 29 09.3 0 13.958 13.639 13.144 12.476 12.276 12.276 22.11 2.25 -50.69 2.25 1.26 0.30 L07_11 GCS9 +03 53 49.10 +23 32 49.5 0 16.498 15.857 15.188 14.669 14.312 14.255 20.11 2.27 -19.91 2.27 1.92 0.00 BPL293 GCS9 Cl* Melotte 22 BPL 293 +03 53 51.53 +23 37 32.4 0 15.072 14.583 13.986 13.438 13.123 13.114 10.35 2.25 -31.49 2.25 0.90 0.00 L07_142 GCS9 +03 53 52.46 +22 37 34.1 0 13.916 13.447 12.840 12.297 11.984 11.987 21.25 2.50 -22.78 2.50 0.32 0.00 SK109 GCS9 Cl* Melotte 22 SK 109 +03 53 55.13 +23 23 36.1 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 8.66 0.70 BPL294_CFHT-Pl-12_M8.0_BRB9_CFHT-Pl-12,CFHT-PLIZ-6_PLZJ9_PLIZ6 GCS9 Cl* Melotte 22 BPL 294 +03 54 00.71 +23 58 59.8 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.03 2.24 7.85 0.73 HHJ322_BPL298 GCS9 Cl* Melotte 22 HHJ 322 +03 54 01.12 +23 19 41.0 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 12.64 0.83 HHJ20_BPL299 GCS9 Cl* Melotte 22 HHJ 20 +03 54 02.73 +23 35 00.9 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 6.97 0.80 BPL301_Moraux2003_68_L07_141 GCS9 Cl* Melotte 22 BPL 301 +03 54 04.89 +25 05 06.2 0 14.698 14.348 13.849 13.358 13.082 13.085 33.90 2.23 -56.38 2.23 10.70 0.00 BPL302 GCS9 Cl* Melotte 22 BPL 302 +03 54 05.35 +23 33 59.2 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 5.84 0.61 BPL303_CFHT-Pl-25_M9.0_BRB15_CFHT-PLIZ-20,PLZJ11_L07_A1_106_L07_254 GCS9 Cl* Melotte 22 BPL 303 +03 54 11.49 +25 18 42.7 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.56 0.88 HCG484_BPL304_DH796 GCS9 Cl* Melotte 22 HCG 484 +03 54 13.02 +23 20 50.8 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 1.02 0.94 HCG482_SK97_HHJ325_BPL305_DH797_Moraux2003_13 GCS9 Cl* Melotte 22 HCG 482 +03 54 14.07 +23 17 51.9 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 5.30 0.47 BRB18_L0.0_CFHT-PLIZ-28 GCS9 Cl* Melotte 22 BRB 18 +03 54 14.18 +25 37 57.2 0 13.687 13.351 12.846 12.276 12.008 11.988 15.24 2.93 -23.51 2.93 0.25 0.00 SK99_HHJ318 GCS9 Cl* Melotte 22 SK 99 +03 54 15.29 +25 09 52.2 0 17.907 17.026 16.179 15.579 15.102 15.061 14.10 2.34 -32.82 2.34 5.56 0.04 BPL306_L07_A1_107 GCS9 Cl* Melotte 22 BPL 306 +03 54 15.60 +24 20 45.7 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 4.55 0.33 BPL307_BPL308_Moraux2003_96 GCS9 Cl* Melotte 22 BPL 307 +03 54 16.80 +22 00 16.6 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 1.97 0.67 DH799 GCS9 Cl* Melotte 22 DH 799 +03 54 19.69 +24 41 52.0 0 13.851 13.644 13.223 12.660 12.539 12.515 24.74 2.23 -26.36 2.23 1.62 0.00 L07_6 GCS9 +03 54 21.23 +23 23 48.9 0 13.899 13.505 12.946 12.350 12.071 12.051 17.29 2.25 -14.89 2.25 10.35 0.00 BPL310 GCS9 Cl* Melotte 22 BPL 310 +03 54 22.49 +23 38 11.9 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 7.78 0.77 HHJ214_BPL311_DH801_Moraux2003_38_L07_143 GCS9 Cl* Melotte 22 HHJ 214 +03 54 24.00 +23 51 58.7 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 12.19 0.58 L07_123 GCS9 +03 54 24.39 +22 41 20.5 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 6.55 0.92 L07_190 GCS9 +03 54 25.04 +24 42 43.4 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 3.32 0.94 HCG487_HHJ131_BPL312_DH802_Moraux2003_67_L07_71 GCS9 Cl* Melotte 22 HCG 487 +03 54 28.11 +23 56 36.0 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 2.36 0.85 BPL313 GCS9 Cl* Melotte 22 BPL 313 +03 54 31.49 +22 39 01.5 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 3.40 0.53 L07_A1_108_L07_244 GCS9 +03 54 33.14 +23 33 39.5 0 13.404 13.138 12.649 12.026 11.829 11.837 29.86 2.24 -16.03 2.24 2.59 0.00 BPL314 GCS9 Cl* Melotte 22 BPL 314 +03 54 34.80 +21 53 01.7 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 5.77 0.87 HCG488_DH804 GCS9 Cl* Melotte 22 HCG 488 +03 54 37.36 +23 13 32.5 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 7.31 0.81 DH805 GCS9 Cl* Melotte 22 DH 805 +03 54 39.13 +24 35 54.5 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 3.51 0.88 BPL315_DH806_Moraux2003_103 GCS9 Cl* Melotte 22 BPL 315 +03 54 40.56 +23 42 23.7 0 16.133 15.654 15.075 14.441 14.147 14.128 21.00 2.26 -8.72 2.26 2.22 0.00 L07_PM_NM_45 GCS9 +03 54 46.39 +25 02 58.2 0 15.875 15.298 14.639 14.106 13.746 13.746 12.58 2.24 -33.12 2.24 3.87 0.03 BPL317 GCS9 Cl* Melotte 22 BPL 317 +03 54 46.53 +25 31 34.9 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.23 0.90 DH808 GCS9 Cl* Melotte 22 DH 808 +03 54 48.00 +25 12 30.2 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.13 2.23 9.47 0.87 SK82_HHJ371_BPL318_DH809 GCS9 Cl* Melotte 22 SK 82 +03 54 55.34 +22 59 40.1 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 3.01 0.78 HHJ62 GCS9 Cl* Melotte 22 HHJ 62 +03 54 58.03 +25 14 29.0 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 3.97 0.82 SK76_BPL321_DH810 GCS9 Cl* Melotte 22 SK 76 +03 54 59.43 +23 58 07.6 0 18.399 17.751 17.008 16.573 16.182 16.573 -15.56 2.74 -76.49 2.74 2.13 0.00 CFHT-Pl-22_XX GCS9 Cl* Melotte 22 CFHT 22 +03 55 03.69 +25 13 16.7 0 15.227 14.748 14.150 13.541 13.229 13.257 24.07 2.23 -25.82 2.23 2.86 0.00 BPL322 GCS9 Cl* Melotte 22 BPL 322 +03 55 05.75 +23 03 17.1 0 17.281 16.747 16.124 15.457 15.159 15.148 22.55 2.35 -23.42 2.35 7.78 0.00 DH811 GCS9 Cl* Melotte 22 DH 811 +03 55 07.09 +24 17 25.7 0 13.823 13.494 12.962 12.312 12.069 12.072 45.79 2.24 -23.35 2.24 9.80 0.00 Moraux2003_24 GCS9 Cl* Melotte 22 MBSC 24 +03 55 08.98 +24 05 02.5 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 2.15 0.90 SK65_HHJ313_BPL324_DH812_Moraux2003_18 GCS9 Cl* Melotte 22 SK 65 +03 55 10.57 +23 40 30.8 0 16.148 15.629 15.011 14.450 14.138 14.42 2.32 -24.94 2.32 4.22 0.00 Moraux2003_107 GCS9 Cl* Melotte 22 MBSC 107 +03 55 11.85 +22 58 02.5 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 8.86 0.83 HHJ61 GCS9 Cl* Melotte 22 HHJ 61 +03 55 17.17 +23 53 16.7 0 16.639 15.930 15.233 14.684 14.291 14.302 -3.54 2.27 -45.04 2.27 8.18 0.00 BPL325 GCS9 Cl* Melotte 22 BPL 325 +03 55 20.68 +22 32 08.6 0 14.849 14.456 13.903 13.290 13.023 13.017 25.35 2.51 -36.82 2.51 2.55 0.26 HHJ144_DH814 GCS9 Cl* Melotte 22 HHJ 144 +03 55 23.08 +24 49 04.9 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 7.67 0.72 BPL327_IPMBD11_PLZJ78_PLIZ2 GCS9 Cl* Melotte 22 BPL 327 +03 55 24.90 +23 27 21.2 0 14.218 13.952 13.438 12.772 12.577 12.555 17.19 2.25 -18.72 2.25 12.58 0.00 L07_165 GCS9 +03 55 27.06 +25 14 45.8 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 7.07 0.60 BPL328_L07_A1_111_L07_216 GCS9 Cl* Melotte 22 BPL 328 +03 55 30.90 +23 23 50.9 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 14.93 0.52 SK54_HHJ284_BPL329_DH815_Moraux2003_32 GCS9 Cl* Melotte 22 SK 54 +03 55 32.84 +23 19 08.0 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 7.73 0.61 DH2004_816 GCS9 Cl* Melotte 22 DH 816 +03 55 34.43 +23 58 28.3 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 6.79 0.55 HHJ78_BPL330_DH818_Moraux2003_80 GCS9 Cl* Melotte 22 HHJ 78 +03 55 44.34 +23 23 26.3 0 14.936 14.679 14.164 13.488 13.317 13.333 24.36 2.26 -31.05 2.26 5.80 0.00 L07_164 GCS9 +03 55 47.14 +25 14 39.5 0 17.252 16.517 15.773 15.223 14.793 14.775 11.42 2.29 -27.04 2.29 4.43 0.00 L07_A1_112 GCS9 +03 55 47.45 +22 50 50.1 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 3.85 0.63 L07_A1_113 GCS9 +03 55 49.92 +23 48 22.5 0 15.778 15.219 14.575 14.013 13.650 13.627 40.52 2.97 -15.60 2.97 0.28 0.00 BPL332 GCS9 Cl* Melotte 22 BPL 332 +03 55 50.11 +21 43 27.7 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 0.38 0.35 DH819 GCS9 Cl* Melotte 22 DH 819 +03 55 56.43 +25 17 59.8 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.63 0.94 HCG504_SK37_DH822 GCS9 Cl* Melotte 22 HCG 504 +03 55 57.55 +21 10 38.7 0 13.543 13.117 12.574 12.061 11.714 11.762 6.82 3.36 -22.12 3.36 1.12 0.00 DH823 GCS9 Cl* Melotte 22 DH 823 +03 56 09.81 +22 27 59.3 0 13.653 13.341 12.818 12.142 11.912 11.933 27.43 2.50 -42.46 2.50 0.16 0.22 HHJ385 GCS9 Cl* Melotte 22 HHJ 385 +03 56 18.60 +23 57 51.6 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.46 0.89 SK22_HHJ386_DH830 GCS9 Cl* Melotte 22 SK 22 +03 56 22.31 +21 07 16.9 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 0.54 0.40 DH831 GCS9 Cl* Melotte 22 DH 831 +03 56 22.39 +24 37 22.8 0 17.293 16.827 16.215 15.615 15.296 15.316 19.53 2.64 -22.35 2.64 1.45 0.00 DH832 GCS9 Cl* Melotte 22 DH 832 +03 56 24.99 +23 05 26.6 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 1.45 0.72 SK17_HHJ419 GCS9 Cl* Melotte 22 SK 17 +03 56 25.93 +24 16 51.3 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.24 0.83 SK18_HHJ416_DH834 GCS9 Cl* Melotte 22 SK 18 +03 56 28.91 +24 01 54.1 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.55 0.93 HHJ165_DH837_Moraux2003_41 GCS9 Cl* Melotte 22 HHJ 165 +03 56 36.56 +25 15 52.3 0 12.102 12.000 11.678 11.535 11.250 11.370 20.72 2.47 -47.20 2.47 9.47 0.56 DH2004_840 GCS9 Cl* Melotte 22 DH 840 +03 56 38.76 +21 35 08.3 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.16 0.93 DH841 GCS9 Cl* Melotte 22 DH 841 +03 56 39.34 +24 31 42.3 0 16.146 15.667 15.060 14.438 14.108 14.131 6.45 2.50 -37.23 2.50 0.69 0.00 BPL338 GCS9 Cl* Melotte 22 BPL 338 +03 56 39.67 +22 41 12.0 0 16.699 16.176 15.614 15.000 14.672 14.657 23.26 3.06 -34.21 3.06 0.25 0.04 DH842 GCS9 Cl* Melotte 22 DH 842 +03 56 46.60 +26 21 00.4 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 2.18 0.77 HHJ64_DH843 GCS9 Cl* Melotte 22 HHJ 64 +03 56 52.31 +25 10 05.1 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2.72 0.54 HHJ17 GCS9 Cl* Melotte 22 HHJ 17 +03 56 52.91 +23 25 43.7 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.16 0.89 HHJ271_DH846_Moraux2003_35 GCS9 Cl* Melotte 22 HHJ 271 +03 56 55.47 +22 08 24.3 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.38 0.80 DH847 GCS9 Cl* Melotte 22 DH 847 +03 56 57.08 +24 48 34.3 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 2.78 0.89 HCG511_HHJ393_DH848 GCS9 Cl* Melotte 22 HCG 511 +03 57 05.73 +22 49 14.9 0 15.172 14.200 13.601 13.300 13.290 41.88 3.06 -25.84 3.06 0.73 0.00 HHJ85 GCS9 Cl* Melotte 22 HHJ 85 +03 57 08.61 +20 07 42.7 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.70 0.90 DH849 GCS9 Cl* Melotte 22 DH 849 +03 57 09.81 +21 36 27.2 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 1.52 0.30 DH850 GCS9 Cl* Melotte 22 DH 850 +03 57 40.68 +25 16 04.1 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 0.52 0.37 DH856 GCS9 Cl* Melotte 22 DH 856 +03 57 42.98 +25 23 06.7 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 2.98 0.92 HCG516_HHJ180_DH857 GCS9 Cl* Melotte 22 HCG 516 +03 57 49.37 +22 08 30.9 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.50 0.72 HHJ7_DH858 GCS9 Cl* Melotte 22 HHJ 7 +03 57 49.44 +23 28 40.9 0 16.455 16.008 15.423 14.867 14.538 14.486 21.43 3.05 -44.61 3.05 1.11 0.60 HHJ4 GCS9 Cl* Melotte 22 HHJ 4 +03 57 52.35 +21 02 15.8 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 0.07 0.37 DH859 GCS9 Cl* Melotte 22 DH 859 +03 57 55.85 +21 16 10.8 0 15.354 14.971 14.436 13.805 13.526 13.536 16.87 3.37 -36.80 3.37 0.12 0.62 DH860 GCS9 Cl* Melotte 22 DH 860 +03 58 01.97 +23 53 54.5 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.06 0.88 HHJ84_DH862 GCS9 Cl* Melotte 22 HHJ 84 +03 58 13.93 +25 06 27.3 0 13.268 12.878 12.303 11.694 11.359 11.407 -0.09 2.47 -50.14 2.47 7.14 0.00 HHJ369 GCS9 Cl* Melotte 22 HHJ 369 +03 58 25.14 +24 00 58.5 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.84 0.92 HHJ220_DH865 GCS9 Cl* Melotte 22 HHJ 220 +03 58 30.99 +23 04 12.7 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.55 0.89 HHJ60_DH866 GCS9 Cl* Melotte 22 HHJ 60 +03 58 34.18 +22 40 11.1 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 1.36 0.81 HHJ93_DH867 GCS9 Cl* Melotte 22 HHJ 93 +03 58 56.15 +23 27 53.5 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.72 0.81 HHJ388_DH868 GCS9 Cl* Melotte 22 HHJ 388 +03 58 57.04 +23 42 31.1 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.94 0.82 HCG519_HHJ411_DH870 GCS9 Cl* Melotte 22 HCG 519 +03 59 05.73 +26 24 26.6 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.87 0.93 DH871 GCS9 Cl* Melotte 22 DH 871 +03 59 06.31 +25 03 20.0 0 13.383 12.988 12.334 11.705 11.382 11.486 4.31 2.47 -61.05 2.47 21.47 0.00 HHJ366_DH872 GCS9 Cl* Melotte 22 HHJ 366 +03 59 12.92 +24 17 18.4 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 0.41 0.22 DH873 GCS9 Cl* Melotte 22 DH 873 +03 59 15.87 +25 54 08.3 0 14.875 14.465 13.906 13.228 12.965 12.971 32.55 2.48 -38.84 2.48 0.41 0.00 HHJ133 GCS9 Cl* Melotte 22 HHJ 133 +03 59 18.99 +23 31 23.9 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.84 0.92 HHJ150_DH874 GCS9 Cl* Melotte 22 HHJ 150 +03 59 26.36 +22 21 22.2 0 15.719 15.289 14.695 14.117 13.819 13.820 31.13 2.88 -39.73 2.88 0.46 0.00 HHJ39 GCS9 Cl* Melotte 22 HHJ 39 +03 59 26.93 +21 48 18.7 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 1.42 0.94 DH876 GCS9 Cl* Melotte 22 DH 876 +03 59 29.33 +21 39 56.0 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.34 0.79 DH878 GCS9 Cl* Melotte 22 DH 878 +03 59 31.47 +21 16 18.3 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.74 0.87 DH879 GCS9 Cl* Melotte 22 DH 879 +03 59 52.43 +22 21 57.6 0 13.184 12.972 12.496 11.861 11.662 11.699 22.40 2.84 -35.16 2.84 0.08 0.23 DH880 GCS9 Cl* Melotte 22 DH 880 +03 59 56.49 +23 40 15.3 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.37 0.69 DH882 GCS9 Cl* Melotte 22 DH 882 +03 59 59.15 +23 41 04.6 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.30 0.83 DH883 GCS9 Cl* Melotte 22 DH 883 +03 59 59.86 +22 05 29.3 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 0.93 0.58 DH884 GCS9 Cl* Melotte 22 DH 884 +04 00 00.40 +23 47 07.2 0 15.850 15.374 14.799 14.250 13.943 27.42 3.57 -41.36 3.57 0.20 0.14 DH885 GCS9 Cl* Melotte 22 DH 885 +04 00 02.53 +26 36 02.0 0 14.847 14.477 13.972 13.308 13.038 13.047 4.33 2.48 -47.48 2.48 6.50 0.00 DH886 GCS9 Cl* Melotte 22 DH 886 +04 00 10.30 +22 02 17.0 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.29 0.94 DH888 GCS9 Cl* Melotte 22 DH 888 +04 00 14.11 +24 48 51.4 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.44 0.77 DH889 GCS9 Cl* Melotte 22 DH 889 +04 00 26.15 +23 26 17.3 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.11 0.89 DH891 GCS9 Cl* Melotte 22 DH 891 +04 00 28.19 +23 51 24.0 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.64 0.78 DH892 GCS9 Cl* Melotte 22 DH 892 +04 00 28.35 +27 20 54.5 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.16 0.81 DH893 GCS9 Cl* Melotte 22 DH 893 +04 00 39.24 +26 44 19.0 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 3.18 0.26 DH894 GCS9 Cl* Melotte 22 DH 894 +04 00 49.33 +25 12 10.6 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.15 0.89 DH895 GCS9 Cl* Melotte 22 DH 895 +04 01 08.61 +22 57 22.3 0 14.093 13.707 13.221 12.574 12.334 12.323 41.92 3.35 -24.05 3.35 38.80 0.00 DH897 GCS9 Cl* Melotte 22 DH 897 +04 01 12.08 +23 54 12.8 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.46 0.82 DH898 GCS9 Cl* Melotte 22 DH 898 +04 01 13.99 +23 10 29.9 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.17 0.77 DH899 GCS9 Cl* Melotte 22 DH 899 +04 01 26.06 +21 35 08.5 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.12 0.94 DH900 GCS9 Cl* Melotte 22 DH 900 +04 01 49.17 +22 10 05.0 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.13 0.83 DH901 GCS9 Cl* Melotte 22 DH 901 +04 01 59.58 +25 19 12.2 0 16.333 15.812 15.179 14.530 14.202 14.168 19.23 3.35 -37.71 3.35 0.41 0.48 DH902 GCS9 Cl* Melotte 22 DH 902 +04 02 22.62 +24 48 24.0 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.66 0.89 DH903 GCS9 Cl* Melotte 22 DH 903 +04 02 49.97 +23 30 38.4 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.12 0.81 DH904 GCS9 Cl* Melotte 22 DH 904 +04 03 01.22 +25 24 23.0 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 0.24 0.27 DH905 GCS9 Cl* Melotte 22 DH 905 +04 03 16.56 +24 35 19.6 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.10 0.83 DH906 GCS9 Cl* Melotte 22 DH 906 +04 03 24.94 +22 52 17.0 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.56 0.92 DH2004_907 GCS9 Cl* Melotte 22 DH 907 +04 03 40.67 +25 21 34.5 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 0.13 0.25 DH908 GCS9 Cl* Melotte 22 DH 908 +04 03 43.84 +23 53 47.0 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.36 0.93 DH909 GCS9 Cl* Melotte 22 DH 909 +04 03 49.57 +23 43 13.7 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.18 0.90 DH910 GCS9 Cl* Melotte 22 DH 910 +04 04 35.56 +25 07 14.7 0 13.458 13.161 12.678 12.087 11.856 11.863 18.96 2.94 -34.69 2.94 1.29 0.30 DH911 GCS9 Cl* Melotte 22 DH 911 +04 04 45.65 +24 41 19.1 0 14.008 13.433 12.771 12.193 11.842 11.858 15.03 2.94 -29.97 2.94 0.86 0.00 DH912 GCS9 Cl* Melotte 22 DH 912 +04 05 13.75 +24 08 42.7 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.54 0.94 DH915 GCS9 Cl* Melotte 22 DH 915 +04 06 29.99 +22 33 43.6 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 0.55 0.14 DH2004_916 GCS9 Cl* Melotte 22 DH 916 +03 27 35.60 +24 31 42.8 0 11.913 11.791 11.262 11.438 10.650 29.84 6.95 -51.81 6.95 5.58 NM DH001 GCS9 Cl* Melotte 22 DH 001 +03 27 37.78 +24 59 00.4 0 18.239 17.866 17.264 16.663 16.418 4.37 9.41 -21.38 9.41 0.68 NM DH2004_2 GCS9 Cl* Melotte 22 DH 2 +03 40 27.44 +24 20 42.0 0 19.876 19.366 18.295 17.674 17.142 0.114 19.62 5.81 -2.47 5.81 2.79 NM int-pl-IZ-83;IPLJ0340274+242042_N GCS9 Cl* Melotte 22 IPL 83 +03 40 40.02 +24 44 08.9 0 13.551 13.221 12.733 12.866 11.933 11.954 100.30 2.48 -40.25 2.48 710.82 NM HCG40_SK760_HHJ412 GCS9 Cl* Melotte 22 HCG 40 +03 40 42.48 +22 25 52.9 0 17.673 17.180 16.589 16.016 15.679 15.679 20.34 2.74 -38.19 2.74 3.77 NM DH149 GCS9 Cl* Melotte 22 DH 149 +03 40 47.27 +23 52 35.9 0 16.329 15.775 15.144 14.569 14.220 14.229 -14.15 2.16 -37.59 2.16 2.18 NM BPL16 GCS9 Cl* Melotte 22 BPL 16 +03 40 52.79 +25 24 43.5 0 19.211 18.106 17.097 16.437 15.871 15.971 11.63 2.75 -0.11 2.75 1.90 NM L07_A1_3 GCS9 +03 41 13.48 +26 01 18.1 0 14.297 14.031 13.545 13.781 12.723 12.729 -45.56 2.23 13.58 2.23 883.14 NM L07_19 GCS9 +03 41 20.82 +25 41 15.4 0 14.693 14.445 13.990 13.378 13.244 13.251 15.68 2.23 -33.00 2.23 4.42 NM L07_37 GCS9 +03 41 31.45 +23 05 12.5 0 17.388 17.172 16.798 16.334 16.205 16.153 -4.54 2.83 5.67 2.83 3.33 NM L07_photNM_1 GCS9 +03 41 33.33 +24 00 56.8 0 16.757 16.263 15.632 15.043 14.669 0.010 -0.93 2.35 28.95 2.35 0.35 NM int-pl-IZ-13;IPLJ0341333+240056_N GCS9 Cl* Melotte 22 IPL 13 +03 41 33.76 +24 11 18.7 0 16.689 16.102 15.481 14.947 14.519 0.009 -17.54 2.34 -3.76 2.34 0.87 NM int-pl-IZ-89;IPLJ0341337+241118_BPL24_Y GCS9 Cl* Melotte 22 IPL 89 +03 41 37.74 +23 04 32.7 0 17.983 17.097 16.312 15.674 15.230 15.224 55.66 2.38 -15.82 2.38 1.35 NM L07_A1_7 GCS9 +03 41 45.01 +24 02 00.5 0 20.471 21.487 18.728 18.097 17.603 0.192 -13.87 6.33 2.10 6.33 2.08 NM int-pl-IZ-12;IPLJ0341450+240200_N GCS9 Cl* Melotte 22 IPL 12 +03 41 45.82 +23 14 26.1 0 16.057 15.577 14.958 14.278 13.965 13.959 11.09 2.27 -13.29 2.27 1.09 NM L07_PM_NM_1 GCS9 +03 42 01.36 +24 07 42.8 0 20.269 19.509 18.937 18.788 18.036 0.158 44.55 7.28 -34.90 7.28 2.78 NM int-pl-IZ-79;IPLJ0342013+240742_N GCS9 Cl* Melotte 22 IPL 79 +03 42 04.72 +23 29 04.2 0 16.255 15.752 15.126 14.475 14.133 14.135 5.09 2.27 -8.96 2.27 1.00 NM L07_PM_NM_2 GCS9 +03 42 07.98 +22 39 33.4 0 18.739 17.474 16.501 15.831 15.206 15.225 -14.88 2.43 -2.56 2.43 5.87 NM int-pl-IZ-76_2MASSJ0342080+223933_Y_L07_A1_12 GCS9 Cl* Melotte 22 IPL 76 +03 42 10.17 +22 48 44.5 0 17.259 16.355 15.543 14.932 14.469 14.500 21.01 2.29 -90.42 2.29 1.52 NM L07_PM_NM_3 GCS9 +03 42 16.89 +23 23 26.2 0 14.399 14.006 13.488 12.830 12.556 12.534 4.44 2.25 -20.25 2.25 2.48 NM SK694 GCS9 Cl* Melotte 22 SK 694 +03 42 26.81 +24 50 20.5 0 16.617 15.878 15.170 14.570 14.175 14.570 5.87 3.00 7.70 3.00 11.87 NM SFHT-Pl-8_XX GCS9 Cl* Melotte 22 CFHT 8 +03 42 27.94 +25 25 20.0 0 18.768 18.348 17.661 17.160 16.956 16.830 -1.58 3.77 -14.43 3.77 1.80 NM L07_photNM_2 GCS9 +03 42 28.52 +24 03 40.9 0 17.374 16.756 16.090 15.493 15.055 0.015 14.53 2.24 4.35 2.24 2.29 NM int-pl-IZ-32;IPLJ0342285+240340_N GCS9 Cl* Melotte 22 IPL 32 +03 42 43.46 +22 38 31.3 0 19.335 18.795 18.306 17.850 17.254 17.390 6.96 6.04 -3.70 6.04 3.63 NM L07_faintJK_9 GCS9 +03 42 46.24 +23 54 50.4 0 18.364 17.566 16.865 16.291 15.869 0.031 -9.31 2.56 -4.06 2.56 2.52 NM int-pl-IZ-30;2MASSJ0342462+235450_N GCS9 Cl* Melotte 22 IPL 30 +03 42 49.78 +23 55 53.0 0 17.226 16.592 15.902 15.202 14.834 0.014 -20.00 2.22 -24.83 2.22 1.37 NM int-pl-IZ-28;2MASSJ0342497+235553_N GCS9 Cl* Melotte 22 IPL 28 +03 43 03.28 +23 41 30.7 0 18.320 17.643 16.965 16.316 15.973 0.032 0.97 2.58 -4.45 2.58 2.37 NM int-pl-IZ-10;IPLJ0343032+234130_N GCS9 Cl* Melotte 22 IPL 10 +03 43 12.21 +23 09 16.5 0 18.623 17.585 16.672 16.054 15.582 15.559 23.62 2.52 -78.70 2.52 5.17 NM L07_photNM_3 GCS9 +03 43 13.91 +24 08 20.2 0 14.902 14.430 13.830 13.297 12.985 13.008 7.02 2.13 -16.75 2.13 1.63 NM BPL55 GCS9 Cl* Melotte 22 BPL 55 +03 43 13.93 +23 47 46.0 0 16.658 16.080 15.498 15.003 14.645 0.010 80.42 2.19 -64.23 2.19 0.96 NM int-pl-IZ-8;2MASSJ0343138+234746_N GCS9 Cl* Melotte 22 IPL 8 +03 43 29.16 +24 02 06.4 0 16.723 16.206 15.569 14.907 14.555 0.010 3.90 2.33 -14.75 2.33 0.81 NM int-pl-IZ-22;IPLJ0343291+240206_N GCS9 Cl* Melotte 22 IPL 22 +03 43 29.87 +24 15 47.3 0 13.312 13.075 12.598 11.994 11.788 -2.96 2.30 -30.76 2.30 0.73 NM BPL60 GCS9 Cl* Melotte 22 BPL 60 +03 43 41.55 +23 38 56.9 0 13.507 13.136 11.471 13.159 10.941 68.56 2.30 127.64 2.30 9.62 NM HII157_HD23157_Tr050 GCS9 HII157 +03 43 47.80 +24 03 17.0 0 19.840 18.837 18.173 17.520 17.095 0.106 17.69 3.88 0.19 3.88 4.05 NM int-pl-IZ-18;IPLJ0343477+240316_N GCS9 Cl* Melotte 22 IPL 18 +03 43 48.10 +22 42 19.4 0 16.799 16.182 15.580 15.085 14.719 0.011 -12.71 2.31 -6.73 2.31 5.51 NM int-pl-IZ-77;2MASSJ0343481+224219_N GCS9 Cl* Melotte 22 IPL 77 +03 43 49.31 +24 07 42.2 0 13.277 13.106 12.742 12.360 12.239 10.92 2.30 -2.78 2.30 1.99 NM BPL64 GCS9 Cl* Melotte 22 BPL 64 +03 43 52.03 +22 55 24.5 0 18.877 17.799 16.954 16.281 15.790 15.790 8.59 2.63 -3.83 2.63 2.80 NM int-pl-IZ-55_IPLJ0343520+225524_Y_L07_photNM_4 GCS9 Cl* Melotte 22 IPL 55 +03 43 53.05 +23 21 50.2 0 16.148 15.662 15.035 14.372 14.027 14.014 -14.95 2.27 -18.64 2.27 6.97 NM L07_PM_NM_4 GCS9 +03 44 09.32 +23 02 18.5 0 18.527 17.911 17.141 16.542 16.135 0.040 -0.31 2.78 0.00 2.78 4.10 NM int-pl-IZ-54;IPLJ0344093+230218_N GCS9 Cl* Melotte 22 IPL 54 +03 44 09.39 +23 17 07.2 0 16.376 15.837 15.168 14.608 14.201 14.250 -32.76 2.28 2.92 2.28 11.23 NM L07_PM_NM_5 GCS9 +03 44 10.08 +22 43 57.8 0 16.296 15.777 15.173 14.582 14.243 14.252 4.33 2.28 -0.56 2.28 2.86 NM L07_PM_NM_6 GCS9 +03 44 10.90 +23 40 15.0 0 19.101 18.430 17.764 17.019 16.548 -2.43 3.36 -8.57 3.36 1.93 NM Roque3 GCS9 Cl* Melotte 22 Roque 3 +03 44 12.66 +23 43 17.6 0 20.244 19.037 18.123 17.534 17.170 15.97 4.26 -3.99 4.26 3.70 NM Roque18 GCS9 Cl* Melotte 22 Roque 18 +03 44 12.68 +25 24 35.1 0 18.091 17.245 16.455 15.959 15.528 15.959 -5.40 2.49 16.14 2.49 2.91 NM CFHT-Pl-20_L07_photNM_5 GCS9 Cl* Melotte 22 CFHT 20 +03 44 20.84 +24 39 02.8 0 19.245 18.126 17.127 16.400 15.949 3.92 3.63 7.91 3.63 6.22 NM Roque36 GCS9 Cl* Melotte 22 Roque 36 +03 44 26.36 +26 02 30.9 0 13.193 12.826 12.324 11.355 11.457 11.506 1.38 2.23 -17.15 2.23 540.35 NM HCG142_SK561_T10_DH337 GCS9 Cl* Melotte 22 HCG 142 +03 44 26.46 +22 40 00.5 0 15.389 15.105 14.594 13.943 13.721 13.732 23.92 2.25 -18.53 2.25 38.11 NM L07_195 GCS9 +03 44 33.80 +22 42 49.2 0 16.520 15.949 15.320 14.714 14.349 14.347 3.12 2.26 9.87 2.26 2.53 NM L07_PM_NM_7 GCS9 +03 44 44.62 +22 49 10.5 0 19.674 19.358 18.763 18.140 18.089 0.100 0.73 5.90 -8.60 5.90 2.55 NM int-pl-IZ-52;IPLJ0344446+224911_N GCS9 Cl* Melotte 22 IPL 52 +03 44 50.99 +25 21 43.5 0 19.424 18.545 17.899 17.322 16.856 16.853 4.78 3.88 -9.91 3.88 4.18 NM L07_262 GCS9 +03 44 58.97 +23 23 19.9 0 11.820 11.684 11.266 11.507 10.629 10.890 19.95 2.23 -34.31 2.23 2.42 NM HII513_DH364 GCS9 Cl* Melotte 22 HII 513 +03 45 12.28 +22 58 31.8 0 14.301 14.092 13.643 13.024 12.948 12.939 17.40 2.24 -40.22 2.24 3.39 NM L07_188 GCS9 +03 45 23.81 +21 52 38.7 0 15.066 14.597 14.011 13.441 13.158 13.108 12.19 2.27 -5.33 2.27 2.63 NM BPL72 GCS9 Cl* Melotte 22 BPL 72 +03 45 25.31 +22 22 07.8 0 19.833 18.841 18.030 17.345 16.867 0.125 0.66 4.90 -0.37 4.90 2.84 NM int-pl-IZ-68;IPLJ0345252+222208_N GCS9 Cl* Melotte 22 IPL 68 +03 45 28.34 +23 48 09.6 0 17.089 16.378 15.577 14.726 14.303 14.314 12.66 2.24 -4.80 2.24 4.43 NM L07_PM_NM_8 GCS9 +03 45 29.87 +22 24 14.5 0 16.385 15.797 15.183 14.585 14.229 14.242 60.29 2.30 -74.75 2.30 16.18 NM BPL74_L07_PM_NM_9 GCS9 Cl* Melotte 22 BPL 74 +03 45 33.16 +25 34 30.0 0 18.115 17.203 16.409 15.847 15.398 15.847 -6.60 2.43 -17.46 2.43 3.65 NM CFHT-Pl-19_L07_photNM_6 GCS9 Cl* Melotte 22 CFHT 19 +03 45 34.50 +23 41 43.5 0 17.069 16.371 15.611 14.865 14.455 14.451 -12.61 2.24 -23.80 2.24 3.02 NM L07_PM_NM_10 GCS9 +03 45 36.44 +24 18 15.4 0 16.225 15.689 15.049 14.432 14.104 14.091 9.33 2.24 -4.26 2.24 4.09 NM L07_PM_NM_11 GCS9 +03 45 36.50 +22 28 31.9 0 19.069 18.388 17.472 17.000 16.703 0.064 -24.55 3.48 -17.14 3.48 10.20 NM int-pl-IZ-66;IPLJ0345365+222832_N GCS9 Cl* Melotte 22 IPL 66 +03 45 36.85 +23 44 47.8 0 16.877 16.225 15.470 14.619 14.255 14.237 18.08 2.24 0.32 2.24 17.83 NM L07_PM_NM_12 GCS9 +03 45 37.25 +23 49 21.0 0 16.385 15.847 15.119 14.210 13.883 13.880 -6.65 2.23 -8.20 2.23 1.69 NM L07_PM_NM_13 GCS9 +03 45 43.11 +25 40 23.1 0 16.198 15.009 13.924 13.240 12.663 12.643 -96.92 2.23 -40.65 2.23 2.96 NM L07_PM_NM_14 GCS9 +03 45 45.25 +22 58 44.6 0 16.700 16.061 15.359 14.836 14.433 0.010 57.50 2.26 -53.09 2.26 10.93 NM int-pl-IZ-58;2MASSJ0345452+225845_BPL76_Y GCS9 Cl* Melotte 22 IPL 58 +03 45 49.90 +23 45 59.8 0 16.226 15.601 14.814 13.904 13.593 13.583 0.36 2.23 -2.57 2.23 0.62 NM L07_PM_NM_15 GCS9 +03 45 52.65 +25 51 42.0 0 12.679 12.343 11.821 11.385 10.925 11.124 122.72 2.23 -53.12 2.23 13.46 NM HCG199_SK500_MT61_DH406 GCS9 Cl* Melotte 22 HCG 199 +03 45 53.20 +25 12 55.8 0 17.551 16.776 16.002 15.368 14.932 14.952 -4.73 2.27 -12.06 2.27 5.27 NM BPL81_L07_A1_37 GCS9 Cl* Melotte 22 BPL 81 +03 46 02.52 +23 45 33.2 0 18.177 17.368 16.459 15.481 15.025 15.026 -1.56 2.31 1.54 2.31 6.68 NM L07_A1_39 GCS9 +03 46 03.75 +23 44 35.6 0 18.178 17.270 16.413 15.556 15.053 15.070 -10.12 2.30 -4.53 2.30 21.32 NM L07_A1_40 GCS9 +03 46 04.92 +22 15 40.2 0 17.472 16.679 15.925 15.296 15.057 15.166 0.19 2.39 -8.27 2.39 2.60 NM L07_photNM_7 GCS9 +03 46 08.02 +23 45 35.5 0 18.882 17.941 16.854 15.857 15.336 15.348 -12.84 2.37 2.06 2.37 2.22 NM L07_A1_41 GCS9 +03 46 08.22 +23 21 38.7 0 17.105 16.500 15.752 15.026 14.646 14.651 2.05 2.28 -10.76 2.28 3.97 NM L07_PM_NM_16 GCS9 +03 46 08.37 +23 20 50.8 0 11.765 11.589 11.200 11.417 10.482 10.755 22.70 2.23 -33.02 2.23 6.52 NM HII915_HCG215 GCS9 HII915 +03 46 14.34 +23 51 02.4 0 15.627 14.918 14.181 13.826 13.458 13.484 1.83 2.22 13.02 2.22 301.52 NM HHJ56_DH432 GCS9 Cl* Melotte 22 HHJ 56 +03 46 14.48 +22 20 51.0 0 16.717 16.105 15.473 15.002 14.615 0.011 66.01 2.32 -69.67 2.32 3.61 NM int-pl-IZ-63_2MASSJ0346144+222051_N_L07_PM_NM_17 GCS9 Cl* Melotte 22 IPL 63 +03 46 26.75 +24 49 18.1 0 16.284 15.730 15.129 14.552 14.213 14.224 -2.42 2.22 -18.50 2.22 17.98 NM L07_PM_NM_18 GCS9 +03 46 32.99 +23 38 00.9 0 16.598 16.068 15.371 14.454 14.143 14.105 4.67 2.24 2.45 2.24 3.95 NM L07_PM_NM_19 GCS9 +03 46 36.82 +23 33 01.8 0 16.694 16.058 15.333 14.498 14.128 14.125 -2.77 2.24 -15.45 2.24 1.91 NM L07_PM_NM_21 GCS9 +03 46 38.36 +25 33 18.6 0 15.621 15.312 14.788 14.129 13.931 13.905 19.87 2.24 -46.62 2.24 9.86 NM L07_33 GCS9 +03 46 40.95 +22 22 38.1 0 19.242 18.150 16.917 16.183 15.497 15.480 69.74 2.66 -50.74 2.66 3.71 NM L07_A1_52 GCS9 +03 46 44.04 +23 38 13.4 0 17.173 16.531 15.775 14.937 14.576 14.555 6.12 2.26 -11.45 2.26 9.02 NM L07_PM_NM_22 GCS9 +03 46 48.31 +24 18 06.2 0 13.266 13.009 12.529 11.953 11.752 11.731 -12.75 2.22 2.02 2.22 6.44 NM HCG236_J311 GCS9 Cl* Melotte 22 HCG 236 +03 46 50.99 +25 40 44.5 0 16.272 15.767 15.181 14.488 14.196 14.200 -5.63 2.26 -22.81 2.26 3.53 NM L07_PM_NM_23 GCS9 +03 46 52.58 +24 17 17.0 0 15.299 14.842 14.265 13.643 13.345 13.340 20.07 2.22 1.29 2.22 10.34 NM BPL111 GCS9 Cl* Melotte 22 BPL 111 +03 47 02.12 +25 54 36.6 0 13.774 13.591 13.184 12.619 12.512 12.525 10.77 2.23 -49.85 2.23 2.44 NM L07_4 GCS9 +03 47 02.54 +25 13 45.5 0 16.469 16.185 15.655 14.946 14.723 14.728 4.07 2.25 -9.76 2.25 1.92 NM BPL123 GCS9 Cl* Melotte 22 BPL 123 +03 47 03.56 +24 49 11.6 0 13.153 13.091 11.335 12.348 10.073 NM HII1266_HD23567_Tr359b GCS9 HII1266 +03 47 05.82 +23 24 52.5 0 19.481 19.105 18.389 17.632 17.262 17.393 -2.31 4.89 5.06 4.89 3.79 NM Roque32 GCS9 Cl* Melotte 22 Roque 32 +03 47 06.65 +24 45 47.4 0 16.069 15.513 14.959 14.428 14.100 14.105 59.57 2.23 27.45 2.23 12.46 NM L07_PM_NM_25 GCS9 +03 47 07.73 +24 21 40.5 0 15.473 15.016 14.402 13.829 13.534 13.532 -25.04 2.22 -2.14 2.22 11.43 NM JS9 GCS9 Cl* Melotte 22 JS 9 +03 47 08.31 +22 33 10.0 0 16.577 15.993 15.342 14.844 14.453 0.010 -15.97 2.31 -6.87 2.31 5.75 NM int-pl-IZ-38;2MASSJ0347082+223309_BPL127_Y_L07_PM_NM_26 GCS9 Cl* Melotte 22 IPL 38 +03 47 13.69 +23 46 28.4 0 16.410 15.813 15.184 14.658 14.309 14.311 -22.45 2.24 11.13 2.24 31.03 NM L07_PM_NM_27 GCS9 +03 47 24.41 +23 54 52.7 0 13.573 13.598 11.481 12.458 10.127 10.989 -61.87 2.22 -1.72 2.22 12.56 NM HII1397_HD23631_Tr402 GCS9 HII1397 +03 47 30.66 +25 13 30.6 0 16.074 15.584 14.998 14.473 14.158 14.126 48.31 2.22 -52.27 2.22 3.66 NM BPL146 GCS9 Cl* Melotte 22 BPL 146 +03 47 36.27 +24 28 50.1 0 16.052 15.566 14.943 14.294 13.957 13.957 -9.42 2.22 -2.90 2.22 3.25 NM L07_PM_NM_28 GCS9 +03 47 37.55 +24 28 59.0 0 19.855 19.463 18.773 18.225 18.131 17.930 -4.03 5.91 20.75 5.91 1.09 NM Roque34 GCS9 Cl* Melotte 22 Roque 34 +03 47 38.75 +22 38 40.3 0 17.442 16.864 16.220 15.689 15.285 15.282 -23.29 2.39 -31.80 2.39 4.85 NM Roque44 GCS9 Cl* Melotte 22 Roque 44 +03 47 41.19 +23 44 24.9 0 11.990 11.816 11.404 11.437 10.714 11.008 21.37 2.22 -39.05 2.22 12.59 NM HII1532_HCG286_SK405_B270_DH531 GCS9 HII1532 +03 47 46.16 +25 21 42.1 0 19.442 18.364 17.367 16.890 16.342 16.383 69.70 3.46 -102.93 3.46 5.96 NM L07_photNM_8 GCS9 +03 47 46.90 +24 03 40.5 0 20.021 20.270 19.206 18.506 18.007 18.106 -3.67 5.35 0.47 5.35 4.33 NM Roque27 GCS9 Cl* Melotte 22 Roque 27 +03 48 02.09 +24 00 02.5 0 18.921 18.465 17.869 17.284 16.982 17.006 1.27 3.24 -5.14 3.24 1.15 NM Roque10 GCS9 Cl* Melotte 22 Roque 10 +03 48 03.68 +23 44 10.4 0 15.989 15.099 14.329 13.709 13.277 13.278 32.62 2.22 -124.76 2.22 0.68 NM NOT1 GCS9 Cl* Melotte 22 NOT 1 +03 48 04.74 +23 51 01.8 0 20.362 19.516 19.084 18.521 17.969 18.210 11.09 6.08 5.08 6.08 1.36 NM Festin98_009 GCS9 Cl* Melotte 22 NPL 009 +03 48 13.80 +24 28 04.0 0 17.517 16.753 16.083 15.485 15.072 15.099 1.43 2.31 -3.33 2.31 2.70 NM Roque43 GCS9 Cl* Melotte 22 Roque 43 +03 48 13.93 +24 38 30.5 0 16.849 16.681 16.451 16.076 15.939 15.998 -2.88 2.65 1.72 2.65 5.01 NM BPL168 GCS9 Cl* Melotte 22 BPL 168 +03 48 23.62 +24 22 35.2 0 16.199 15.558 14.889 14.317 13.945 13.947 -10.86 2.23 -14.81 2.23 2.24 NM BPL177_Festin98_004_L07_PM_NM_29 GCS9 Cl* Melotte 22 BPL 177 +03 48 25.61 +22 52 13.0 0 15.518 15.386 15.395 14.708 14.646 14.641 11.27 2.17 -37.83 2.17 450.04 NM BPL179 GCS9 Cl* Melotte 22 BPL 179 +03 48 30.10 +24 20 43.9 0 14.608 14.578 12.604 14.861 11.050 12.051 -94.69 2.22 157.47 2.22 6.88 NM HII1876_HD23763_Tr518 GCS9 HII1876 +03 48 32.39 +24 13 18.4 0 20.066 19.095 18.425 17.638 17.303 17.351 2.98 3.77 -23.67 3.77 2.76 NM Festin98_010 GCS9 Cl* Melotte 22 NPL 010 +03 48 32.67 +23 52 40.6 0 14.722 14.278 13.711 13.109 12.832 12.842 0.96 2.22 -5.44 2.22 5.32 NM WILL1 GCS9 Cl* Melotte 22 WBM 1 +03 48 36.30 +23 33 25.3 0 16.077 15.589 15.028 14.489 14.152 14.167 -14.94 2.23 -33.20 2.23 1.87 NM L07_PM_NM_30 GCS9 +03 48 44.14 +24 21 18.7 0 17.816 17.195 16.443 15.823 15.448 15.431 2.52 2.33 -8.88 2.33 4.62 NM Roque37 GCS9 Cl* Melotte 22 Roque 37 +03 48 48.45 +21 59 00.3 0 16.559 15.982 15.348 14.816 14.470 14.458 -16.99 2.31 5.11 2.31 5.45 NM L07_PM_NM_31 GCS9 +03 48 49.03 +24 20 25.3 0 19.222 18.227 17.237 16.569 16.059 16.074 -33.18 2.59 -58.05 2.59 4.06 NM Roque33_Festin98_008_L07_photNM_9 GCS9 Cl* Melotte 22 Roque 33 +03 48 55.23 +22 50 42.2 0 14.915 14.474 13.893 13.361 13.070 13.047 4.68 2.13 -22.37 2.13 1.12 NM BPL194 GCS9 Cl* Melotte 22 BPL 194 +03 48 58.61 +23 37 03.9 0 19.604 18.306 17.390 16.771 16.233 16.221 6.50 2.60 -15.01 2.60 4.76 NM L07_photNM_10 GCS9 +03 48 58.96 +25 06 14.0 0 11.958 11.762 11.365 11.410 10.602 10.877 7.12 2.20 -45.83 2.20 1.85 NM HII2082 GCS9 HII2082 +03 49 11.59 +24 26 17.4 0 16.051 15.500 14.910 14.436 14.081 14.097 33.46 2.21 -90.64 2.21 2.53 NM L07_PM_NM_32 GCS9 +03 49 12.12 +23 12 55.9 0 16.875 16.165 15.429 14.824 14.414 14.401 53.85 2.28 -0.69 2.28 4.85 NM L07_PM_NM_33 GCS9 +03 49 33.05 +26 50 43.0 0 16.972 16.327 15.617 15.078 14.687 14.684 28.54 2.99 9.02 2.99 0.42 NM IPMBD22 GCS9 Cl* Melotte 22 IPMBD 22 +03 49 45.95 +22 53 43.7 0 15.830 15.510 15.011 14.332 14.144 14.153 13.11 2.27 -37.98 2.27 1.52 NM L07_186 GCS9 +03 49 51.23 +25 26 06.6 0 19.731 18.569 17.609 17.074 16.515 16.545 2.12 3.45 13.47 3.45 1.53 NM L07_photNM_13 GCS9 +03 49 53.76 +23 59 01.4 0 17.271 16.739 16.088 15.537 15.167 15.153 15.94 2.29 -9.47 2.29 31.41 NM Roque46 GCS9 Cl* Melotte 22 Roque 46 +03 50 00.31 +24 28 15.5 0 18.288 17.707 17.006 16.494 16.087 16.072 2.01 2.60 -7.24 2.60 4.36 NM Roque40 GCS9 Cl* Melotte 22 Roque 40 +03 50 01.67 +24 36 48.6 0 15.586 15.002 14.433 13.921 13.543 13.549 5.46 2.21 -6.13 2.21 12.12 NM BPL221 GCS9 Cl* Melotte 22 BPL 221 +03 50 03.90 +23 58 34.1 0 14.131 13.765 13.259 12.738 12.457 12.446 -8.17 2.22 -11.58 2.22 19.96 NM HHJ256 GCS9 Cl* Melotte 22 HHJ 256 +03 50 05.96 +23 42 14.1 0 19.805 19.357 18.931 18.260 17.904 17.940 -0.52 5.77 8.42 5.77 5.36 NM Roque29 GCS9 Cl* Melotte 22 Roque 29 +03 50 15.55 +26 06 30.0 0 16.909 16.185 15.415 14.832 14.403 14.396 40.58 2.28 -66.43 2.28 2.93 NM L07_PM_NM_34 GCS9 +03 50 20.77 +24 08 40.9 0 19.627 19.216 18.782 18.095 17.744 17.820 -0.38 5.11 4.55 5.11 1.62 NM Roque28 GCS9 Cl* Melotte 22 Roque 28 +03 50 41.10 +25 44 21.6 0 15.634 15.517 15.188 14.817 14.704 14.716 -8.19 2.27 -10.74 2.27 17.17 NM L07_faintJK_1 GCS9 +03 51 04.05 +24 32 57.8 0 16.030 15.535 14.962 14.396 14.049 14.060 76.40 2.22 -22.07 2.22 9.89 NM L07_PM_NM_35 GCS9 +03 51 05.58 +24 44 12.2 0 11.945 11.736 11.358 11.489 10.568 10.909 20.38 2.20 -47.92 2.20 5.38 NM HII2927_HCG418_DH693 GCS9 HII2927 +03 51 09.72 +25 18 52.4 0 16.714 16.037 15.393 14.875 14.493 14.534 -18.86 2.28 -30.02 2.28 14.71 NM L07_PM_NM_52 GCS9 +03 51 10.52 +22 48 14.4 0 16.768 16.141 15.471 14.799 14.434 14.413 -4.75 2.28 -21.83 2.28 1.24 NM L07_PM_NM_36 GCS9 +03 51 22.47 +23 42 19.2 0 13.909 13.760 13.410 12.975 12.869 12.863 2.24 2.22 -6.49 2.22 1.31 NM BPL234 GCS9 Cl* Melotte 22 BPL 234 +03 51 27.90 +22 48 12.9 0 16.113 15.431 14.734 14.118 13.705 13.722 11.13 2.26 -12.61 2.26 4.23 NM L07_PM_NM_37 GCS9 +03 51 35.03 +24 03 34.8 0 16.774 16.625 16.231 15.758 15.586 15.587 -6.66 2.32 -6.66 2.32 40.44 NM L07_faintJK_4 GCS9 +03 51 37.72 +25 42 46.5 0 19.137 17.784 16.352 15.461 14.660 14.657 55.25 2.37 -38.67 2.37 4.06 NM L07_photNM_14 GCS9 +03 51 38.18 +23 03 11.2 0 16.892 16.218 15.549 14.978 14.588 14.593 -5.14 2.31 40.41 2.31 5.15 NM L07_PM_NM_38 GCS9 +03 51 53.92 +24 02 51.4 0 13.571 13.114 13.213 12.035 11.680 11.721 -34.06 2.22 -22.59 2.22 165.02 NM HCG441_SK211_BPL243_DH726_Moraux2003_17 GCS9 Cl* Melotte 22 HCG 441 +03 51 57.12 +24 57 06.3 0 16.193 15.875 15.320 14.797 14.498 14.480 15.09 2.23 -31.04 2.23 6.23 NM DH729 GCS9 Cl* Melotte 22 DH 729 +03 52 01.58 +24 20 11.7 0 16.260 15.770 15.166 14.508 14.197 14.223 4.89 2.26 -3.61 2.26 3.40 NM BPL247 GCS9 Cl* Melotte 22 BPL 247 +03 52 07.88 +23 59 13.0 0 16.169 15.371 14.636 14.066 13.640 14.066 18.40 2.25 -115.67 2.25 1.33 NM CFHT-Pl-6_BRB3_L07_PM_NM_39 GCS9 Cl* Melotte 22 CFHT 6 +03 52 13.44 +24 28 52.3 0 19.996 18.601 17.754 17.227 16.636 16.681 7.13 3.47 -14.21 3.47 2.49 NM L07_photNM_16 GCS9 +03 52 17.49 +22 51 01.9 0 16.649 16.084 15.415 14.781 14.414 14.402 4.92 2.30 -6.97 2.30 1.46 NM L07_PM_NM_40 GCS9 +03 52 24.00 +23 55 15.7 0 14.588 14.438 14.048 13.571 13.477 13.478 14.07 2.25 -36.84 2.25 1.05 NM DH2004_751 GCS9 Cl* Melotte 22 DH 751 +03 52 44.31 +24 14 13.2 0 13.520 13.343 12.946 12.457 12.359 12.354 19.94 2.24 -11.03 2.24 7.05 NM L07_8 GCS9 +03 52 46.44 +24 24 17.1 0 19.772 18.591 17.569 16.949 16.281 16.359 -3.07 2.92 -13.02 2.92 18.30 NM L07_photNM_17 GCS9 +03 52 47.18 +22 38 44.1 0 17.260 16.818 16.156 15.566 15.252 15.282 -40.26 2.44 15.01 2.44 273.74 NM L07_faintJK_10 GCS9 +03 52 49.69 +25 00 50.7 0 13.101 12.909 12.566 12.095 11.969 11.985 -1.51 2.23 -0.95 2.23 2.51 NM BPL271 GCS9 Cl* Melotte 22 BPL 271 +03 52 52.11 +25 10 26.0 0 13.187 12.804 12.306 11.803 11.485 11.517 43.14 2.23 -67.75 2.23 5.68 NM BPL273_SK152 GCS9 Cl* Melotte 22 BPL 273 +03 52 58.25 +23 26 19.1 0 13.562 13.427 13.136 12.789 12.721 12.722 7.00 2.25 1.17 2.25 5.43 NM BPL276 GCS9 Cl* Melotte 22 BPL 276 +03 53 21.62 +23 58 53.9 0 13.493 13.135 12.644 12.051 11.822 47.53 2.30 -56.56 2.30 1.90 NM SK132_DH782 GCS9 Cl* Melotte 22 SK 132 +03 53 23.13 +23 19 20.4 0 17.823 16.897 15.966 15.341 14.850 14.834 21.87 2.36 -4.56 2.36 1.54 NM BPL283_CFHT-Pl-18_M8_L07_A1_104 GCS9 Cl* Melotte 22 BPL 283 +03 53 23.37 +25 05 50.0 0 16.629 16.085 15.495 15.014 14.656 14.648 43.26 2.28 -95.99 2.28 1.97 NM BPL284 GCS9 Cl* Melotte 22 BPL 284 +03 53 34.55 +24 04 38.4 0 16.740 16.177 15.552 14.979 14.606 14.22 2.35 9.30 2.35 1.46 NM BPL287 GCS9 Cl* Melotte 22 BPL 287 +03 53 48.72 +25 04 20.1 0 16.777 16.308 15.238 15.126 14.827 14.820 -6.07 2.28 16.38 2.28 119.28 NM L07_PM_NM_41 GCS9 +03 53 59.92 +23 41 00.2 0 16.200 15.677 15.073 14.485 14.141 14.149 -0.43 2.26 -8.06 2.26 1.04 NM BPL296_L07_PM_NM_42 GCS9 Cl* Melotte 22 BPL 296 +03 54 01.40 +24 44 46.7 0 19.768 18.534 17.775 17.194 16.809 17.194 3.91 3.31 -23.74 3.31 1.84 NM CFHT-Pl-26_XX GCS9 Cl* Melotte 22 CFHT 26 +03 54 15.64 +25 21 18.9 0 15.714 15.104 14.458 13.881 13.530 13.501 1.39 2.95 -13.24 2.95 0.52 NM BPL309 GCS9 Cl* Melotte 22 BPL 309 +03 54 17.46 +23 11 56.9 0 16.231 15.688 15.035 14.453 14.100 14.085 -12.92 2.28 -3.82 2.28 10.54 NM L07_PM_NM_43 GCS9 +03 54 39.33 +23 03 12.4 0 16.447 15.693 14.998 14.476 14.064 14.083 6.47 2.28 -4.39 2.28 6.56 NM L07_PM_NM_44 GCS9 +03 54 44.20 +25 15 10.9 0 17.428 16.693 15.954 15.421 15.043 15.031 -6.19 2.31 -33.42 2.31 4.31 NM BPL316 GCS9 Cl* Melotte 22 BPL 316 +03 54 45.87 +22 53 54.8 0 17.409 16.961 16.437 15.849 15.518 15.619 17.05 2.56 -18.44 2.56 4.02 NM DH807 GCS9 Cl* Melotte 22 DH 807 +03 54 46.11 +23 00 20.6 0 17.056 16.404 15.700 15.125 14.741 14.732 -17.41 2.33 -4.76 2.33 7.01 NM L07_PM_NM_46 GCS9 +03 54 49.71 +25 06 24.2 0 13.354 13.219 12.850 12.393 12.309 12.312 -1.04 2.23 -7.78 2.23 6.31 NM BPL319 GCS9 Cl* Melotte 22 BPL 319 +03 54 51.47 +23 45 12.2 0 19.670 18.540 17.687 17.122 16.691 16.585 56.85 3.56 -24.59 3.56 2.56 NM BRB19_None GCS9 Cl* Melotte 22 BRB 19 +03 54 54.51 +22 50 21.5 0 16.464 15.871 15.230 14.673 14.333 14.338 2.56 2.30 -7.22 2.30 4.32 NM L07_PM_NM_47 GCS9 +03 54 55.74 +25 09 04.2 0 14.981 14.562 14.004 13.425 13.130 13.141 17.22 2.23 -9.40 2.23 12.41 NM BPL320 GCS9 Cl* Melotte 22 BPL 320 +03 54 55.92 +24 37 42.9 0 19.397 19.014 18.467 17.790 17.603 17.582 6.11 5.79 -3.45 5.79 6.15 NM L07_photNM_18 GCS9 +03 55 03.43 +24 28 54.6 0 19.678 18.842 18.141 17.524 17.349 17.331 3.23 4.67 -16.09 4.67 6.30 NM L07_257 GCS9 +03 55 06.14 +25 11 06.2 0 15.549 15.000 14.339 13.790 13.431 13.470 0.47 2.24 -5.03 2.24 4.70 NM BPL323 GCS9 Cl* Melotte 22 BPL 323 +03 55 08.19 +23 58 08.6 0 19.348 18.984 18.417 17.822 17.551 -0.94 4.78 -14.24 4.78 2.60 NM L07_faintYJ_30 GCS9 +03 55 12.61 +23 17 37.2 0 17.842 16.877 15.977 15.348 14.842 15.348 54.40 2.33 -48.46 2.33 8.41 NM CFHT-Pl-15_M7.0_BRB13_L07_A1_109 GCS9 Cl* Melotte 22 CFHT 15 +03 55 14.72 +22 42 08.5 0 16.652 16.043 15.366 14.734 14.395 14.359 1.47 2.29 -7.33 2.29 9.73 NM L07_PM_NM_53 GCS9 +03 55 18.11 +24 17 05.7 0 16.325 15.757 15.111 14.541 14.192 14.208 15.89 2.26 -2.19 2.26 10.94 NM BPL326_L07_A1_110 GCS9 Cl* Melotte 22 BPL 326 +03 55 19.92 +24 12 13.9 0 15.750 15.298 14.699 14.128 13.819 13.821 -7.38 2.25 -20.24 2.25 5.61 NM L07_106 GCS9 +03 55 30.07 +23 54 53.5 0 16.503 15.930 15.308 14.808 14.429 14.420 -15.28 2.27 -4.94 2.27 2.49 NM L07_PM_NM_48 GCS9 +03 55 39.59 +24 12 50.7 0 20.511 19.251 17.844 17.303 16.618 16.648 28.07 3.37 -158.64 3.37 1.11 NM L07_photNM_19 GCS9 +03 55 42.02 +22 57 01.4 0 18.646 17.343 15.943 14.999 14.186 14.191 161.10 2.34 -44.49 2.34 2.38 NM L07_photNM_20 GCS9 +03 55 45.49 +23 51 25.4 0 20.033 19.742 18.723 18.580 18.088 17.959 -5.63 8.56 -13.56 8.56 1.63 NM L07_faintJK_5 GCS9 +03 55 46.37 +23 21 16.0 0 13.188 12.908 12.386 11.820 11.587 11.619 18.86 2.25 -3.24 2.25 4.59 NM SK40 GCS9 Cl* Melotte 22 SK 40 +03 56 21.72 +25 21 10.4 0 16.646 15.945 15.246 14.665 14.234 14.261 4.82 2.53 -4.34 2.53 1.00 NM BPL336 GCS9 Cl* Melotte 22 BPL 336 +03 56 34.22 +24 15 13.2 0 17.385 16.926 16.372 15.806 15.535 15.527 12.43 3.15 -41.24 3.15 0.86 NM DH838 GCS9 Cl* Melotte 22 DH 838 +03 56 36.20 +25 18 05.7 0 16.008 15.372 14.712 14.140 13.761 13.809 8.47 2.50 -13.23 2.50 0.59 NM BPL337 GCS9 Cl* Melotte 22 BPL 337 +03 56 51.33 +25 02 25.4 0 13.297 12.918 12.362 11.804 11.466 11.541 28.86 2.47 0.62 2.47 0.95 NM BPL339 GCS9 Cl* Melotte 22 BPL 339 +03 56 51.90 +23 31 36.6 0 18.325 17.973 17.367 16.753 16.502 16.419 -3.97 3.81 -6.61 3.81 1.07 NM DH2004_844 GCS9 Cl* Melotte 22 DH 844 +03 57 58.45 +24 20 08.9 0 17.341 16.910 16.301 15.694 15.391 15.376 -2.76 3.11 -15.77 3.11 0.84 NM DH861 GCS9 Cl* Melotte 22 DH 861 +03 58 56.42 +24 18 32.2 0 14.148 13.730 13.145 12.585 12.404 12.406 61.58 2.96 -48.24 2.96 45.45 NM HHJ292_DH869 GCS9 Cl* Melotte 22 HHJ 292 +04 00 19.40 +24 41 05.0 0 18.236 17.805 17.236 16.704 16.312 16.366 2.20 3.49 -13.79 3.49 1.46 NM DH2004_890 GCS9 Cl* Melotte 22 DH 890 +03 32 33.72 +24 29 34.7 0 13.066 12.465 12.179 13.40 6.88 -33.70 6.88 4.83 noZY DH021 GCS9 Cl* Melotte 22 DH 021 +03 32 42.31 +23 34 00.0 0 13.393 12.828 12.539 12.522 50.10 2.64 -42.29 2.64 98.04 noZY DH022 GCS9 Cl* Melotte 22 DH 022 +03 34 22.13 +27 23 45.3 0 12.594 12.061 11.758 11.766 19.10 4.62 -39.68 4.62 1.84 noZY DH035 GCS9 Cl* Melotte 22 DH 035 +03 34 46.37 +27 55 32.4 0 12.506 11.899 11.620 11.663 17.73 4.62 -33.95 4.62 0.60 noZY DH038 GCS9 Cl* Melotte 22 DH 038 +03 43 59.09 +22 54 29.0 0 19.298 18.365 18.018 22.78 9.41 4.51 9.41 3.41 noZY int-pl-IZ-56;IPLJ0343590+225429_N GCS9 Cl* Melotte 22 IPL 56 +03 44 19.50 +22 39 03.5 0 18.709 18.333 17.679 17.927 28.27 8.54 -3.53 8.54 9.76 noZY L07_faintJK_8 GCS9 +03 44 27.29 +25 44 41.7 0 18.818 17.787 16.965 16.902 8.52 5.25 -26.68 5.25 1.95 noZY BRB27_CFHT-PLIZ1262_PLZJ32 GCS9 Cl* Melotte 22 BRB 27 +03 45 35.26 +23 36 39.6 0 18.891 18.394 18.140 18.489 25.34 8.42 14.07 8.42 6.64 noZY L07_faintJK_7 GCS9 +03 47 05.68 +20 00 37.0 0 14.107 13.553 13.218 13.230 21.25 2.66 -50.47 2.66 2.67 noZY DH492 GCS9 Cl* Melotte 22 DH 492 +03 47 51.19 +25 26 57.9 0 18.212 17.780 17.630 9.00 noZY L07_faintJK_12 GCS9 +03 48 11.42 +20 46 42.4 0 13.091 12.550 12.217 12.230 18.39 2.65 -43.76 2.65 4.29 noZY DH558 GCS9 Cl* Melotte 22 DH 558 +03 48 32.69 +25 06 05.1 0 18.548 17.996 17.438 17.609 0.60 5.00 3.81 5.00 2.60 noZY L07_faintJK_2 GCS9 +03 48 55.41 +24 20 09.6 0 19.996 19.199 18.367 18.093 -21.91 11.56 22.88 11.56 0.29 noY Roque19 GCS9 Cl* Melotte 22 Roque 19 +03 49 09.15 +24 19 59.9 0 16.407 15.807 15.534 15.463 16.82 2.45 -9.31 2.45 0.38 noZY 18.37 GCS9 Cl* Melotte 22 MHOBD 2 +03 49 11.04 +24 20 51.1 0 12.979 12.444 12.146 12.154 16.62 2.33 -46.70 2.33 1.45 noZY HCG355_HHJ287_BPL199_DH604 GCS9 Cl* Melotte 22 HCG 355 +03 49 11.42 +24 14 24.4 0 13.693 13.150 12.839 12.818 20.72 2.34 -43.10 2.34 1.94 noZY BPL200_DH606 GCS9 Cl* Melotte 22 BPL 200 +03 49 12.20 +23 53 12.2 0 11.727 14.085 10.383 11.580 5.42 2.33 -47.80 2.33 40.02 noZY HII2195_HD23863_Tr607 GCS9 HII2195 +03 49 12.52 +24 11 12.8 0 16.410 15.750 15.245 15.233 14.85 2.44 -40.77 2.44 1.58 noZY BPL201_L07_A1_84 GCS9 Cl* Melotte 22 BPL 201 +03 49 16.79 +24 23 45.7 0 11.577 12.188 10.276 10.839 -151.15 2.33 -101.06 2.33 28.91 noZY HII2220_HD23872_Tr613 GCS9 HII2220 +03 49 18.66 +23 46 48.8 0 14.294 13.665 13.393 13.394 15.38 2.34 -44.61 2.34 0.64 noZY DH2004_610 GCS9 Cl* Melotte 22 DH 610 +03 49 21.76 +24 22 51.2 0 12.746 17.742 11.121 25.15 2.33 -49.52 2.33 24.37 noZY HII2263_HD23873_Tr622 GCS9 HII2263 +03 49 32.54 +23 55 42.4 0 13.238 12.702 12.389 12.412 14.17 2.33 -43.14 2.33 0.80 noZY HCG371_SK310_HHJ221_DH627 GCS9 Cl* Melotte 22 HCG 371 +03 49 33.12 +24 13 04.6 0 14.583 14.051 13.701 13.694 19.96 2.34 -41.58 2.34 2.40 noZY BPL210 GCS9 Cl* Melotte 22 BPL 210 +03 49 36.12 +23 56 23.1 0 13.112 12.569 12.282 12.268 16.38 2.33 -43.47 2.33 1.74 noZY HCG373_SK307_HHJ286_DH632 GCS9 Cl* Melotte 22 HCG 373 +03 49 36.53 +24 18 14.0 0 13.243 12.679 12.382 12.419 13.89 2.34 -45.09 2.34 6.76 noZY HCG375_HHJ250_BPL212_DH634 GCS9 Cl* Melotte 22 HCG 375 +03 49 56.60 +24 20 56.3 0 11.532 12.226 10.215 11.260 27.98 2.33 -35.54 2.33 12.96 noZY HII2488_HD23948_Tr688 GCS9 HII2488 +03 50 58.17 +23 55 42.5 0 13.574 13.042 12.728 12.708 21.42 2.34 -47.56 2.34 23.03 noZY HCG414_DH690 GCS9 Cl* Melotte 22 HCG 414 +03 51 11.54 +24 23 13.0 0 12.113 11.631 11.289 11.323 13.08 2.33 -44.19 2.33 0.66 noZY HCG422_SK245_DH697_Moraux2003_1 GCS9 Cl* Melotte 22 HCG 422 +03 51 12.08 +23 55 57.2 0 11.712 11.710 10.948 11.183 25.68 2.33 -44.30 2.33 3.63 noZY HII2966_SK244_DH698 GCS9 HII2966 +03 51 18.22 +24 20 26.0 0 13.809 13.281 13.036 13.013 48.24 2.34 -48.37 2.34 4.66 noZY SK236 GCS9 Cl* Melotte 22 SK 236 +03 51 20.13 +23 45 17.9 0 19.366 18.614 17.836 18.128 -20.62 7.01 -49.89 7.01 3.15 noZY BRB33_None GCS9 Cl* Melotte 22 BRB 33 +03 51 21.34 +23 52 08.5 0 14.111 13.774 13.735 13.701 -2.74 2.34 -4.49 2.34 5.70 noZY Calar7_XX.X GCS9 Cl* Melotte 22 CALAR 7 +03 51 25.35 +23 53 21.5 0 11.431 11.542 10.779 11.060 19.95 2.33 -43.33 2.33 6.34 noZY HCG430_SK229_DH711_BPL235_CFHT-Pl-21_M8_BRB14_CFHT-Pl-21 GCS9 Cl* Melotte 22 HCG 430 +03 51 25.98 +24 15 29.9 0 19.310 18.627 17.965 17.950 16.58 6.80 -16.12 6.80 2.25 noZY BRB31_None GCS9 Cl* Melotte 22 BRB 31 +03 51 29.95 +23 53 57.0 0 11.172 11.359 10.547 10.916 18.96 2.33 -38.73 2.33 7.81 noZY HII3063_Tr862_HCG431_DH713 GCS9 HII3063 +03 51 34.26 +23 47 49.8 0 13.874 13.299 13.025 12.997 16.21 2.34 -45.54 2.34 0.73 noZY BPL236_DH716_Moraux2003_71 GCS9 Cl* Melotte 22 BPL 236 +03 51 39.20 +23 51 18.1 0 13.736 13.171 12.836 12.848 13.98 2.34 -44.51 2.34 8.17 noZY BPL238_DH720 GCS9 Cl* Melotte 22 BPL 238 +03 51 42.32 +24 21 41.3 0 13.568 13.042 12.728 12.744 19.28 2.34 -41.03 2.34 3.74 noZY BPL239_DH723_Moraux2003_53 GCS9 Cl* Melotte 22 BPL 239 +03 52 54.91 +24 37 18.1 0 18.803 17.754 16.926 16.989 12.05 3.86 -46.85 3.86 2.29 noZY PLZJ37_BRB28_L07_faintJK_11 GCS9 Cl* Melotte 22 PlZJ 37 +03 54 01.44 +23 49 57.5 0 18.686 17.711 16.999 16.927 35.53 4.84 -33.95 4.84 3.15 noZY BRB29_L4.5 GCS9 Cl* Melotte 22 BRB 29 +03 54 13.41 +23 32 22.2 0 18.655 18.120 18.105 18.015 11.41 7.09 -15.53 7.09 2.97 noZY L07_faintJK_6 GCS9 +03 55 57.94 +24 41 41.6 0 18.859 18.183 18.214 17.900 -8.05 7.61 14.92 7.61 1.53 noZY L07_faintJK_3 GCS9 +03 35 18.76 +23 26 20.7 0 15.450 14.829 14.310 13.909 13.942 15.05 3.12 -38.99 3.12 1.24 noZ DH045 GCS9 Cl* Melotte 22 DH 045 +03 36 38.40 +23 08 44.2 0 14.764 14.165 13.620 13.288 13.320 25.49 3.11 -39.56 3.11 0.46 noZ DH060 GCS9 Cl* Melotte 22 DH 060 +03 42 01.68 +23 58 22.4 0 19.951 18.621 17.888 17.153 5.67 5.52 3.25 5.52 6.41 noZ int-pl-IZ-14;IPLJ0342016+235823_N GCS9 Cl* Melotte 22 IPL 14 +03 42 14.28 +22 43 02.5 0 20.062 18.517 18.045 17.495 17.568 -21.44 5.77 0.43 5.77 2.76 noZ L07_faintYJ_1 GCS9 +03 42 59.31 +25 37 38.9 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 6.35 noZ L07_faintYJ_2 GCS9 +03 43 21.40 +24 34 42.0 0 20.092 18.782 18.309 17.574 -29.69 9.19 -11.53 9.19 0.28 noZ Roque24 GCS9 Cl* Melotte 22 Roque 24 +03 43 25.89 +24 00 51.8 0 20.716 19.423 18.592 18.203 -16.04 8.54 2.59 8.54 1.10 noZ int-pl-IZ-23;IPLJ0343258+240051_N GCS9 Cl* Melotte 22 IPL 23 +03 43 26.65 +23 41 38.8 0 20.084 19.258 18.586 17.889 0.50 8.68 -12.21 8.68 1.87 noZ int-pl-IZ-4;IPLJ0343266+234139_N GCS9 Cl* Melotte 22 IPL 4 +03 43 49.93 +24 03 37.6 0 20.056 18.915 18.060 17.609 10.66 6.29 -12.02 6.29 7.85 noZ int-pl-IZ-17;IPLJ0343498+240337_N GCS9 Cl* Melotte 22 IPL 17 +03 44 00.27 +24 33 25.1 0 12.731 11.579 13.069 11.261 -135.16 3.07 159.00 3.07 1.29 noZ HII232_HD23194_Tr074 GCS9 HII232 +03 44 30.52 +24 21 17.4 0 19.589 18.397 17.705 17.310 17.563 9.90 4.99 2.04 4.99 5.70 noZ L07_faintYJ_3 GCS9 +03 44 31.28 +25 35 14.7 0 19.426 18.269 17.398 16.675 16.604 10.61 3.73 -39.40 3.73 1.93 noZ BRB22_CFHT-PLIZ2141_PLZJ61_L07_faintYJ_4 GCS9 Cl* Melotte 22 BRB 22 +03 44 47.33 +24 21 35.7 0 19.688 18.409 17.506 16.906 16.979 35.23 3.66 -31.91 3.66 3.07 noZ L07_faintYJ_5 GCS9 +03 44 49.73 +22 51 10.6 0 19.950 19.044 18.679 18.196 57.62 9.24 -32.33 9.24 2.37 noZ int-pl-IZ-51;IPLJ0344496+225111_N GCS9 Cl* Melotte 22 IPL 51 +03 44 51.70 +23 02 30.4 0 19.970 19.021 18.836 18.867 -3.01 8.34 -2.50 8.34 2.83 noZ int-pl-IZ-61;IPLJ0344516+230230_N GCS9 Cl* Melotte 22 IPL 61 +03 45 01.81 +24 04 17.7 0 20.559 19.037 18.830 18.050 18.035 34.71 7.32 -15.30 7.32 2.57 noZ L07_faintYJ_6 GCS9 +03 45 04.22 +26 40 44.6 0 14.156 13.564 13.007 12.685 12.707 21.95 3.00 -39.21 3.00 0.12 noZ HHJ185_DH371_Moraux2003_56 GCS9 Cl* Melotte 22 HHJ 185 +03 45 14.52 +22 29 28.6 0 19.756 18.383 17.410 16.493 14.00 4.20 -53.10 4.20 1.66 noZ int-pl-IZ-69;IPLJ0345144+222929_Y GCS9 Cl* Melotte 22 IPL 69 +03 45 20.05 +22 33 25.5 0 19.320 18.432 17.776 17.052 35.01 5.33 -16.01 5.33 2.52 noZ int-pl-IZ-75;IPLJ0345200+223325_N GCS9 Cl* Melotte 22 IPL 75 +03 45 27.73 +23 10 13.1 0 13.121 12.596 11.997 11.686 11.703 10.78 2.28 -31.45 2.28 3.05 noZ SK520 GCS9 Cl* Melotte 22 SK 520 +03 45 33.30 +23 34 34.3 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 1.70 noZ L07_faintYJ_7 GCS9 +03 45 33.99 +23 11 06.5 0 14.420 13.828 13.290 12.974 12.977 22.86 2.28 -40.74 2.28 3.12 noZ DH396 GCS9 Cl* Melotte 22 DH 396 +03 45 35.06 +21 56 22.2 0 19.470 18.169 17.595 17.136 17.002 -7.69 5.05 -13.84 5.05 1.35 noZ L07_faintYJ_8 GCS9 +03 45 42.26 +22 48 07.5 0 16.198 15.598 15.013 14.722 1.77 2.31 -16.21 2.31 5.74 noZ int-pl-IZ-50;2MASSJ0345422+224807_N GCS9 Cl* Melotte 22 IPL 50 +03 45 55.17 +22 51 31.0 0 16.109 15.358 14.804 14.433 14.420 14.67 2.30 -48.69 2.30 5.52 noZ int-pl-IZ-42_2MASSJ0345551+225131_Y_L07_243 GCS9 Cl* Melotte 22 IPL 42 +03 46 08.43 +22 37 57.3 0 20.002 19.462 18.771 17.791 22.24 9.40 21.27 9.40 3.53 noZ int-pl-IZ-73;IPLJ0346083+223757_N GCS9 Cl* Melotte 22 IPL 73 +03 46 09.59 +22 42 37.2 0 13.286 12.709 12.202 11.874 11.888 14.57 2.28 -37.86 2.28 3.40 noZ HCG217_SK487_HHJ297_BPL91_DH424 GCS9 Cl* Melotte 22 HCG 217 +03 46 13.85 +22 42 19.6 0 13.878 13.286 12.749 12.429 12.464 15.81 2.28 -42.30 2.28 3.16 noZ HHJ200_BPL92_DH431 GCS9 Cl* Melotte 22 HHJ 200 +03 46 27.94 +23 42 39.0 0 19.824 18.591 17.757 17.478 17.275 -7.35 4.50 -5.31 4.50 4.50 noZ L07_faintYJ_9 GCS9 +03 46 29.11 +22 59 47.6 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 1.38 noZ L07_faintYJ_10 GCS9 +03 46 43.13 +22 35 19.9 0 19.439 18.710 18.058 17.757 4.51 7.62 -19.89 7.62 4.95 noZ int-pl-IZ-40;IPLJ0346430+223520_N GCS9 Cl* Melotte 22 IPL 40 +03 46 51.06 +22 34 28.6 0 19.887 18.580 18.106 17.309 17.424 10.21 5.90 -91.85 5.90 0.99 noZ L07_faintYJ_11 GCS9 +03 47 23.86 +23 08 56.9 0 13.440 12.896 12.358 12.060 12.050 16.54 2.28 -42.81 2.28 0.72 noZ HCG270_SK427_HHJ275_DH511 GCS9 Cl* Melotte 22 HCG 270 +03 47 23.97 +22 42 37.3 0 16.123 15.354 14.809 14.402 14.361 -1.91 2.31 -9.05 2.31 13.33 noZ BPL142_int-pl-IZ-37;RPLJ0347239+22423_Roque17_Y GCS9 Cl* Melotte 22 BPL 142 +03 47 25.58 +22 41 05.7 0 16.097 15.475 14.892 14.567 50.44 2.31 -53.06 2.31 7.71 noZ int-pl-IZ-36;2MASSJ0347255+224105_N GCS9 Cl* Melotte 22 IPL 36 +03 47 34.79 +22 48 04.5 0 13.538 13.004 12.450 12.140 12.143 14.52 2.28 -42.93 2.28 3.56 noZ HCG284_HHJ278_BPL148_DH524 GCS9 Cl* Melotte 22 HCG 284 +03 47 38.47 +23 56 27.7 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 0.74 noZ L07_faintYJ_12 GCS9 +03 47 41.41 +22 44 32.9 0 16.111 15.521 14.865 14.511 14.527 13.41 2.31 0.90 2.31 15.45 noZ int-pl-IZ-47_RPLJ0347413+224433_Roque48_N GCS9 Cl* Melotte 22 IPL 47 +03 47 43.67 +26 48 12.7 0 14.029 13.455 12.910 12.602 12.617 20.73 2.99 -29.14 2.99 0.15 noZ HHJ193 GCS9 Cl* Melotte 22 HHJ 193 +03 47 44.16 +24 57 24.0 0 20.205 18.716 17.889 17.262 17.066 10.23 4.17 -30.52 4.17 2.74 noZ L07_faintYJ_13 GCS9 +03 47 46.52 +24 55 46.5 0 19.536 18.271 17.418 16.608 16.543 20.20 3.10 -45.92 3.10 3.57 noZ L07_faintYJ_14 GCS9 +03 47 54.17 +22 39 25.5 0 14.311 13.757 13.214 12.899 12.921 18.12 2.28 -43.32 2.28 6.76 noZ HHJ145_BPL161 GCS9 Cl* Melotte 22 HHJ 145 +03 48 04.95 +22 51 29.4 0 19.175 18.460 17.613 17.200 9.01 5.49 -14.56 5.49 4.62 noZ int-pl-IZ-46;IPLJ0348048+225129_N GCS9 Cl* Melotte 22 IPL 46 +03 48 05.47 +21 57 31.2 0 20.160 18.769 17.961 17.316 17.407 2.75 6.05 -28.88 6.05 3.01 noZ L07_faintYJ_15 GCS9 +03 48 15.65 +25 50 08.9 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 1.86 noZ L07_faintYJ_16 GCS9 +03 48 19.48 +23 56 24.6 0 20.187 19.592 18.702 18.018 18.761 -0.62 6.47 -7.62 6.47 0.70 noZ Festin98_011 GCS9 Cl* Melotte 22 NPL 011 +03 48 39.57 +23 56 11.7 0 20.722 19.221 18.750 18.179 18.196 3.85 6.28 -27.06 6.28 1.63 noZ L07_faintYJ_17 GCS9 +03 48 45.69 +25 42 01.5 0 13.138 12.569 11.988 11.686 11.709 18.97 2.27 -44.69 2.27 0.64 noZ HHJ333_DH591 GCS9 Cl* Melotte 22 HHJ 333 +03 48 49.37 +22 45 50.0 0 19.901 19.049 18.457 18.022 18.177 2.45 9.50 2.39 9.50 2.37 noZ Roque26 GCS9 Cl* Melotte 22 Roque 26 +03 49 12.65 +25 42 07.2 0 13.114 12.530 12.019 11.673 11.710 12.39 2.27 -44.95 2.27 17.46 noZ HCG365_SK325_HHJ331_DH608 GCS9 Cl* Melotte 22 HCG 365 +03 50 15.96 +24 23 28.6 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 1.54 noZ L07_faintYJ_18 GCS9 +03 50 39.55 +25 02 54.6 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 2.83 noZ BRB23_L3.5_L07_faintYJ_19 GCS9 Cl* Melotte 22 BRB 23 +03 50 41.21 +25 39 30.4 0 14.791 14.167 13.614 13.272 13.262 17.33 2.28 -46.49 2.28 6.53 noZ DH682 GCS9 Cl* Melotte 22 DH 682 +03 50 56.62 +25 35 06.3 0 12.970 12.392 11.893 11.585 11.581 14.82 2.27 -42.39 2.27 7.19 noZ HCG412_SK253_DH688 GCS9 Cl* Melotte 22 HCG 412 +03 51 29.47 +24 00 37.4 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3.20 noZ L07_faintYJ_20 GCS9 +03 51 41.62 +25 55 45.4 0 20.584 19.121 18.467 18.048 18.073 -19.45 8.36 -21.24 8.36 0.66 noZ L07_faintYJ_21 GCS9 +03 52 05.33 +25 37 34.0 0 18.965 17.690 17.946 17.510 17.886 153.64 8.22 -46.42 8.22 14.51 noZ L07_faintYJ_22 GCS9 +03 52 27.19 +23 12 08.0 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 0.70 noZ L07_faintYJ_23 GCS9 +03 52 34.75 +22 56 04.5 0 19.602 18.412 17.568 17.084 17.019 34.50 4.45 -2.97 4.45 0.86 noZ L07_faintYJ_24 GCS9 +03 52 39.15 +24 46 29.4 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 1.97 noZ BRB20_L1.0_CFHT-PLIZ-35_L07_faintYJ_25 GCS9 Cl* Melotte 22 BRB 20 +03 52 59.62 +24 42 35.6 0 20.657 19.082 18.693 18.234 18.298 1.65 9.10 16.05 9.10 3.50 noZ L07_faintYJ_26 GCS9 +03 52 59.70 +23 51 55.7 0 19.756 18.587 18.105 17.405 17.261 2.86 5.19 4.78 5.19 1.78 noZ BRB25_None GCS9 Cl* Melotte 22 BRB 25 +03 53 18.93 +23 12 39.1 0 20.060 18.328 17.612 16.887 16.883 -24.95 4.25 2.99 4.25 3.50 noZ L07_faintYJ_27 GCS9 +03 53 19.24 +24 53 30.2 0 18.319 17.810 17.093 16.905 16.967 5.95 3.90 1.25 3.90 2.08 noZ PLZJ112_None GCS9 Cl* Melotte 22 PlZJ 112 +03 53 24.50 +25 02 07.0 0 13.975 13.398 12.853 12.542 12.550 14.26 2.28 -40.35 2.28 34.86 noZ HCG471_HHJ170_BPL286_DH785 GCS9 Cl* Melotte 22 HCG 471 +03 53 40.96 +24 25 09.6 0 12.946 12.445 11.889 11.573 11.626 17.52 2.28 -38.69 2.28 14.78 noZ HCG473_SK122_HHJ372_BPL288 GCS9 Cl* Melotte 22 HCG 473 +03 53 58.23 +24 25 08.8 0 16.052 15.397 14.825 14.435 14.461 14.64 2.31 -34.72 2.31 1.63 noZ BPL295 GCS9 Cl* Melotte 22 BPL 295 +03 54 00.38 +24 34 49.8 0 15.467 14.807 14.279 13.873 13.921 17.48 2.29 -38.45 2.29 1.22 noZ BPL297_Moraux2003_100 GCS9 Cl* Melotte 22 BPL 297 +03 54 10.28 +23 41 40.0 0 19.260 18.143 17.171 16.393 16.395 13.79 3.22 -50.49 3.22 4.20 noZ PLZJ4_BRB21_L3.0_L07_faintYJ_28 GCS9 Cl* Melotte 22 PlZJ 4 +03 54 30.49 +25 11 21.8 0 19.979 18.677 18.127 17.536 17.561 -8.62 5.35 -35.68 5.35 0.30 noZ L07_faintYJ_29 GCS9 +03 55 49.29 +24 53 47.2 0 16.057 15.302 14.707 14.299 14.299 -23.95 2.30 -51.07 2.30 9.97 noZ BPL331 GCS9 Cl* Melotte 22 BPL 331 +03 55 58.17 +24 32 59.6 0 13.269 12.731 12.207 11.901 11.914 12.73 2.28 -40.90 2.28 6.04 noZ DH2004_824 GCS9 Cl* Melotte 22 DH 824 +03 55 58.84 +24 57 39.9 0 13.575 12.975 12.458 12.124 12.142 14.04 2.28 -42.01 2.28 6.81 noZ HHJ260_BPL333_DH825 GCS9 Cl* Melotte 22 HHJ 260 +03 56 11.39 +25 03 36.5 0 15.908 15.193 14.655 14.257 14.290 16.67 2.30 -41.79 2.30 70.90 noZ BPL334_L07_A1_114_L07_215 GCS9 Cl* Melotte 22 BPL 334 +03 41 52.33 +24 15 12.7 0 20.667 19.205 18.358 18.067 0.214 -19.58 9.32 -9.18 9.32 0.78 noY int-pl-IZ-87;IPLJ0341523+241512_N GCS9 Cl* Melotte 22 IPL 87 +03 46 28.19 +22 48 57.7 0 15.194 14.082 13.518 13.192 13.168 45.73 2.28 -58.16 2.28 2.41 noY HHJ87_BPL104 GCS9 Cl* Melotte 22 HHJ 87 +03 46 39.31 +22 47 48.2 0 17.213 16.235 15.626 15.376 15.346 -14.43 2.42 -20.96 2.42 3.51 noY HHJ1 GCS9 Cl* Melotte 22 HHJ 1 +03 46 42.37 +22 41 01.5 0 16.964 15.709 15.192 14.813 0.012 50.06 2.35 -12.61 2.35 5.12 noY int-pl-IZ-41;2MASSJ0346423+224101_N GCS9 Cl* Melotte 22 IPL 41 +03 46 59.02 +22 41 40.3 0 20.165 18.548 18.140 17.540 0.150 48.91 7.04 -26.50 7.04 1.44 noY int-pl-IZ-39;IPLJ0346589+224140_N GCS9 Cl* Melotte 22 IPL 39 +03 51 26.60 +22 48 45.9 0 18.883 16.679 15.938 15.325 15.339 26.84 2.54 -77.34 2.54 6.64 noY L07_A1_96 GCS9 XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 138 / Number of bytes per record NAXIS2 = 1314 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 17 / Number of data fields (columns) EXTNAME = 'J_MNRAS_422_1495_tablec1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablec1' / Table name in METAtab Coordinates, near-infrared (ZYJHK1K2) photometry and proper motions all new Pleiades member candidates identified in the UKIDSS GCS DR9 with the probabilistic and standard selection methods TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:12 offset=1 TFORM1 = 'A12 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 15 / UCD=pos.eq.dec;meta.main char:12 offset=14 TFORM2 = 'A12 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 28 / UCD=meta.number short: ....... offset=27 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 32 / UCD=phot.mag;em.opt.I float: . offset=31 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 39 / UCD=phot.mag;em.IR.J float: .. offset=38 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 46 / UCD=phot.mag;em.IR.J float: .. offset=45 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 53 / UCD=phot.mag;em.IR.H float: .. offset=52 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 60 / UCD=phot.mag;em.IR.K float: .. offset=59 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude, first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 67 / UCD=phot.mag;em.IR.K float: .. offset=66 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude, second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 74 / UCD=pos.pm;pos.eq.ra float: .. offset=73 TFORM10 = 'F6.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 81 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=80 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA*cosDE TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 87 / UCD=pos.pm;pos.eq.dec float: . offset=86 TFORM12 = 'F6.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.probability float: .. offset=99 TFORM14 = 'F5.2 ' / Fortran Format TTYPE14 = 'Mmb ' / [0/1]? Membership probability TBCOL15 = 106 / UCD=meta.note short: ......... offset=105 TFORM15 = 'I2 ' / Fortran Format TTYPE15 = 'n_Mmb ' / [1/12] Method used for identification (1) (link) TBCOL16 = 109 / UCD=meta.ref.url char:4 ...... offset=108 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL17 = 114 / UCD=meta.id char:24* ......... offset=113 TFORM17 = 'A24 ' / Fortran Format TTYPE17 = 'SimbadName' / Simbad column added by the CDS END +03 36 01.950 +27 11 04.70 1 16.248 15.492 14.774 14.234 13.810 13.782 15.79 3.79 -42.70 3.79 0.72 12 GCS9 UGCS J033601.95+271104.7 +03 51 27.880 +27 58 55.70 0 15.548 14.979 14.360 13.821 13.468 13.470 22.99 2.95 -41.88 2.95 0.74 12 GCS9 UGCS J035127.88+275855.7 +03 45 06.250 +28 42 19.10 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.85 12 GCS9 Cl* Melotte 22 DH 374 +03 40 06.210 +28 08 32.00 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.62 12 GCS9 Cl* Melotte 22 DH 125 +03 50 54.420 +27 26 13.00 0 15.915 15.313 14.686 14.132 13.756 13.726 19.89 2.89 -41.91 2.89 0.88 12 GCS9 UGCS J035054.41+272612.9 +03 51 30.600 +27 22 49.50 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.89 12 GCS9 Cl* Melotte 22 DH 714 +03 41 33.580 +27 09 50.10 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.89 12 GCS9 Cl* Melotte 22 DH 178 +03 48 57.510 +27 38 25.60 0 15.618 15.106 14.510 13.963 13.618 13.613 16.32 2.89 -40.24 2.89 0.86 12 GCS9 UGCS J034857.50+273825.6 +03 41 38.790 +27 41 07.10 0 12.878 12.514 12.023 11.588 11.219 11.285 24.87 3.28 -39.81 3.28 0.65 12 GCS9 UGCS J034138.79+274107.0 +03 45 40.770 +28 32 05.80 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.75 12 GCS9 Cl* Melotte 22 DH 399 +03 46 23.740 +26 34 23.00 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.73 12 GCS9 Cl* Melotte 22 DH 444 +03 47 56.660 +26 31 50.90 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 0.76 12 GCS9 Cl* Melotte 22 DH 543 +03 47 28.410 +26 32 05.50 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.93 12 GCS9 Cl* Melotte 22 DH 516 +03 50 39.700 +26 34 20.10 0 14.506 14.061 13.500 12.959 12.652 12.675 20.26 2.95 -42.79 2.95 0.94 12 GCS9 UGCS J035039.70+263420.0 +03 50 39.700 +26 34 17.70 0 14.817 14.334 13.753 13.228 12.895 12.916 19.47 2.95 -42.84 2.95 0.94 12 GCS9 UGCS J035039.69+263417.7 +03 41 56.490 +27 02 57.90 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.86 12 GCS9 Cl* Melotte 22 DH 195 +03 41 53.050 +27 04 42.90 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.92 12 GCS9 Cl* Melotte 22 DH 192 +04 05 52.090 +27 09 13.10 0 15.196 14.742 14.147 13.596 13.266 13.266 22.95 2.96 -45.92 2.96 0.66 12 GCS9 UGCS J040552.08+270913.1 +03 43 44.090 +25 39 49.50 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 0.90 12 GCS9 Cl* Melotte 22 MBSC 72 +03 44 10.760 +25 37 38.20 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 0.62 12 GCS9 Cl* Melotte 22 DH 316 +03 43 34.140 +25 35 25.80 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 0.93 12 GCS9 V* V622 Tau +03 44 25.080 +25 34 03.90 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 0.88 12 GCS9 Cl* Melotte 22 HHJ 100 +03 43 52.790 +25 29 30.30 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 298 +03 43 53.880 +25 28 30.00 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 0.76 12 GCS9 V* MQ Tau +03 43 37.320 +25 24 32.00 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 0.69 12 GCS9 V* LY Tau +03 44 33.490 +26 14 53.10 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.93 12 GCS9 Cl* Melotte 22 DH 347 +03 54 46.530 +25 31 34.90 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.90 12 GCS9 Cl* Melotte 22 DH 808 +03 43 07.580 +25 34 29.00 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 0.92 12 GCS9 V* V845 Tau +03 42 43.810 +25 32 06.20 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 341 +03 56 46.600 +26 21 00.40 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 0.77 12 GCS9 Cl* Melotte 22 DH 843 +03 43 11.770 +25 31 31.90 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 0.72 12 GCS9 UGCS J034311.76+253131.9 +03 47 25.900 +25 26 26.40 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 0.91 12 GCS9 Cl* Melotte 22 MBSC 49 +03 47 37.350 +25 20 02.30 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 0.93 12 GCS9 Cl* Melotte 22 MBSC 43 +03 36 42.670 +28 21 05.90 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.78 12 GCS9 Cl* Melotte 22 DH 62 +03 45 58.800 +26 20 01.70 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.81 12 GCS9 Cl* Melotte 22 DH 413 +03 38 43.300 +25 22 26.90 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 0.79 12 GCS9 V* V783 Tau +03 41 11.540 +26 16 18.80 0 15.386 14.841 14.239 13.677 13.324 13.349 21.71 3.00 -37.68 3.00 0.60 12 GCS9 UGCS J034111.53+261618.8 +03 51 13.850 +25 23 11.30 0 13.741 13.323 12.796 12.246 11.944 12.001 16.21 2.23 -43.01 2.23 0.93 12 GCS9 V* V801 Tau +03 35 39.800 +25 38 45.20 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.68 12 GCS9 Cl* Melotte 22 DH 46 +04 03 25.000 +28 32 23.60 0 14.208 13.836 13.311 12.724 12.453 12.501 24.32 2.96 -39.19 2.96 0.66 12 GCS9 UGCS J040325.00+283223.6 +03 55 56.430 +25 17 59.80 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.94 12 GCS9 V* V690 Tau +03 50 08.420 +25 32 55.70 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 0.93 12 GCS9 V* V671 Tau +03 49 47.040 +25 42 36.80 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 638 +03 54 11.490 +25 18 42.70 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.88 12 GCS9 V* V887 Tau +03 40 23.060 +25 29 47.60 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.93 12 GCS9 V* KO Tau +03 41 59.750 +26 27 39.60 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.64 12 GCS9 Cl* Melotte 22 DH 200 +03 37 24.150 +25 43 20.10 0 12.730 12.420 11.937 11.360 11.085 11.153 22.40 2.95 -44.05 2.95 0.78 12 GCS9 UGCS J033724.15+254320.1 +03 46 45.780 +25 27 30.20 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 0.88 12 GCS9 V* V532 Tau +03 34 47.540 +26 22 13.40 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.60 12 GCS9 Cl* Melotte 22 DH 39 +03 47 04.750 +25 22 50.00 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 0.87 12 GCS9 V* V452 Tau +03 39 15.560 +26 48 03.60 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.74 12 GCS9 Cl* Melotte 22 DH 102 +03 47 43.880 +26 13 26.80 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.86 12 GCS9 Cl* Melotte 22 DH 534 +03 47 30.590 +26 16 44.50 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.93 12 GCS9 V* V456 Tau +03 47 21.940 +26 22 47.20 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 0.71 12 GCS9 Cl* Melotte 22 DH 508 +03 40 11.040 +25 23 26.60 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.87 12 GCS9 Cl* Melotte 22 DH 129 +03 40 14.910 +25 19 18.70 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.87 12 GCS9 Cl* Melotte 22 DH 131 +03 44 04.910 +26 58 10.50 0 15.115 14.597 13.992 13.447 13.119 13.096 16.42 2.94 -41.32 2.94 0.88 12 GCS9 UGCS J034404.91+265810.5 +03 51 39.510 +26 52 55.60 0 15.480 14.951 14.332 13.794 13.454 13.448 20.14 2.96 -44.60 2.96 0.87 12 GCS9 UGCS J035139.51+265255.5 +03 51 55.240 +26 57 40.20 0 13.156 12.689 12.074 11.716 11.274 11.253 17.80 2.95 -40.13 2.95 0.91 12 GCS9 UGCS J035155.24+265740.1 +03 52 13.200 +26 11 45.30 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 0.88 12 GCS9 Cl* Melotte 22 DH 745 +03 52 12.190 +26 22 08.90 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 0.86 12 GCS9 V* V564 Tau +03 35 56.060 +25 21 59.30 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.87 12 GCS9 Cl* Melotte 22 DH 50 +03 36 05.330 +25 21 03.40 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.94 12 GCS9 Cl* Melotte 22 DH 51 +03 42 38.350 +25 28 44.30 0 15.625 15.076 14.453 13.924 13.588 13.556 21.08 2.24 -41.30 2.24 0.85 12 GCS9 UGCS J034238.35+252844.2 +03 50 14.760 +25 25 29.80 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 662 +03 50 01.570 +25 24 01.50 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 646 +03 49 20.310 +25 25 42.40 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 0.66 12 GCS9 V* V798 Tau +03 46 47.190 +25 20 53.10 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 0.66 12 GCS9 Cl* Melotte 22 DH 469 +03 41 04.150 +25 30 25.60 0 15.724 15.167 14.547 14.003 13.636 13.636 12.89 2.24 -40.93 2.24 0.71 12 GCS9 UGCS J034104.14+253025.6 +03 42 08.290 +25 36 59.90 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 211 +03 42 03.420 +25 22 39.10 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 0.68 12 GCS9 Cl* Melotte 22 DH 206 +03 41 30.350 +25 17 05.90 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 0.60 12 GCS9 UGCS J034130.35+251705.9 +03 52 41.820 +26 46 10.50 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.74 12 GCS9 Cl* Melotte 22 DH 761 +03 45 09.040 +25 32 49.00 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 377 +04 06 54.480 +26 20 07.50 0 12.888 12.543 11.998 11.384 11.046 11.113 14.03 2.96 -40.84 2.96 0.70 12 GCS9 UGCS J040654.47+262007.5 +03 39 22.420 +26 11 36.00 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 0.93 12 GCS9 Cl* Melotte 22 DH 104 +03 42 12.620 +26 49 44.90 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.91 12 GCS9 Cl* Melotte 22 DH 215 +03 37 48.930 +26 51 45.10 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.86 12 GCS9 Cl* Melotte 22 DH 79 +03 52 58.780 +25 26 19.00 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.90 12 GCS9 Cl* Melotte 22 BPL 277 +03 52 54.250 +25 17 43.30 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.89 12 GCS9 V* V477 Tau +03 49 48.160 +26 37 47.50 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.89 12 GCS9 Cl* Melotte 22 MBSC 98 +03 38 27.830 +26 51 25.40 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.81 12 GCS9 Cl* Melotte 22 DH 89 +03 37 15.610 +26 29 29.80 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.85 12 GCS9 Cl* Melotte 22 STAR 15 +03 48 50.450 +25 17 54.70 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 0.67 12 GCS9 Cl* Melotte 22 BPL 192 +03 52 07.960 +25 27 54.60 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.67 12 GCS9 V* V390 Tau +03 37 11.980 +26 46 28.70 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.65 12 GCS9 Cl* Melotte 22 DH 68 +03 46 44.790 +24 44 58.20 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 109 +03 47 16.450 +24 44 50.10 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 136 +03 47 38.050 +24 49 10.80 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 0.91 12 GCS9 Cl* Melotte 22 BPL 151 +03 48 22.810 +24 48 53.40 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 0.78 12 GCS9 Cl* Melotte 22 BPL 176 +03 47 34.170 +25 43 05.90 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 522 +03 47 33.060 +25 38 18.40 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 0.90 12 GCS9 V* V791 Tau +03 47 28.430 +24 40 33.00 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 0.87 12 GCS9 Cl* Melotte 22 DH 517 +03 40 05.980 +25 40 20.80 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.89 12 GCS9 Cl* Melotte 22 DH 124 +03 46 15.110 +26 46 48.80 1 16.624 15.838 15.032 14.495 14.037 14.088 20.73 2.97 -42.79 2.97 0.68 12 GCS9 UGCS J034615.11+264648.8 +03 39 08.130 +24 46 14.40 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.91 12 GCS9 V* KM Tau +03 49 38.240 +26 51 05.60 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.94 12 GCS9 Cl* Melotte 22 MBSC 29 +03 36 10.560 +24 45 59.70 0 14.996 14.487 13.888 13.330 13.004 12.994 17.12 2.63 -43.88 2.63 0.94 12 GCS9 UGCS J033610.55+244559.7 +03 50 06.430 +26 58 18.70 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.92 12 GCS9 Cl* Melotte 22 DH 652 +03 50 40.830 +24 40 02.60 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 0.78 12 GCS9 Cl* Melotte 22 DH 681 +03 46 35.360 +23 57 07.40 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 0.75 12 GCS9 Cl* Melotte 22 SHF 35 +03 46 55.780 +23 56 24.10 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 480 +03 47 00.360 +23 52 48.20 0 15.230 14.607 13.981 13.439 13.093 13.073 14.89 2.22 -45.67 2.22 0.80 12 GCS9 UGCS J034700.35+235248.1 +03 46 22.200 +23 52 41.00 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 441 +03 47 13.670 +23 49 53.20 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 0.89 12 GCS9 V* V645 Tau +03 46 27.690 +23 48 45.50 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 161 +03 51 58.820 +24 40 04.30 0 13.675 13.254 12.728 12.172 11.871 11.897 14.65 2.20 -42.70 2.20 0.88 12 GCS9 UGCS J035158.82+244004.3 +03 51 59.320 +24 39 58.80 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.83 12 GCS9 V* V387 Tau +03 44 10.960 +24 48 45.80 0 14.889 14.418 13.865 13.318 13.020 21.80 2.97 -46.03 2.97 0.87 12 GCS9 UGCS J034410.95+244845.7 +03 44 25.600 +24 40 52.60 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 0.62 12 GCS9 V* V438 Tau +03 43 12.120 +24 44 45.10 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 0.61 12 GCS9 V* V509 Tau +04 00 28.190 +23 51 24.00 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.78 12 GCS9 Cl* Melotte 22 DH 892 +03 49 22.150 +25 47 37.70 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 0.80 12 GCS9 Cl* Melotte 22 DH 618 +03 49 32.810 +25 47 46.80 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 628 +03 43 09.750 +24 41 32.70 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 0.67 12 GCS9 V* LU Tau +03 43 13.070 +24 39 19.30 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 0.91 12 GCS9 V* LV Tau +03 49 58.610 +25 53 46.20 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 0.89 12 GCS9 UGCS J034958.61+255346.1 +03 41 54.210 +25 43 47.10 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 0.89 12 GCS9 V* V608 Tau +03 42 10.990 +25 44 35.00 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 0.83 12 GCS9 UGCS J034210.99+254435.0 +03 53 09.630 +23 33 47.70 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.63 12 GCS9 2MASS J03530962+2333480 +03 44 51.510 +25 05 16.50 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 0.91 12 GCS9 UGCS J034451.50+250516.4 +03 45 02.880 +25 05 19.60 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 0.75 12 GCS9 Cl* Melotte 22 DH 368 +03 45 18.150 +25 05 58.10 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 0.74 12 GCS9 V* OR Tau +03 44 32.170 +25 08 12.30 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 0.90 12 GCS9 Cl* Melotte 22 HHJ 68 +03 52 51.790 +23 33 47.90 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 0.88 12 GCS9 2MASS J03525177+2333483 +03 41 40.910 +25 54 24.10 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 0.74 12 GCS9 2MASS J03414089+2554242 +03 45 16.990 +25 15 47.50 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 0.89 12 GCS9 V* V520 Tau +03 48 44.050 +25 06 22.40 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 589 +04 04 37.180 +23 23 51.00 0 12.985 12.568 12.054 11.620 11.305 11.370 15.43 3.77 -45.51 3.77 0.63 12 GCS9 UGCS J040437.18+232351.0 +03 41 19.350 +23 51 41.70 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 0.90 12 GCS9 V* V734 Tau +03 49 58.330 +25 06 20.90 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 0.88 12 GCS9 Cl* Melotte 22 BPL 219 +03 41 05.230 +23 50 14.90 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 0.85 12 GCS9 Cl* Melotte 22 BPL 19 +03 50 01.870 +25 12 40.90 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 0.85 12 GCS9 V* V552 Tau +03 51 17.960 +26 01 22.10 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 701 +03 51 18.720 +26 03 15.40 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 0.90 12 GCS9 UGCS J035118.71+260315.4 +03 51 18.860 +26 03 08.70 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 0.92 12 GCS9 Cl* Melotte 22 SK 237 +03 52 44.290 +23 54 14.90 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 0.86 12 GCS9 2MASS J03524427+2354151 +03 51 24.170 +26 03 11.30 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 0.93 12 GCS9 V* V380 Tau +03 52 33.340 +23 51 06.70 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 756 +03 52 18.720 +23 52 36.60 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 0.82 12 GCS9 Cl* Melotte 22 BPL 260 +03 45 51.950 +25 10 01.70 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 0.92 12 GCS9 Cl* Melotte 22 DH 404 +03 45 39.040 +25 13 27.60 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 0.82 12 GCS9 V* OQ Tau +03 46 50.190 +22 12 42.30 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 0.87 12 GCS9 Cl* Melotte 22 BPL 110 +03 46 03.670 +25 52 28.80 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 416 +03 45 52.600 +25 54 59.80 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 405 +03 57 33.070 +23 56 47.10 0 14.486 14.043 13.479 12.922 12.627 12.632 17.44 2.96 -47.07 2.96 0.88 12 GCS9 UGCS J035733.06+235647.0 +03 53 24.240 +25 14 37.70 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 0.71 12 GCS9 UGCS J035324.24+251437.6 +04 00 26.150 +23 26 17.30 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.89 12 GCS9 Cl* Melotte 22 DH 891 +04 00 16.930 +23 26 22.60 0 14.173 13.780 13.204 12.602 12.318 12.347 15.97 3.35 -48.85 3.35 0.70 12 GCS9 UGCS J040016.93+232622.5 +03 37 31.070 +23 46 07.20 0 15.431 14.956 14.357 13.829 13.476 13.487 16.45 2.50 -38.98 2.50 0.81 12 GCS9 UGCS J033731.07+234607.1 +04 00 40.260 +23 26 53.80 0 14.831 14.274 13.616 13.072 12.736 12.745 18.94 3.35 -45.90 3.35 0.92 12 GCS9 UGCS J040040.25+232653.8 +04 01 12.080 +23 54 12.80 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.82 12 GCS9 Cl* Melotte 22 DH 898 +03 46 31.320 +22 18 19.60 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 0.92 12 GCS9 V* V859 Tau +03 46 40.600 +22 22 03.50 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 0.88 12 GCS9 UGCS J034640.59+222203.5 +03 49 08.840 +25 53 48.60 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 603 +03 39 57.150 +26 07 00.10 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.82 12 GCS9 Cl* Melotte 22 MBSC 94 +03 50 05.000 +23 18 17.20 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 0.77 12 GCS9 Cl* Melotte 22 DH 650 +03 46 00.930 +22 12 29.40 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 0.68 12 GCS9 Cl* Melotte 22 STAR 9 +03 40 10.930 +26 06 40.80 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.67 12 GCS9 V* KS Tau +03 49 15.630 +23 22 49.10 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 0.88 12 GCS9 Cl* Melotte 22 STAR 28 +03 49 32.570 +23 24 41.00 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 0.91 12 GCS9 Cl* Melotte 22 HHJ 245 +03 41 19.860 +25 06 49.00 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.87 12 GCS9 Cl* Melotte 22 HHJ 191 +03 41 30.710 +25 11 52.30 0 15.090 14.523 13.857 13.309 12.925 18.12 2.97 -40.52 2.97 0.88 12 GCS9 UGCS J034130.71+251152.2 +03 40 59.260 +25 11 55.20 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.86 12 GCS9 Cl* Melotte 22 DH 162 +03 40 39.460 +23 26 34.80 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 0.69 12 GCS9 Cl* Melotte 22 DH 147 +03 53 16.440 +23 20 58.10 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 0.80 12 GCS9 Cl* Melotte 22 BPL 281 +03 46 07.560 +23 44 42.60 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 0.70 12 GCS9 UGCS J034607.55+234442.5 +03 38 06.260 +25 05 39.90 0 13.122 12.831 12.361 11.819 11.524 11.570 14.79 2.48 -45.21 2.48 0.87 12 GCS9 UGCS J033806.26+250539.9 +03 46 06.520 +23 50 20.20 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 0.81 12 GCS9 Cl* Melotte 22 HII 892 +03 46 04.300 +23 55 40.60 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 363 +03 53 55.130 +23 23 36.10 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 0.70 12 GCS9 2MASS J03535511+2323363 +03 45 59.200 +23 48 46.80 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 0.92 12 GCS9 Cl* Melotte 22 HHJ 127 +03 45 49.420 +23 57 05.70 0 15.929 15.340 14.688 14.182 13.809 13.790 15.31 2.23 -44.67 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 16 +03 43 06.500 +22 17 49.20 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 0.67 12 GCS9 Cl* Melotte 22 HHJ 18 +03 37 50.990 +25 09 25.30 0 13.080 12.684 12.159 11.654 11.378 11.376 21.24 2.48 -46.66 2.48 0.85 12 GCS9 UGCS J033750.99+250925.2 +03 45 46.900 +23 53 00.30 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 0.88 12 GCS9 UGCS J034546.90+235300.3 +03 45 46.480 +23 47 43.10 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 0.89 12 GCS9 Cl* Melotte 22 HHJ 421 +03 45 38.990 +23 57 00.90 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 0.69 12 GCS9 UGCS J034538.98+235700.8 +03 45 38.910 +23 48 55.90 0 15.708 14.967 14.161 13.398 12.966 12.956 20.47 2.22 -40.70 2.22 0.85 12 GCS9 UGCS J034538.90+234855.8 +03 42 56.240 +22 22 38.50 0 15.481 14.854 14.196 13.662 13.283 13.273 23.05 2.51 -45.22 2.51 0.69 12 GCS9 UGCS J034256.23+222238.4 +03 29 58.760 +23 22 18.30 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.81 12 GCS9 Cl* Melotte 22 DH 9 +03 53 48.040 +23 49 09.60 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 0.81 12 GCS9 Cl* Melotte 22 BPL 291 +03 53 24.120 +23 47 58.40 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 784 +04 03 43.840 +23 53 47.00 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.93 12 GCS9 Cl* Melotte 22 DH 909 +03 39 44.190 +22 07 45.50 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 111 +03 49 29.700 +22 18 13.30 0 14.741 14.294 13.748 13.159 12.858 12.841 19.08 2.51 -39.34 2.51 0.90 12 GCS9 UGCS J034929.70+221813.2 +03 49 25.550 +22 18 44.40 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 0.79 12 GCS9 Cl* Melotte 22 BPL 206 +03 49 35.460 +22 23 26.10 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.93 12 GCS9 Cl* Melotte 22 BPL 211 +03 45 12.620 +23 53 45.00 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 0.71 12 GCS9 Cl* Melotte 22 HHJ 14 +03 52 38.910 +25 50 25.30 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.89 12 GCS9 V* V768 Tau +03 52 07.430 +25 53 02.70 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 0.88 12 GCS9 UGCS J035207.42+255302.6 +03 52 31.380 +25 15 07.50 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 755 +03 43 47.080 +26 04 35.30 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 311 +03 48 17.370 +23 48 23.50 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 188 +03 35 46.410 +22 24 49.70 0 15.172 14.684 14.105 13.586 13.262 13.247 22.16 2.94 -42.14 2.94 0.81 12 GCS9 UGCS J033546.40+222449.6 +03 42 02.930 +23 55 53.70 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.87 12 GCS9 V* V727 Tau +03 41 10.270 +25 45 55.90 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 164 +03 40 40.320 +25 50 48.10 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 148 +03 44 33.080 +25 45 09.50 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.71 12 GCS9 V* V742 Tau +03 33 46.650 +23 48 19.40 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 0.94 12 GCS9 Cl* Melotte 22 DH 30 +03 57 30.160 +25 16 46.70 0 13.312 12.967 12.456 11.833 11.542 11.620 21.77 2.47 -45.25 2.47 0.88 12 GCS9 UGCS J035730.15+251646.7 +03 42 54.000 +26 08 16.10 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 244 +03 45 04.990 +23 46 06.40 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 372 +03 43 19.060 +26 04 43.90 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 260 +03 44 58.590 +23 55 40.90 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 363 +03 44 56.010 +23 55 53.40 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 0.91 12 GCS9 V* NX Tau +03 43 26.200 +26 02 30.60 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 0.92 12 GCS9 V* V619 Tau +03 44 34.300 +23 51 24.60 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 14 +03 36 16.320 +25 08 48.80 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.91 12 GCS9 Cl* Melotte 22 DH 54 +03 51 57.530 +25 48 31.20 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 0.94 12 GCS9 V* V561 Tau +03 47 58.040 +22 06 50.80 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 0.74 12 GCS9 2MASS J03475802+2206511 +03 46 54.030 +25 14 44.80 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 0.92 12 GCS9 V* V860 Tau +03 47 15.290 +25 06 55.30 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 0.89 12 GCS9 Cl* Melotte 22 SK 432 +03 47 20.840 +25 05 12.10 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 0.77 12 GCS9 Cl* Melotte 22 HHJ 417 +03 47 25.800 +25 08 32.80 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 0.93 12 GCS9 V* V539 Tau +03 41 23.470 +22 01 53.90 0 14.746 14.157 13.488 12.874 12.525 12.523 15.55 2.51 -42.58 2.51 0.91 12 GCS9 UGCS J034123.46+220153.9 +03 47 47.870 +25 13 34.30 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 0.94 12 GCS9 Cl* Melotte 22 DH 537 +03 52 18.460 +22 00 53.30 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 749 +03 48 15.490 +25 14 36.40 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 0.74 12 GCS9 Cl* Melotte 22 DH 563 +03 42 36.950 +25 13 57.50 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 0.70 12 GCS9 Cl* Melotte 22 DH 233 +03 42 42.120 +25 11 48.70 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 0.82 12 GCS9 Cl* Melotte 22 DH 237 +04 00 10.300 +22 02 17.00 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 888 +03 47 20.430 +22 21 56.30 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 0.80 12 GCS9 UGCS J034720.42+222156.3 +03 47 44.670 +22 12 43.90 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 0.74 12 GCS9 Cl* Melotte 22 BPL 154 +03 47 44.680 +22 23 53.00 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.90 12 GCS9 V* V335 Tau +03 43 36.680 +25 47 00.50 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 0.85 12 GCS9 Cl* Melotte 22 DH 276 +03 35 59.910 +26 01 32.20 0 15.077 14.539 13.909 13.336 12.989 17.12 5.31 -37.68 5.31 0.73 12 GCS9 UGCS J033559.91+260132.2 +03 43 42.900 +25 51 37.00 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 77 +03 50 44.350 +25 07 05.10 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 0.84 12 GCS9 UGCS J035044.34+250705.0 +03 51 23.890 +25 05 52.60 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 0.88 12 GCS9 Cl* Melotte 22 DH 708 +03 44 58.380 +22 11 30.10 0 15.843 15.286 14.652 14.119 13.760 13.765 15.85 2.52 -42.05 2.52 0.88 12 GCS9 UGCS J034458.38+221130.0 +03 43 56.700 +25 15 43.90 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.86 12 GCS9 Cl* Melotte 22 SK 596 +03 54 28.110 +23 56 36.00 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 313 +03 39 13.330 +25 43 49.50 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.90 12 GCS9 Cl* Melotte 22 DH 100 +03 51 36.410 +25 13 40.60 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 717 +03 52 02.640 +25 06 14.90 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 736 +03 55 27.060 +25 14 45.80 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 0.60 12 GCS9 Cl* Melotte 22 BPL 328 +03 36 44.120 +22 01 38.80 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.85 12 GCS9 Cl* Melotte 22 DH 63 +04 01 11.370 +22 10 37.80 0 15.142 14.644 14.046 13.493 13.176 13.178 24.34 3.41 -43.11 3.41 0.60 12 GCS9 UGCS J040111.36+221037.8 +03 43 57.000 +23 57 05.70 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 0.90 12 GCS9 V* V739 Tau +03 44 19.690 +23 53 45.60 0 15.780 15.031 14.317 13.697 13.262 15.52 2.31 -45.09 2.31 0.85 12 GCS9 Cl* Melotte 22 PPL 5 +03 44 12.140 +23 52 37.30 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 0.87 12 GCS9 Cl* Melotte 22 HHJ 217 +03 44 14.650 +23 49 40.00 1 15.892 15.245 14.547 13.957 13.546 14.46 2.31 -40.15 2.31 0.79 12 GCS9 Cl* Melotte 22 PPL 11 +03 37 41.350 +22 17 19.00 0 15.026 14.524 13.892 13.401 13.008 13.055 19.22 2.94 -40.01 2.94 0.86 12 GCS9 UGCS J033741.35+221718.9 +03 39 42.720 +23 54 27.60 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 0.88 12 GCS9 V* V489 Tau +03 40 18.850 +23 54 23.30 0 14.517 14.091 13.554 13.046 12.719 12.723 16.56 2.50 -42.50 2.50 0.93 12 GCS9 UGCS J034018.84+235423.3 +03 39 47.990 +23 50 56.60 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 115 +04 04 34.790 +22 02 46.20 0 14.508 14.023 13.423 12.876 12.548 12.543 20.21 5.07 -43.45 5.07 0.94 12 GCS9 UGCS J040434.79+220246.2 +03 39 48.510 +23 46 03.80 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 0.83 12 GCS9 Cl* Melotte 22 BPL 6 +03 39 50.680 +23 45 52.30 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 0.89 12 GCS9 Cl* Melotte 22 DH 118 +03 58 01.970 +23 53 54.50 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 862 +03 59 31.470 +21 16 18.30 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.87 12 GCS9 Cl* Melotte 22 DH 879 +03 49 48.440 +22 10 48.30 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 0.71 12 GCS9 V* V877 Tau +03 40 01.840 +25 04 19.50 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 0.93 12 GCS9 Cl* Melotte 22 HHJ 102 +03 40 31.170 +25 08 52.80 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 0.93 12 GCS9 V* KV Tau +03 48 05.870 +23 53 00.90 0 15.251 14.707 14.138 13.612 13.253 13.280 17.10 2.22 -37.49 2.22 0.71 12 GCS9 UGCS J034805.86+235300.8 +03 47 29.590 +23 52 49.30 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 8 +03 47 31.650 +23 52 19.10 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 0.82 12 GCS9 UGCS J034731.64+235219.0 +04 01 49.170 +22 10 05.00 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.83 12 GCS9 Cl* Melotte 22 DH 901 +03 38 27.520 +25 30 18.10 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 0.69 12 GCS9 Cl* Melotte 22 HHJ 2 +03 38 24.220 +25 19 49.20 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 86 +03 45 39.120 +22 04 23.10 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 0.67 12 GCS9 Cl* Melotte 22 BPL 75 +03 37 54.790 +25 26 31.30 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.83 12 GCS9 Cl* Melotte 22 HHJ 402 +03 54 58.030 +25 14 29.00 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 0.82 12 GCS9 Cl* Melotte 22 DH 810 +03 50 31.950 +22 08 47.50 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 0.75 12 GCS9 V* V674 Tau +03 57 49.370 +22 08 30.90 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.72 12 GCS9 Cl* Melotte 22 DH 858 +03 44 02.500 +21 13 15.80 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.85 12 GCS9 Cl* Melotte 22 DH 307 +03 50 25.160 +23 55 41.80 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 0.90 12 GCS9 V* V373 Tau +03 50 12.460 +23 55 35.90 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 659 +03 50 02.180 +23 51 44.60 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 0.81 12 GCS9 V* V368 Tau +03 50 12.600 +23 48 48.90 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 0.77 12 GCS9 Cl* Melotte 22 DH 660 +03 57 42.980 +25 23 06.70 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 0.92 12 GCS9 V* V583 Tau +03 43 16.610 +23 50 01.50 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.92 12 GCS9 Cl* Melotte 22 DH 258 +03 52 04.480 +24 14 39.60 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 739 +03 36 11.070 +23 48 23.00 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 0.61 12 GCS9 Cl* Melotte 22 DH 53 +03 34 54.950 +22 04 46.20 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.66 12 GCS9 Cl* Melotte 22 DH 40 +03 44 59.480 +23 21 18.10 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 365 +03 45 12.160 +23 21 52.90 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 0.93 12 GCS9 V* V442 Tau +03 44 58.020 +23 24 31.00 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 0.79 12 GCS9 Cl* Melotte 22 DH 362 +03 45 08.410 +23 25 00.90 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 0.79 12 GCS9 UGCS J034508.41+232500.9 +03 45 54.990 +24 13 26.00 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 82 +03 44 36.280 +23 30 10.90 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 0.93 12 GCS9 V* NT Tau +03 45 40.250 +24 17 20.60 0 14.722 14.287 13.746 13.164 12.893 12.881 22.35 2.22 -39.61 2.22 0.84 12 GCS9 UGCS J034540.24+241720.5 +03 45 51.330 +24 17 44.30 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 0.75 12 GCS9 UGCS J034551.33+241744.2 +03 45 30.230 +24 18 45.30 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 0.77 12 GCS9 Cl* Melotte 22 MT 59 +03 45 49.940 +23 19 44.70 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 0.66 12 GCS9 UGCS J034549.94+231944.7 +03 45 42.870 +23 20 12.20 0 15.145 14.690 14.081 13.551 13.217 13.217 17.99 2.24 -40.55 2.24 0.88 12 GCS9 UGCS J034542.87+232012.1 +03 45 37.790 +24 20 08.10 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 0.93 12 GCS9 UGCS J034537.78+242008.1 +03 45 37.950 +24 20 03.40 0 13.151 12.791 12.328 11.940 11.503 11.541 16.88 2.22 -43.71 2.22 0.93 12 GCS9 UGCS J034537.95+242003.4 +03 45 24.790 +24 20 45.30 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 392 +03 46 07.510 +24 22 27.60 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.89 12 GCS9 V* V638 Tau +03 41 24.690 +25 23 05.80 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 117 +03 52 05.830 +24 17 31.00 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 0.67 12 GCS9 2MASS J03520581+2417314 +03 52 44.480 +24 20 59.30 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 766 +03 45 09.040 +25 22 29.70 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 81 +03 45 05.320 +25 29 10.90 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 373 +03 45 01.210 +25 21 05.60 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 367 +03 38 02.050 +24 20 15.10 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 81 +03 44 32.330 +25 25 17.90 0 16.994 16.209 15.450 14.883 14.476 14.459 14.24 2.27 -43.51 2.27 0.64 12 GCS9 2MASS J03443231+2525181 +03 48 48.790 +23 24 48.00 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 0.64 12 GCS9 Cl* Melotte 22 HHJ 86 +03 48 15.250 +23 26 05.40 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 0.89 12 GCS9 Cl* Melotte 22 HHJ 232 +03 49 01.500 +24 11 38.30 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 0.81 12 GCS9 Cl* Melotte 22 DH 599 +03 48 35.490 +24 12 03.00 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 0.85 12 GCS9 V* V352 Tau +03 48 39.910 +24 12 42.70 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 0.87 12 GCS9 V* V873 Tau +03 48 22.710 +23 27 42.80 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 573 +03 48 25.240 +24 14 25.80 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 0.94 12 GCS9 V* V871 Tau +03 48 14.300 +24 15 50.50 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 0.69 12 GCS9 Cl* Melotte 22 BPL 169 +03 48 31.060 +24 16 53.10 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 0.79 12 GCS9 V* V349 Tau +03 48 57.360 +24 19 43.60 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 0.89 12 GCS9 V* V759 Tau +03 45 26.560 +22 31 32.00 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 0.67 12 GCS9 Cl* Melotte 22 DH 393 +03 48 55.650 +24 21 40.10 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 0.70 12 GCS9 Cl* Melotte 22 HHJ 8 +03 37 56.420 +23 22 56.60 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.65 12 GCS9 Cl* Melotte 22 DH 80 +03 41 00.390 +24 13 35.00 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.89 12 GCS9 Cl* Melotte 22 BPL 17 +03 41 22.450 +24 23 51.60 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 0.90 12 GCS9 Cl* Melotte 22 DH 169 +03 50 37.420 +22 28 08.00 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 0.87 12 GCS9 V* V878 Tau +03 56 52.910 +23 25 43.70 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 846 +03 46 52.610 +23 38 43.00 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 473 +03 46 43.190 +23 37 21.90 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 466 +03 46 44.880 +23 37 07.30 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 0.83 12 GCS9 UGCS J034644.87+233707.2 +03 47 03.780 +23 36 58.60 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 0.80 12 GCS9 V* QQ Tau +03 46 58.170 +23 33 38.80 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 482 +03 47 02.350 +23 32 36.00 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 487 +03 46 50.090 +23 31 56.10 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 0.94 12 GCS9 V* PU Tau +03 56 25.930 +24 16 51.30 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.83 12 GCS9 Cl* Melotte 22 SK 18 +03 58 57.040 +23 42 31.10 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.82 12 GCS9 V* V405 Tau +03 54 01.120 +23 19 41.00 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 299 +03 54 13.020 +23 20 50.80 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 0.94 12 GCS9 V* V400 Tau +03 42 02.870 +24 12 36.00 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.61 12 GCS9 V* V497 Tau +03 48 07.960 +23 44 23.50 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.92 12 GCS9 V* V658 Tau +03 47 22.680 +23 44 06.70 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 0.92 12 GCS9 V* V537 Tau +03 47 44.660 +23 42 03.10 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 536 +03 47 33.460 +23 41 32.80 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.88 12 GCS9 V* QV Tau +03 47 51.970 +23 39 48.00 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 540 +03 47 26.770 +23 38 02.40 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 0.93 12 GCS9 V* V864 Tau +03 47 29.930 +23 33 15.00 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 0.74 12 GCS9 Cl* Melotte 22 HHJ 16 +03 43 39.050 +23 44 05.20 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 0.93 12 GCS9 V* MP Tau +03 43 39.720 +23 41 32.40 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.86 12 GCS9 Cl* Melotte 22 HHJ 40 +03 43 37.110 +23 38 31.90 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.91 12 GCS9 Cl* Melotte 22 DH 278 +03 44 16.460 +23 37 04.00 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 0.93 12 GCS9 V* MW Tau +03 44 47.840 +24 12 52.50 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 0.94 12 GCS9 V* NU Tau +03 44 27.500 +24 14 17.00 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 341 +03 45 13.140 +24 15 23.60 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 0.86 12 GCS9 V* NZ Tau +04 02 49.970 +23 30 38.40 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.81 12 GCS9 Cl* Melotte 22 DH 904 +03 44 46.140 +24 23 02.80 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 24 +03 42 27.310 +22 34 24.60 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 0.90 12 GCS9 V* V612 Tau +03 45 08.690 +24 24 09.30 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 0.74 12 GCS9 UGCS J034508.68+242409.3 +03 57 08.610 +20 07 42.70 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.90 12 GCS9 Cl* Melotte 22 DH 849 +03 51 11.880 +23 44 43.30 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 0.89 12 GCS9 Cl* Melotte 22 BPL 232 +03 51 03.280 +23 35 00.10 0 15.395 14.846 14.243 13.676 13.365 13.342 15.36 2.22 -44.88 2.22 0.85 12 GCS9 UGCS J035103.27+233500.0 +03 51 51.550 +23 34 49.10 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 0.89 12 GCS9 2MASS J03515154+2334494 +03 40 32.380 +19 54 30.00 0 15.279 14.784 14.160 13.581 13.241 13.222 13.86 5.02 -39.74 5.02 0.73 12 GCS9 UGCS J034032.38+195429.9 +03 42 34.030 +23 24 51.80 0 15.870 15.322 14.668 14.108 13.758 13.737 14.90 2.26 -38.79 2.26 0.74 12 GCS9 UGCS J034234.02+232451.8 +03 42 36.270 +23 22 04.70 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 0.73 12 GCS9 V* V433 Tau +03 56 09.600 +22 28 01.40 0 14.827 14.311 13.669 13.091 12.772 12.772 24.94 2.51 -41.87 2.51 0.72 12 GCS9 2MASS J03560958+2228017 +03 43 01.400 +23 29 30.40 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 0.94 12 GCS9 UGCS J034301.40+232930.4 +03 39 38.690 +23 23 20.20 0 14.410 14.024 13.460 12.927 12.628 12.603 18.87 2.98 -38.42 2.98 0.86 12 GCS9 UGCS J033938.69+232320.2 +03 39 41.120 +23 28 23.30 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.70 12 GCS9 Cl* Melotte 22 HHJ 83 +03 39 57.350 +23 19 42.20 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.61 12 GCS9 Cl* Melotte 22 HHJ 103 +03 40 00.190 +23 26 05.70 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 0.85 12 GCS9 V* KU Tau +03 53 30.760 +19 54 24.10 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.91 12 GCS9 Cl* Melotte 22 DH 787 +03 40 26.270 +23 21 29.10 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.71 12 GCS9 Cl* Melotte 22 HHJ 167 +03 51 06.130 +22 38 00.60 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 0.82 12 GCS9 Cl* Melotte 22 DH 694 +03 54 22.490 +23 38 11.90 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 801 +03 54 02.730 +23 35 00.90 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 0.80 12 GCS9 Cl* Melotte 22 BPL 301 +03 51 07.110 +23 20 57.60 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 695 +03 47 22.280 +24 16 59.90 0 16.097 15.485 14.840 14.281 13.922 13.904 20.93 2.23 -44.36 2.23 0.64 12 GCS9 UGCS J034722.27+241659.8 +03 47 22.380 +24 14 18.80 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 0.81 12 GCS9 Cl* Melotte 22 BPL 139 +03 47 25.110 +24 15 17.20 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 0.93 12 GCS9 Cl* Melotte 22 BPL 143 +03 47 29.480 +24 12 19.70 0 15.420 14.860 14.256 13.725 13.372 13.384 17.13 2.22 -47.51 2.22 0.76 12 GCS9 UGCS J034729.48+241219.7 +03 47 30.600 +24 22 13.80 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 0.94 12 GCS9 V* QT Tau +03 51 39.080 +23 22 04.30 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 237 +03 52 05.580 +22 34 55.10 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 740 +03 52 34.480 +22 30 07.50 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 0.63 12 GCS9 V* V395 Tau +03 46 22.470 +23 29 08.00 0 14.962 14.417 13.743 13.146 12.784 12.747 19.14 2.24 -43.87 2.24 0.94 12 GCS9 UGCS J034622.46+232908.0 +03 52 51.720 +22 31 32.70 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 0.94 12 GCS9 V* V568 Tau +03 47 15.380 +23 26 05.80 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 0.92 12 GCS9 Cl* Melotte 22 HHJ 203 +03 52 56.970 +22 26 01.10 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.92 12 GCS9 V* V883 Tau +03 43 43.530 +24 12 50.00 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 284 +03 47 22.030 +23 21 36.30 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 0.93 12 GCS9 UGCS J034722.02+232136.3 +03 47 28.120 +23 26 53.40 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 0.92 12 GCS9 Cl* Melotte 22 MSK 140 +03 47 32.190 +23 18 23.30 0 14.313 13.888 13.351 12.830 12.511 12.509 18.09 2.24 -46.67 2.24 0.90 12 GCS9 UGCS J034732.19+231823.2 +03 43 51.760 +24 14 15.90 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 0.90 12 GCS9 V* V847 Tau +03 43 52.770 +24 18 48.40 0 16.556 15.907 15.211 14.628 14.221 19.79 2.32 -39.76 2.32 0.64 12 GCS9 UGCS J034352.77+241848.4 +03 47 36.040 +23 28 26.70 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 526 +03 43 57.290 +24 13 20.30 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 0.72 12 GCS9 V* V511 Tau +04 00 08.160 +22 32 01.10 1 16.449 15.820 15.102 14.503 14.098 14.104 16.31 2.88 -39.63 2.88 0.64 12 GCS9 UGCS J040008.16+223201.0 +03 48 04.980 +23 24 13.40 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 0.89 12 GCS9 Cl* Melotte 22 DH 546 +03 39 55.210 +24 12 54.10 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 0.86 12 GCS9 Cl* Melotte 22 MHO 2 +03 58 56.150 +23 27 53.50 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.81 12 GCS9 Cl* Melotte 22 DH 868 +03 40 07.110 +24 13 03.30 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.90 12 GCS9 Cl* Melotte 22 DH 126 +03 59 07.060 +22 27 48.20 0 14.565 14.030 13.393 12.824 12.466 12.480 16.23 2.84 -40.81 2.84 0.91 12 GCS9 UGCS J035907.05+222748.1 +03 59 18.990 +23 31 23.90 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.92 12 GCS9 Cl* Melotte 22 DH 874 +03 40 26.950 +24 14 14.20 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 140 +03 49 59.540 +24 11 45.60 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 220 +03 50 27.350 +23 22 44.90 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 12 GCS9 Cl* Melotte 22 DH 670 +03 50 12.870 +24 21 06.20 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 0.75 12 GCS9 UGCS J035012.86+242106.2 +03 50 13.000 +24 21 07.00 0 13.248 12.864 12.329 11.821 11.509 11.534 20.58 2.22 -44.46 2.22 0.92 12 GCS9 UGCS J035012.99+242106.9 +03 50 15.270 +24 13 36.00 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.94 12 GCS9 V* V469 Tau +03 50 19.150 +24 16 34.00 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 0.67 12 GCS9 Cl* Melotte 22 BPL 228 +03 50 42.150 +23 29 33.10 0 15.510 14.987 14.385 13.858 13.532 13.543 21.92 2.14 -45.96 2.14 0.74 12 GCS9 UGCS J035042.14+232933.0 +03 50 33.080 +24 20 21.60 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 674 +03 50 42.440 +24 12 55.40 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 683 +03 50 54.650 +24 21 55.70 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 687 +03 47 22.460 +22 31 10.80 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 140 +03 46 55.320 +23 22 49.30 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 479 +03 40 01.870 +24 46 25.90 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.94 12 GCS9 V* V598 Tau +03 47 09.520 +23 25 56.30 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 498 +03 48 05.720 +22 38 09.30 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 0.82 12 GCS9 V* V341 Tau +03 42 08.840 +23 35 16.80 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 0.72 12 GCS9 Cl* Melotte 22 DH 212 +03 39 32.320 +24 16 01.10 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.91 12 GCS9 V* V597 Tau +03 41 25.330 +22 32 55.80 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.89 12 GCS9 V* V492 Tau +03 59 59.150 +23 41 04.60 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.83 12 GCS9 Cl* Melotte 22 DH 883 +03 59 56.490 +23 40 15.30 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.69 12 GCS9 Cl* Melotte 22 DH 882 +04 03 49.570 +23 43 13.70 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.90 12 GCS9 Cl* Melotte 22 DH 910 +04 01 13.990 +23 10 29.90 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.77 12 GCS9 Cl* Melotte 22 DH 899 +03 45 16.420 +23 34 01.60 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 384 +03 45 06.790 +23 36 51.40 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 0.92 12 GCS9 V* V516 Tau +03 44 56.690 +23 36 23.50 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 361 +03 44 51.260 +23 34 20.60 0 16.524 15.857 15.186 14.615 14.212 14.214 20.98 2.24 -41.95 2.24 0.66 12 GCS9 UGCS J034451.26+233420.5 +03 44 35.900 +23 34 41.90 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 0.72 12 GCS9 Cl* Melotte 22 HHJ 5 +03 44 31.720 +23 35 26.00 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 345 +03 49 01.160 +23 38 15.50 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 598 +03 36 22.330 +22 44 32.70 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 0.85 12 GCS9 Cl* Melotte 22 DH 55 +03 48 19.840 +23 36 11.80 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 568 +03 48 16.090 +23 35 15.20 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 564 +03 48 15.270 +23 42 03.40 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 562 +03 48 13.780 +23 37 59.30 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 0.94 12 GCS9 V* V458 Tau +03 48 08.960 +23 42 23.30 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 552 +03 41 02.990 +23 43 21.40 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.71 12 GCS9 V* V603 Tau +03 38 13.040 +23 37 20.70 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 0.84 12 GCS9 Cl* Melotte 22 HHJ 32 +03 41 43.720 +23 07 59.70 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 0.60 12 GCS9 UGCS J034143.72+230759.7 +03 42 15.370 +23 11 30.90 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 0.81 12 GCS9 Cl* Melotte 22 HHJ 109 +03 54 25.040 +24 42 43.40 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 0.94 12 GCS9 V* V401 Tau +03 54 24.390 +22 41 20.50 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 0.92 12 GCS9 UGCS J035424.39+224120.5 +03 49 58.340 +23 42 33.70 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 0.93 12 GCS9 Cl* Melotte 22 MSK 211 +03 49 57.640 +23 43 28.20 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 644 +04 01 39.830 +22 47 53.70 0 16.669 15.899 15.153 14.575 14.169 14.157 21.16 3.39 -42.62 3.39 0.65 12 GCS9 UGCS J040139.83+224753.7 +03 49 31.220 +23 41 19.60 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 625 +03 49 29.810 +23 38 55.20 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 624 +03 49 21.500 +23 39 06.30 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 0.82 12 GCS9 V* V551 Tau +03 46 12.670 +23 35 13.60 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 0.94 12 GCS9 UGCS J034612.66+233513.5 +03 45 27.520 +23 37 56.90 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 0.90 12 GCS9 Cl* Melotte 22 HHJ 106 +03 31 33.200 +27 32 49.10 0 14.985 14.331 13.622 13.095 12.730 20.59 6.93 -48.45 6.93 0.77 12 GCS9 UGCS J033133.20+273249.0 +03 48 23.920 +23 08 08.10 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 0.83 12 GCS9 Cl* Melotte 22 DH 576 +03 48 40.990 +23 14 17.20 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 0.92 12 GCS9 V* V875 Tau +03 50 15.430 +22 46 19.90 0 16.872 16.103 15.397 14.842 14.414 14.387 15.23 2.19 -42.79 2.19 0.70 12 GCS9 UGCS J035015.43+224619.8 +03 42 56.550 +22 51 17.90 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 0.78 12 GCS9 UGCS J034256.54+225117.8 +03 43 04.200 +22 48 03.30 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 0.90 12 GCS9 V* V435 Tau +03 42 29.430 +22 47 25.90 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 0.90 12 GCS9 V* V613 Tau +03 43 19.010 +22 47 10.40 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 0.89 12 GCS9 V* V621 Tau +03 41 26.360 +23 08 02.70 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 0.92 12 GCS9 Cl* Melotte 22 DH 174 +03 40 51.820 +23 13 50.50 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 0.93 12 GCS9 V* V601 Tau +03 41 25.970 +23 15 35.80 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 0.94 12 GCS9 UGCS J034125.96+231535.8 +03 43 27.440 +22 37 41.00 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 0.67 12 GCS9 V* LX Tau +03 39 44.790 +22 39 15.30 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.94 12 GCS9 Cl* Melotte 22 DH 112 +03 40 07.010 +22 38 47.50 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.89 12 GCS9 Cl* Melotte 22 HHJ 114 +03 34 35.570 +23 38 43.30 0 15.561 15.064 14.437 13.919 13.538 13.570 17.78 2.61 -36.55 2.61 0.60 12 GCS9 UGCS J033435.57+233843.3 +03 50 25.190 +22 40 31.70 0 15.262 14.699 14.109 13.541 13.204 13.220 18.66 2.13 -45.07 2.13 0.88 12 GCS9 UGCS J035025.18+224031.7 +03 50 04.250 +23 10 44.50 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 0.84 12 GCS9 Cl* Melotte 22 DH 649 +03 49 41.140 +23 14 59.30 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 0.83 12 GCS9 UGCS J034941.13+231459.2 +03 49 32.160 +23 16 17.90 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 0.86 12 GCS9 Cl* Melotte 22 DH 626 +03 56 24.990 +23 05 26.60 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 0.72 12 GCS9 Cl* Melotte 22 HHJ 419 +03 45 28.900 +27 28 07.20 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.88 12 GCS9 Cl* Melotte 22 DH 394 +03 36 24.180 +22 37 24.30 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 0.93 12 GCS9 UGCS J033624.18+223724.3 +03 43 36.570 +23 12 34.10 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 0.79 12 GCS9 V* MN Tau +03 44 09.320 +23 08 46.80 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 0.91 12 GCS9 Cl* Melotte 22 DH 313 +03 44 22.140 +23 10 54.80 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 329 +03 45 24.700 +24 38 46.40 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 0.89 12 GCS9 UGCS J034524.69+243846.4 +03 45 06.560 +24 40 42.70 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 0.71 12 GCS9 Cl* Melotte 22 HHJ 48 +03 45 08.760 +24 50 31.50 0 13.942 13.474 12.930 12.429 12.086 12.117 17.85 2.21 -41.29 2.21 0.93 12 GCS9 UGCS J034508.76+245031.4 +03 37 16.530 +23 11 04.20 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.84 12 GCS9 Cl* Melotte 22 DH 69 +03 45 01.140 +24 46 40.90 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 0.71 12 GCS9 V* V744 Tau +04 06 28.550 +22 36 59.60 0 14.067 13.659 13.076 12.477 12.156 12.133 17.57 5.07 -40.55 5.07 0.92 12 GCS9 UGCS J040628.54+223659.6 +03 58 34.180 +22 40 11.10 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 0.81 12 GCS9 Cl* Melotte 22 DH 867 +03 50 08.620 +24 40 17.70 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 0.90 12 GCS9 Cl* Melotte 22 DH 655 +03 49 55.790 +24 44 31.30 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 0.92 12 GCS9 V* V364 Tau +03 31 49.860 +22 50 24.50 0 14.569 14.083 13.494 12.998 12.676 12.690 25.11 3.42 -44.59 3.42 0.68 12 GCS9 UGCS J033149.85+225024.4 +03 32 07.870 +23 13 57.20 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.84 12 GCS9 Cl* Melotte 22 DH 17 +03 37 26.390 +24 34 01.20 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.93 12 GCS9 Cl* Melotte 22 DH 71 +03 42 44.370 +23 06 16.10 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 0.75 12 GCS9 Cl* Melotte 22 DH 240 +03 42 51.680 +23 08 43.70 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 0.92 12 GCS9 UGCS J034251.68+230843.6 +03 49 21.250 +24 41 40.80 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 615 +03 48 39.310 +24 50 19.90 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 585 +03 54 37.360 +23 13 32.50 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 0.81 12 GCS9 Cl* Melotte 22 DH 805 +03 51 23.820 +22 50 29.10 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 0.88 12 GCS9 Cl* Melotte 22 DH 706 +03 39 43.320 +23 12 25.30 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.67 12 GCS9 Cl* Melotte 22 DH 110 +03 46 28.630 +24 45 32.10 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 0.81 12 GCS9 V* V446 Tau +03 46 17.940 +24 41 09.30 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 433 +03 46 08.700 +24 40 33.10 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 0.93 12 GCS9 V* V857 Tau +03 46 02.960 +24 40 55.60 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 414 +03 45 36.720 +24 39 06.50 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 0.85 12 GCS9 V* V443 Tau +03 50 18.220 +24 35 15.30 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 0.85 12 GCS9 Cl* Melotte 22 DH 665 +03 50 10.770 +24 28 41.40 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 656 +03 49 33.040 +24 32 02.40 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 0.79 12 GCS9 V* V361 Tau +03 46 27.010 +24 27 13.90 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 0.82 12 GCS9 Cl* Melotte 22 DH 447 +03 46 24.640 +24 28 46.20 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 0.93 12 GCS9 V* V1275 Tau +03 46 24.120 +24 30 12.70 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 101 +03 46 23.030 +24 36 17.90 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 442 +03 46 21.360 +24 33 52.20 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 439 +03 56 57.080 +24 48 34.30 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 0.89 12 GCS9 V* V693 Tau +03 46 05.640 +24 36 44.30 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 0.93 12 GCS9 Cl* Melotte 22 SK 488 +03 46 05.690 +24 36 49.70 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 0.87 12 GCS9 UGCS J034605.69+243649.7 +03 45 51.090 +24 26 10.90 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 25 +03 45 49.370 +24 25 07.10 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 77 +03 45 35.690 +24 24 34.10 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 0.74 12 GCS9 UGCS J034535.69+242434.1 +03 41 59.670 +24 42 18.30 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 0.65 12 GCS9 Cl* Melotte 22 BPL 31 +03 52 46.450 +24 33 41.00 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 0.76 12 GCS9 Cl* Melotte 22 BPL 270 +03 52 43.240 +24 27 58.50 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 763 +03 52 30.910 +24 32 39.50 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.89 12 GCS9 V* V476 Tau +03 52 20.660 +24 33 55.50 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 0.78 12 GCS9 V* V392 Tau +03 42 03.300 +24 32 13.30 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 0.79 12 GCS9 Cl* Melotte 22 DH 205 +03 50 38.920 +23 13 02.50 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 0.79 12 GCS9 Cl* Melotte 22 DH 678 +03 48 50.450 +22 44 29.80 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 0.71 12 GCS9 Cl* Melotte 22 HHJ 3 +03 46 57.100 +23 15 02.20 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 0.83 12 GCS9 V* V453 Tau +03 49 15.120 +24 36 22.50 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 0.64 12 GCS9 UGCS J034915.12+243622.5 +03 48 45.350 +24 37 26.30 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 0.83 12 GCS9 V* V463 Tau +03 48 44.690 +24 37 23.50 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 0.75 12 GCS9 2MASS J03484468+2437237 +03 48 42.690 +24 27 19.40 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.85 12 GCS9 Cl* Melotte 22 DH 587 +03 48 40.430 +24 36 34.00 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 0.89 12 GCS9 V* V662 Tau +03 51 19.480 +23 09 49.40 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 703 +03 51 51.150 +23 17 41.10 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 0.93 12 GCS9 V* V802 Tau +03 58 30.990 +23 04 12.70 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 866 +03 42 29.710 +20 48 39.60 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.82 12 GCS9 Cl* Melotte 22 DH 229 +03 43 22.550 +23 00 56.50 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 0.70 12 GCS9 UGCS J034322.55+230056.5 +03 51 19.770 +23 04 04.10 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 704 +03 51 50.600 +22 53 44.30 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 0.72 12 GCS9 V* V386 Tau +03 52 11.230 +20 52 33.40 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.91 12 GCS9 Cl* Melotte 22 DH 743 +03 46 36.070 +23 04 17.20 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 0.94 12 GCS9 Cl* Melotte 22 MSK 100 +03 46 48.790 +23 04 07.30 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 0.90 12 GCS9 V* V533 Tau +03 46 21.620 +23 04 00.90 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 440 +03 46 31.020 +23 01 34.60 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 453 +03 46 19.410 +23 00 55.70 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 0.80 12 GCS9 Cl* Melotte 22 DH 435 +03 46 34.800 +22 56 07.50 0 12.325 12.095 11.650 11.585 10.883 11.064 23.59 2.23 -45.09 2.23 0.61 12 GCS9 V* V750 Tau +03 48 05.830 +23 02 02.70 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 0.93 12 GCS9 V* V340 Tau +03 47 39.800 +23 00 04.40 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 0.88 12 GCS9 Cl* Melotte 22 HHJ 94 +03 47 52.880 +22 59 33.80 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 0.89 12 GCS9 Cl* Melotte 22 BPL 160 +03 43 44.970 +23 03 21.10 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 0.84 12 GCS9 Cl* Melotte 22 HHJ 321 +03 43 25.160 +22 53 44.30 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 0.85 12 GCS9 Cl* Melotte 22 DH 263 +03 50 27.830 +23 03 54.90 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 671 +03 50 17.590 +22 55 58.80 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 0.89 12 GCS9 Cl* Melotte 22 DH 664 +03 54 55.340 +22 59 40.10 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 0.78 12 GCS9 Cl* Melotte 22 HHJ 62 +03 55 11.850 +22 58 02.50 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 0.83 12 GCS9 Cl* Melotte 22 HHJ 61 +03 40 23.840 +23 04 09.00 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.90 12 GCS9 Cl* Melotte 22 DH 135 +03 39 49.720 +23 03 26.20 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.89 12 GCS9 Cl* Melotte 22 DH 117 +03 40 51.080 +20 41 17.20 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.75 12 GCS9 Cl* Melotte 22 DH 155 +03 42 23.660 +27 45 56.30 0 13.845 13.397 12.838 12.326 11.997 12.001 23.57 3.28 -40.53 3.28 0.76 12 GCS9 UGCS J034223.65+274556.2 +03 49 01.020 +22 58 49.20 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 0.84 12 GCS9 V* V548 Tau +03 48 50.670 +23 04 30.00 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 0.90 12 GCS9 Cl* Melotte 22 HHJ 116 +03 48 35.200 +22 53 42.10 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 0.62 12 GCS9 V* V661 Tau +03 48 22.660 +22 52 21.30 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 0.91 12 GCS9 V* V870 Tau +03 41 58.660 +22 57 01.70 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 0.90 12 GCS9 V* V432 Tau +03 41 46.660 +23 01 19.60 0 15.129 14.622 14.032 13.502 13.175 13.164 18.10 2.25 -44.32 2.25 0.89 12 GCS9 UGCS J034146.65+230119.6 +03 40 54.480 +22 54 25.50 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 159 +03 53 15.710 +22 52 14.30 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 0.93 12 GCS9 V* V681 Tau +03 53 10.160 +23 03 10.60 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 779 +03 53 01.630 +22 58 48.20 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 0.94 12 GCS9 Cl* Melotte 22 DH 776 +03 49 41.210 +22 56 40.50 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 0.69 12 GCS9 Cl* Melotte 22 BPL 213 +03 49 25.610 +23 02 49.90 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 620 +03 45 56.970 +23 01 29.00 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 0.90 12 GCS9 V* V524 Tau +03 57 35.270 +22 59 08.00 0 13.457 13.075 12.532 11.943 11.634 11.688 20.34 2.99 -43.22 2.99 0.93 12 GCS9 UGCS J035735.26+225907.9 +03 59 29.420 +20 34 29.20 0 16.672 16.008 15.311 14.764 14.373 14.392 16.88 4.05 -38.85 4.05 0.60 12 GCS9 UGCS J035929.42+203429.1 +03 33 49.810 +22 56 19.60 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.83 12 GCS9 Cl* Melotte 22 DH 31 +03 45 10.820 +23 02 58.00 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 0.92 12 GCS9 V* V440 Tau +03 44 38.950 +23 02 25.30 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 351 +03 44 37.780 +22 55 15.50 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.88 12 GCS9 V* NS Tau +03 51 20.680 +19 38 37.80 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.77 12 GCS9 Cl* Melotte 22 DH 705 +03 53 35.390 +21 47 05.70 0 16.517 15.849 15.160 14.619 14.236 14.231 17.16 2.55 -42.61 2.55 0.75 12 GCS9 UGCS J035335.39+214705.6 +03 42 35.650 +21 50 29.70 0 12.761 12.502 12.030 11.527 11.224 11.275 23.68 2.51 -42.67 2.51 0.75 12 GCS9 V* V614 Tau +03 45 08.890 +19 43 29.90 0 16.502 15.850 15.168 14.631 14.237 14.214 17.00 4.02 -40.72 4.02 0.71 12 GCS9 UGCS J034508.89+194329.8 +03 49 41.710 +21 56 19.20 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.72 12 GCS9 Cl* Melotte 22 BPL 214 +03 59 29.330 +21 39 56.00 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.79 12 GCS9 Cl* Melotte 22 DH 878 +03 59 28.420 +19 30 41.20 0 14.604 14.084 13.471 12.921 12.572 12.598 16.99 3.04 -38.71 3.04 0.86 12 GCS9 UGCS J035928.42+193041.1 +03 57 01.510 +19 23 22.10 0 15.564 15.023 14.454 13.871 13.553 13.566 19.45 6.06 -38.27 6.06 0.77 12 GCS9 UGCS J035701.50+192322.0 +03 56 38.760 +21 35 08.30 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.93 12 GCS9 Cl* Melotte 22 DH 841 +04 01 26.060 +21 35 08.50 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.94 12 GCS9 Cl* Melotte 22 DH 900 +04 01 45.820 +21 44 04.90 0 16.644 15.914 15.183 14.661 14.260 14.256 18.38 3.82 -39.06 3.82 0.62 12 GCS9 UGCS J040145.82+214404.8 +03 46 16.080 +21 41 09.90 0 15.678 15.135 14.516 13.979 13.651 13.622 18.91 2.66 -43.14 2.66 0.90 12 GCS9 UGCS J034616.08+214109.8 +03 42 33.110 +21 42 16.60 0 14.159 13.678 13.082 12.475 12.163 12.158 18.09 3.58 -43.09 3.58 0.94 12 GCS9 UGCS J034233.10+214216.6 +03 35 04.720 +25 50 48.00 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.85 12 GCS9 Cl* Melotte 22 DH 41 +03 44 11.920 +19 18 19.10 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 0.77 12 GCS9 Cl* Melotte 22 DH 318 +03 32 57.570 +27 17 19.40 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.80 12 GCS9 Cl* Melotte 22 DH 24 +03 46 40.270 +25 43 53.40 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 464 +03 40 37.820 +21 24 22.50 0 14.983 14.452 13.790 13.151 12.787 12.764 22.77 3.58 -38.56 3.58 0.76 12 GCS9 UGCS J034037.82+212422.5 +03 55 03.610 +21 31 09.60 0 15.425 14.932 14.277 13.754 13.419 13.419 21.47 3.36 -41.78 3.36 0.84 12 GCS9 UGCS J035503.61+213109.6 +03 48 48.230 +21 24 40.50 0 14.474 14.049 13.470 12.887 12.617 12.608 22.67 3.05 -42.13 3.05 0.89 12 GCS9 UGCS J034848.23+212440.4 +03 46 32.790 +19 17 30.20 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 0.90 12 GCS9 Cl* Melotte 22 DH 455 +03 27 54.260 +24 56 10.90 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.93 12 GCS9 Cl* Melotte 22 DH 3 +03 43 28.210 +24 53 30.90 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.87 12 GCS9 V* V436 Tau +03 43 48.450 +25 02 36.70 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.93 12 GCS9 V* V625 Tau +03 44 24.680 +24 51 53.20 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 0.86 12 GCS9 V* V630 Tau +03 49 13.230 +21 22 00.80 0 15.580 15.063 14.449 13.905 13.547 13.560 21.58 3.05 -41.77 3.05 0.83 12 GCS9 UGCS J034913.22+212200.8 +04 00 33.590 +21 27 10.10 0 15.280 14.774 14.134 13.608 13.262 13.253 18.18 3.00 -39.40 3.00 0.84 12 GCS9 UGCS J040033.58+212710.0 +03 50 29.960 +25 03 06.70 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.87 12 GCS9 V* V470 Tau +03 47 44.440 +24 36 55.70 0 15.085 14.555 13.994 13.447 13.132 13.121 14.49 2.21 -41.75 2.21 0.83 12 GCS9 UGCS J034744.44+243655.6 +03 47 38.370 +24 35 59.70 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 0.85 12 GCS9 UGCS J034738.36+243559.6 +03 47 59.380 +24 35 37.00 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 0.87 12 GCS9 V* V1281 Tau +03 48 21.540 +24 34 43.40 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 0.73 12 GCS9 Cl* Melotte 22 DH 571 +03 47 50.950 +24 30 18.60 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 0.92 12 GCS9 V* V654 Tau +03 47 39.360 +24 27 31.90 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 0.94 12 GCS9 V* V457 Tau +03 47 49.790 +24 25 43.10 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 0.87 12 GCS9 V* V866 Tau +03 47 37.660 +24 24 23.10 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 0.84 12 GCS9 V* V1280 Tau +03 47 32.140 +24 24 18.40 0 14.949 14.466 13.873 13.317 13.042 13.004 16.18 2.21 -46.43 2.21 0.88 12 GCS9 UGCS J034732.14+242418.3 +03 38 45.750 +24 28 03.70 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 0.84 12 GCS9 Cl* Melotte 22 DH 94 +03 40 24.200 +24 35 04.00 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.91 12 GCS9 V* V491 Tau +03 40 26.090 +24 32 13.00 0 14.779 14.302 13.776 13.231 12.923 12.924 16.10 2.49 -36.49 2.49 0.61 12 GCS9 UGCS J034026.08+243212.9 +03 35 57.210 +24 36 08.70 0 13.467 13.062 12.538 12.051 11.754 11.753 14.41 2.62 -44.34 2.62 0.87 12 GCS9 UGCS J033557.21+243608.7 +03 51 34.010 +24 34 08.90 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 715 +03 43 26.980 +24 27 09.60 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.92 12 GCS9 Cl* Melotte 22 DH 267 +03 43 20.580 +24 26 34.90 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.88 12 GCS9 V* V620 Tau +03 44 09.610 +24 35 22.10 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.89 12 GCS9 Cl* Melotte 22 DH 314 +03 44 19.060 +24 35 18.20 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.90 12 GCS9 V* V512 Tau +03 43 43.150 +24 32 56.00 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.69 12 GCS9 Cl* Melotte 22 DH 283 +03 44 26.540 +24 29 11.30 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 0.91 12 GCS9 Cl* Melotte 22 DH 338 +03 44 17.760 +24 26 46.70 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.88 12 GCS9 V* V849 Tau +03 54 39.130 +24 35 54.50 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 0.88 12 GCS9 Cl* Melotte 22 DH 806 +03 37 37.700 +26 21 04.00 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.83 12 GCS9 Cl* Melotte 22 DH 76 +03 50 54.990 +24 33 03.50 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 0.85 12 GCS9 Cl* Melotte 22 MBSC 69 +03 51 03.620 +24 32 35.10 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 0.78 12 GCS9 Cl* Melotte 22 DH 691 +03 50 57.410 +24 24 44.40 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 0.83 12 GCS9 2MASS J03505739+2424447 +03 50 15.960 +26 11 37.00 0 16.295 15.586 14.902 14.337 13.979 13.985 18.50 2.96 -41.40 2.96 0.73 12 GCS9 UGCS J035015.96+261137.0 +03 42 56.570 +24 13 45.40 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.92 12 GCS9 Cl* Melotte 22 BPL 47 +03 43 01.270 +24 14 52.20 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 0.68 12 GCS9 Cl* Melotte 22 BPL 50 +03 43 13.470 +24 11 00.90 0 15.692 15.163 14.527 14.001 13.642 13.649 16.42 2.13 -42.52 2.13 0.89 12 GCS9 UGCS J034313.46+241100.9 +03 49 35.290 +25 59 34.60 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 630 +03 49 41.340 +26 08 53.20 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 636 +03 53 35.520 +26 07 08.00 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.87 12 GCS9 Cl* Melotte 22 DH 790 +03 45 43.180 +26 02 26.60 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 401 +03 46 53.610 +24 17 14.80 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 0.85 12 GCS9 V* PV Tau +03 46 55.320 +24 11 16.60 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 478 +03 47 07.880 +24 23 37.80 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 0.80 12 GCS9 V* V1278 Tau +03 47 08.150 +24 18 24.50 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 0.90 12 GCS9 V* V861 Tau +03 47 09.420 +24 15 34.70 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 0.68 12 GCS9 Cl* Melotte 22 DH 496 +03 47 11.030 +24 13 51.50 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 0.90 12 GCS9 V* QS Tau +03 47 11.790 +24 13 31.30 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.73 12 GCS9 Cl* Melotte 22 BPL 130 +03 47 11.860 +24 13 53.80 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 499 +03 35 09.410 +24 14 19.80 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 0.83 12 GCS9 Cl* Melotte 22 DH 42 +03 40 43.200 +22 49 53.80 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 151 +03 49 56.810 +24 59 07.10 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 0.61 12 GCS9 Cl* Melotte 22 KPNO 5 +03 50 06.560 +24 59 46.30 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 653 +03 46 19.860 +24 59 01.30 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 0.88 12 GCS9 Cl* Melotte 22 DH 437 +03 45 12.440 +22 41 50.80 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 0.87 12 GCS9 Cl* Melotte 22 DH 380 +03 52 35.320 +25 01 04.50 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 758 +03 49 21.930 +24 54 43.20 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 0.94 12 GCS9 Cl* Melotte 22 DH 617 +03 37 47.510 +24 53 46.10 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 0.86 12 GCS9 Cl* Melotte 22 DH 78 +03 49 26.640 +22 50 54.50 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 0.91 12 GCS9 Cl* Melotte 22 SK 315 +03 48 17.610 +22 04 00.90 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 0.94 12 GCS9 Cl* Melotte 22 BPL 171 +03 48 42.150 +25 00 28.20 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.92 12 GCS9 V* V461 Tau +03 48 40.600 +25 01 19.80 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 186 +03 41 39.840 +24 52 30.00 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 0.88 12 GCS9 Cl* Melotte 22 HHJ 69 +03 41 47.090 +25 00 21.90 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.92 12 GCS9 Cl* Melotte 22 HHJ 125 +03 42 28.660 +25 01 00.20 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 0.93 12 GCS9 Cl* Melotte 22 HHJ 362 +03 42 00.020 +25 01 47.10 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 201 +03 47 35.860 +24 52 26.70 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 0.91 12 GCS9 V* V752 Tau +03 48 20.290 +24 54 54.90 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 0.94 12 GCS9 V* V344 Tau +03 49 02.970 +21 54 46.90 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 0.75 12 GCS9 Cl* Melotte 22 BPL 197 +03 47 40.960 +21 49 05.00 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 0.80 12 GCS9 UGCS J034740.95+214905.0 +03 59 26.930 +21 48 18.70 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 876 +03 52 01.650 +25 01 29.10 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 0.89 12 GCS9 V* V562 Tau +03 52 03.580 +25 01 13.60 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 737 +03 51 51.560 +21 49 16.80 0 14.207 13.736 13.193 12.644 12.340 12.343 24.16 2.51 -42.78 2.51 0.81 12 GCS9 UGCS J035151.56+214916.8 +03 39 16.750 +24 57 38.50 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 0.85 12 GCS9 Cl* Melotte 22 DH 103 +03 40 20.090 +21 51 33.60 0 15.554 14.955 14.342 13.783 13.442 13.436 12.33 2.51 -42.73 2.51 0.69 12 GCS9 UGCS J034020.08+215133.6 +03 48 29.760 +23 58 05.80 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 0.92 12 GCS9 V* V460 Tau +03 48 10.180 +23 59 20.10 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.88 12 GCS9 Cl* Melotte 22 DH 555 +03 48 33.780 +24 01 58.80 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 582 +03 48 31.840 +24 01 58.60 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 0.93 12 GCS9 V* V872 Tau +03 58 07.690 +20 23 38.10 0 14.755 14.283 13.696 13.187 12.883 12.864 17.14 3.97 -39.44 3.97 0.89 12 GCS9 UGCS J035807.69+202338.0 +03 57 58.500 +20 24 19.50 0 14.806 14.330 13.710 13.193 12.861 12.867 16.74 3.97 -41.74 3.97 0.93 12 GCS9 UGCS J035758.50+202419.4 +04 05 13.750 +24 08 42.70 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.94 12 GCS9 Cl* Melotte 22 DH 915 +03 45 09.460 +23 58 44.70 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 0.72 12 GCS9 Cl* Melotte 22 PPL 2 +03 44 57.340 +23 59 32.30 0 13.672 13.326 12.770 12.194 11.941 11.960 16.63 2.22 -39.69 2.22 0.88 12 GCS9 V* V712 Tau +03 44 27.920 +23 59 59.60 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 342 +03 44 47.330 +24 00 37.70 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 0.94 12 GCS9 V* NW Tau +03 44 53.210 +24 01 06.50 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 359 +03 45 09.810 +24 04 32.70 0 15.935 15.388 14.737 14.174 13.793 13.744 17.63 2.23 -39.69 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 5 +03 45 16.130 +24 07 16.00 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 0.91 12 GCS9 V* V519 Tau +03 48 46.020 +24 10 12.50 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 592 +03 46 06.050 +23 58 19.10 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 0.73 12 GCS9 Cl* Melotte 22 SHF 26 +03 45 42.330 +24 04 11.10 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 0.70 12 GCS9 Cl* Melotte 22 SHF 12 +03 45 39.290 +24 08 20.40 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 398 +03 45 57.910 +24 08 40.90 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 0.90 12 GCS9 Cl* Melotte 22 DH 412 +03 46 04.570 +24 09 55.80 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 0.84 12 GCS9 2MASS J03460455+2409561 +03 49 52.440 +24 03 43.00 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 0.70 12 GCS9 UGCS J034952.43+240342.9 +03 49 16.420 +24 03 49.00 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 0.71 12 GCS9 V* V467 Tau +03 49 55.490 +24 06 05.00 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 641 +03 51 58.350 +23 58 19.30 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 0.65 12 GCS9 Cl* Melotte 22 DH 732 +03 59 23.500 +20 20 16.90 0 15.988 15.360 14.723 14.163 13.823 13.837 20.00 4.01 -37.38 4.01 0.67 12 GCS9 UGCS J035923.50+202016.8 +03 41 31.210 +24 03 24.70 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 177 +03 56 18.600 +23 57 51.60 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.89 12 GCS9 Cl* Melotte 22 HHJ 386 +03 56 28.910 +24 01 54.10 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.93 12 GCS9 Cl* Melotte 22 DH 837 +03 42 17.900 +24 06 57.60 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.94 12 GCS9 Cl* Melotte 22 DH 217 +03 41 52.940 +24 07 24.70 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 0.82 12 GCS9 Cl* Melotte 22 DH 191 +03 42 00.370 +24 10 12.60 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 0.73 12 GCS9 Cl* Melotte 22 BPL 32 +03 42 13.900 +20 21 43.60 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.76 12 GCS9 Cl* Melotte 22 DH 216 +03 46 18.540 +23 59 02.50 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 0.88 12 GCS9 Cl* Melotte 22 SHF 27 +03 46 23.470 +24 01 51.20 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 0.94 12 GCS9 V* V528 Tau +03 46 25.390 +24 09 36.20 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.64 12 GCS9 V* OZ Tau +03 46 35.540 +24 01 35.40 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.89 12 GCS9 2MASS J03463552+2401355 +03 46 43.600 +23 59 42.30 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 467 +03 46 51.440 +24 06 16.10 0 15.287 14.777 14.196 13.624 13.272 13.288 17.34 2.22 -38.74 2.22 0.81 12 GCS9 UGCS J034651.43+240616.1 +03 46 53.960 +24 07 57.10 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 0.78 12 GCS9 Cl* Melotte 22 MHO 8 +03 46 58.260 +24 01 41.30 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 0.79 12 GCS9 UGCS J034658.26+240141.3 +03 46 59.320 +24 01 42.80 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 484 +03 47 10.650 +23 58 16.40 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 0.72 12 GCS9 Cl* Melotte 22 SHF 41 +03 47 13.170 +24 00 45.20 0 16.022 15.428 14.759 14.221 13.828 13.866 20.02 2.23 -40.38 2.23 0.66 12 GCS9 2MASS J03471316+2400453 +03 47 09.180 +24 03 07.70 0 13.411 13.064 12.521 11.964 11.675 11.719 20.92 2.22 -38.72 2.22 0.82 12 GCS9 Cl* Melotte 22 SRS 60765 +03 43 01.640 +20 20 13.80 0 15.588 15.080 14.494 13.912 13.618 13.585 15.94 3.44 -42.59 3.44 0.88 12 GCS9 UGCS J034301.63+202013.7 +03 55 08.980 +24 05 02.50 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 0.90 12 GCS9 Cl* Melotte 22 DH 812 +03 50 57.420 +24 06 30.70 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 0.80 12 GCS9 V* V800 Tau +03 51 19.070 +24 10 13.10 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 0.92 12 GCS9 V* V466 Tau +03 51 55.050 +23 57 42.00 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 0.86 12 GCS9 V* V388 Tau +03 47 25.350 +24 02 56.80 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 0.88 12 GCS9 Cl* Melotte 22 HHJ 427 +03 47 32.000 +24 10 24.70 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 147 +03 47 34.520 +24 02 23.00 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 0.72 12 GCS9 Cl* Melotte 22 DH 523 +03 47 46.400 +24 03 02.30 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 438 +03 48 06.410 +24 06 51.70 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.94 12 GCS9 UGCS J034806.41+240651.6 +03 48 06.640 +24 00 06.70 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 549 +03 48 09.220 +23 58 40.50 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 553 +03 48 13.310 +23 58 46.80 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 0.93 12 GCS9 V* V342 Tau +03 43 28.740 +24 09 06.10 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.90 12 GCS9 Cl* Melotte 22 HHJ 76 +03 58 25.140 +24 00 58.50 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 865 +03 39 09.870 +23 58 52.40 0 16.201 15.649 14.939 14.373 14.000 14.020 15.05 2.62 -40.39 2.62 0.63 12 GCS9 UGCS J033909.86+235852.4 +03 39 35.460 +24 07 06.10 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 0.87 12 GCS9 Cl* Melotte 22 DH 108 +03 39 46.350 +23 58 52.90 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 0.69 12 GCS9 Cl* Melotte 22 DH 113 +03 42 41.180 +24 01 42.70 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.93 12 GCS9 V* LQ Tau +03 42 41.850 +24 00 15.50 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.94 12 GCS9 Cl* Melotte 22 DH 236 +03 42 56.550 +24 04 57.80 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 0.82 12 GCS9 V* LT Tau +03 43 11.650 +24 06 52.40 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 0.74 12 GCS9 Cl* Melotte 22 BPL 52 +03 40 25.620 +24 06 00.00 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 138 +03 40 26.410 +24 05 23.50 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 0.84 12 GCS9 Cl* Melotte 22 SK 778 +03 46 29.730 +21 02 16.70 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.92 12 GCS9 Cl* Melotte 22 DH 452 +03 49 50.380 +20 57 59.90 0 14.307 13.848 13.262 12.675 12.412 12.387 20.01 3.05 -39.37 3.05 0.89 12 GCS9 UGCS J034950.38+205759.8 +03 43 05.730 +21 01 49.10 0 15.042 14.523 13.929 13.374 13.044 13.028 19.59 3.59 -44.27 3.59 0.88 12 GCS9 UGCS J034305.73+210149.1 +03 57 37.100 +21 01 48.00 0 15.678 15.108 14.501 13.963 13.607 13.593 20.33 3.37 -45.03 3.37 0.85 12 GCS9 UGCS J035737.10+210147.9 +03 58 10.250 +20 56 55.60 0 16.121 15.494 14.840 14.289 13.924 13.940 19.36 3.38 -39.83 3.38 0.65 12 GCS9 UGCS J035810.24+205655.5 +03 46 10.100 +26 00 09.00 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.82 12 GCS9 Cl* Melotte 22 HHJ 80 +03 46 19.430 +26 02 35.50 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 0.74 12 GCS9 Cl* Melotte 22 DH 436 +03 45 21.350 +26 05 25.50 0 15.885 15.309 14.637 14.115 13.734 13.722 19.33 2.24 -43.28 2.24 0.89 12 GCS9 UGCS J034521.34+260525.4 +03 52 13.320 +26 08 36.80 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.82 12 GCS9 V* V715 Tau +03 45 41.270 +23 54 09.70 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 0.69 12 GCS9 Cl* Melotte 22 PPL 1 +03 50 22.010 +23 55 30.30 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 0.65 12 GCS9 UGCS J035022.01+235530.3 +03 52 06.720 +24 16 00.40 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 0.68 12 GCS9 2MASS J03520670+2416008 +03 46 14.060 +23 21 56.40 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 0.68 12 GCS9 UGCS J034614.06+232156.4 +03 39 17.050 +22 27 10.90 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 0.69 12 GCS9 2MASS J03391704+2227111 +03 48 04.670 +23 39 30.10 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 0.66 12 GCS9 UGCS J034804.66+233930.1 +03 47 49.450 +23 31 52.80 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 0.65 12 GCS9 UGCS J034749.44+233152.8 +03 41 54.160 +23 05 04.70 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 0.69 12 GCS9 Cl* Melotte 22 MHOBD 3 +03 55 23.080 +24 49 04.90 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 0.72 12 GCS9 Cl* Melotte 22 BPL 327 +03 44 53.120 +23 34 22.80 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 0.67 12 GCS9 UGCS J034453.12+233422.8 +03 41 05.410 +23 32 56.70 0 17.223 16.421 15.660 15.137 14.704 14.689 20.80 2.22 -41.06 2.22 0.68 12 GCS9 UGCS J034105.40+233256.6 +03 49 04.860 +23 33 39.30 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 0.70 12 GCS9 Cl* Melotte 22 IPMBD 20 +03 45 54.960 +23 33 57.80 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 0.72 12 GCS9 Cl* Melotte 22 SHF 21 +03 45 31.370 +24 52 47.40 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 0.66 12 GCS9 2MASS J03453136+2452476 +03 36 54.100 +21 58 02.70 0 17.346 16.486 15.758 15.194 14.752 14.754 19.11 3.00 -39.30 3.00 0.65 12 GCS9 UGCS J033654.10+215802.7 +04 01 24.410 +20 17 15.60 0 17.022 16.289 15.614 15.051 14.645 14.638 22.29 3.22 -42.39 3.22 0.63 12 GCS9 UGCS J040124.40+201715.5 +03 38 10.200 +26 07 33.00 0 17.160 16.444 15.695 15.142 14.702 14.718 18.46 3.04 -40.95 3.04 0.71 12 GCS9 UGCS J033810.20+260733.0 +03 40 55.300 +25 34 57.40 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 0.69 12 GCS9 UGCS J034055.30+253457.3 +03 30 44.540 +25 39 07.70 0 17.609 16.705 15.900 15.296 14.886 14.862 16.76 3.43 -42.84 3.43 0.70 12 GCS9 UGCS J033044.53+253907.6 +03 37 46.600 +26 50 44.50 0 17.381 16.593 15.840 15.292 14.877 14.867 19.58 3.37 -40.72 3.37 0.70 12 GCS9 UGCS J033746.59+265044.5 +03 44 35.160 +25 13 42.80 1 17.656 16.584 15.662 14.985 14.448 14.460 19.33 2.26 -44.97 2.26 0.68 12 GCS9 UGCS J034435.16+251342.7 +03 49 33.960 +22 16 35.90 0 17.942 16.978 16.144 15.535 15.049 15.042 21.92 2.64 -43.77 2.64 0.64 12 GCS9 UGCS J034933.96+221635.8 +03 47 27.720 +22 09 38.50 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 0.72 12 GCS9 Cl* Melotte 22 MHOBD 5 +03 51 44.950 +23 26 39.30 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 0.62 12 GCS9 2MASS J03514491+2326395 +03 50 52.170 +23 27 11.20 1 17.690 16.638 15.700 15.060 14.505 14.555 19.89 2.21 -41.33 2.21 0.71 12 GCS9 UGCS J035052.17+232711.2 +03 26 33.430 +22 39 42.90 0 17.587 16.833 16.109 15.489 15.093 15.096 20.93 5.59 -43.56 5.59 0.69 12 GCS9 UGCS J032633.42+223942.8 +03 42 18.010 +24 55 09.90 0 17.486 16.658 15.876 15.298 14.854 17.65 3.06 -39.29 3.06 0.64 12 GCS9 UGCS J034218.01+245509.9 +03 45 50.660 +24 09 03.50 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 0.65 12 GCS9 2MASS J03455065+2409037 +03 47 50.410 +23 54 47.80 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 0.63 12 GCS9 2MASS J03475038+2354480 +03 55 34.280 +22 23 29.20 0 18.055 17.108 16.189 15.579 15.117 15.096 18.47 2.72 -40.79 2.72 0.71 12 GCS9 UGCS J035534.28+222329.2 +03 40 06.590 +21 08 59.90 0 18.210 17.152 16.272 15.651 15.113 15.123 21.31 3.80 -40.02 3.80 0.68 12 GCS9 UGCS J034006.59+210859.9 +03 47 17.920 +24 22 31.60 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 0.68 12 GCS9 UGCS J034717.92+242231.6 +03 46 34.990 +23 31 14.40 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 0.70 12 GCS9 UGCS J034634.98+233114.4 +03 47 59.740 +22 36 01.80 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 0.69 12 GCS9 2MASS J03475972+2236019 +03 45 50.630 +23 44 36.90 0 18.291 17.301 16.379 15.794 15.257 15.244 20.44 2.31 -41.86 2.31 0.71 12 GCS9 Cl* Melotte 22 NPNPL 1 +03 57 18.480 +21 37 32.30 0 18.299 17.369 16.382 15.808 15.239 15.276 21.09 3.54 -41.34 3.54 0.70 12 GCS9 UGCS J035718.48+213732.3 +03 51 38.960 +24 30 44.80 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 0.64 12 GCS9 UGCS J035138.95+243044.7 +03 46 23.120 +24 20 36.00 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 0.66 12 GCS9 2MASS J03462312+2420363 +03 59 09.870 +21 46 02.00 0 18.325 17.223 16.331 15.747 15.203 15.210 20.58 3.13 -40.99 3.13 0.71 12 GCS9 UGCS J035909.86+214602.0 +03 40 38.100 +26 38 29.20 0 18.747 17.679 16.599 15.993 15.481 15.481 18.85 3.21 -40.47 3.21 0.71 12 GCS9 UGCS J034038.09+263829.2 +03 46 34.250 +23 50 03.60 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 0.66 12 GCS9 Cl* Melotte 22 NPNPL 2 +03 34 20.190 +25 22 05.10 0 19.676 18.250 17.178 16.453 15.945 20.30 6.48 -41.54 6.48 0.66 12 GCS9 UGCS J033420.18+252205.0 +03 43 03.830 +23 54 19.60 0 18.852 17.703 16.751 16.052 15.562 15.535 18.37 2.48 -38.49 2.48 0.66 12 GCS9 2MASS J03430378+2354200 +04 05 51.620 +23 44 48.10 0 19.517 18.119 17.046 16.350 15.809 15.769 19.98 3.68 -42.86 3.68 0.68 12 GCS9 UGCS J040551.61+234448.0 +03 49 04.740 +23 26 43.10 0 18.943 17.724 16.792 16.171 15.598 15.597 17.56 2.58 -40.60 2.58 0.70 12 GCS9 UGCS J034904.73+232643.1 +03 57 13.460 +24 22 51.80 0 19.665 18.458 17.407 16.653 16.109 16.086 17.40 3.82 -42.66 3.82 0.66 12 GCS9 UGCS J035713.46+242251.7 +03 54 05.350 +23 33 59.20 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 0.61 12 GCS9 2MASS J03540532+2333597 +03 40 27.930 +24 12 09.30 0 19.730 18.501 17.352 16.700 16.088 16.073 15.91 3.22 -42.54 3.22 0.62 12 GCS9 Cl* Melotte 22 IPL 84 +03 45 11.730 +23 41 43.60 0 20.311 18.985 17.617 16.886 16.120 16.204 13.04 2.65 -46.83 2.65 0.62 12 GCS9 Cl* Melotte 22 NPNPL 3 +03 44 22.450 +23 39 01.30 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 0.61 12 GCS9 UGCS J034422.44+233901.2 +03 55 47.450 +22 50 50.10 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 0.63 12 GCS9 UGCS J035547.44+225050.0 +03 36 05.920 +23 01 23.70 0 19.381 18.086 17.063 16.451 15.840 15.845 24.12 3.64 -46.32 3.64 0.64 12 GCS9 UGCS J033605.92+230123.6 +03 46 27.100 +21 48 22.60 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 0.65 12 GCS9 UGCS J034627.10+214822.5 +04 00 58.260 +21 33 31.70 0 19.241 18.035 16.948 16.250 15.691 15.702 18.08 4.76 -43.38 4.76 0.67 12 GCS9 UGCS J040058.25+213331.7 +03 42 18.080 +19 07 33.00 0 19.797 18.358 17.264 16.589 16.057 16.053 17.58 5.18 -47.78 5.18 0.65 12 GCS9 UGCS J034218.08+190732.9 +04 07 10.570 +24 59 49.20 0 18.433 17.603 16.733 15.987 15.491 15.459 19.65 3.35 -39.70 3.35 0.70 12 GCS9 UGCS J040710.56+245949.1 +03 42 30.590 +25 02 39.30 0 19.269 18.013 17.004 16.367 15.746 22.01 3.54 -44.77 3.54 0.68 12 GCS9 UGCS J034230.58+250239.2 +03 56 59.900 +24 05 35.50 0 19.904 19.003 17.628 16.950 16.225 16.196 16.25 4.13 -41.75 4.13 0.62 12 GCS9 UGCS J035659.90+240535.4 +03 41 13.210 +24 05 25.60 0 20.248 19.168 17.791 16.898 16.325 16.266 14.11 3.48 -46.41 3.48 0.61 12 GCS9 Cl* Melotte 22 IPL 81 +03 50 16.090 +24 08 34.70 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 0.67 12 GCS9 UGCS J035016.08+240834.7 +03 43 46.290 +23 58 37.90 0 19.124 17.930 16.876 16.248 15.628 20.13 2.62 -45.01 2.62 0.69 12 GCS9 2MASS J03434627+2358382 +03 55 30.190 +21 01 10.20 0 18.713 17.522 16.584 15.983 15.414 15.354 19.46 3.52 -41.16 3.52 0.72 12 GCS9 UGCS J035530.19+210110.1 +03 44 09.010 +27 56 42.00 0 14.980 14.512 13.981 13.444 13.140 13.140 27.76 3.70 -38.32 3.70 2 GCS9 UGCS J034409.01+275642.0 +03 48 31.180 +29 22 39.20 0 13.011 12.688 12.199 11.692 11.374 11.413 5.92 5.84 -27.47 5.84 2 GCS9 UGCS J034831.17+292239.1 +03 47 08.900 +29 02 38.80 0 13.793 13.432 12.890 12.285 12.076 12.094 10.64 5.84 -40.62 5.84 2 GCS9 UGCS J034708.90+290238.8 +03 58 33.710 +28 07 09.10 0 16.950 16.428 15.680 15.099 14.755 14.708 24.72 4.01 -50.41 4.01 2 GCS9 UGCS J035833.70+280709.0 +03 53 07.090 +28 07 33.00 0 13.630 13.247 12.738 12.198 11.946 11.966 28.63 3.74 -50.15 3.74 2 GCS9 UGCS J035307.09+280733.0 +03 44 59.810 +29 12 29.50 0 16.688 16.081 15.393 14.773 14.418 14.415 6.41 5.88 -48.49 5.88 2 GCS9 UGCS J034459.81+291229.4 +03 40 56.060 +28 43 39.00 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 2 GCS9 Cl* Melotte 22 DH 161 +03 31 12.320 +27 05 59.20 0 15.984 15.464 14.836 14.275 13.919 -2.97 7.05 -25.10 7.05 2 GCS9 UGCS J033112.32+270559.2 +03 48 55.420 +28 41 20.70 0 14.701 14.119 13.468 12.911 12.565 12.571 19.52 2.93 -34.58 2.93 2 GCS9 UGCS J034855.41+284120.6 +03 37 17.780 +28 45 35.80 0 14.292 13.909 13.380 12.741 12.457 12.474 11.77 4.93 -40.45 4.93 2 GCS9 UGCS J033717.77+284535.8 +03 56 10.690 +27 11 17.90 0 13.820 13.467 12.963 12.391 12.128 12.123 8.42 3.30 -35.60 3.30 2 GCS9 UGCS J035610.69+271117.8 +03 29 11.270 +27 09 52.50 0 14.529 13.987 13.341 12.786 12.440 27.16 6.92 -25.14 6.92 2 GCS9 UGCS J032911.26+270952.4 +03 29 03.100 +27 04 59.90 0 15.372 14.837 14.194 13.600 13.273 21.10 6.95 -29.46 6.95 2 GCS9 UGCS J032903.10+270459.8 +03 30 11.390 +27 37 55.80 0 13.268 12.939 12.423 12.042 11.670 11.668 15.01 4.88 -33.94 4.88 2 GCS9 UGCS J033011.39+273755.7 +03 45 10.110 +27 40 09.10 0 15.074 14.582 14.010 13.477 13.154 13.152 20.96 3.45 -35.33 3.45 2 GCS9 UGCS J034510.11+274009.0 +03 42 24.950 +27 32 21.50 0 15.402 14.912 14.307 13.812 13.463 13.477 18.17 3.30 -32.77 3.30 2 GCS9 UGCS J034224.95+273221.5 +03 41 23.630 +27 24 16.90 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 2 GCS9 Cl* Melotte 22 DH 171 +03 42 24.930 +27 42 15.40 0 15.788 15.273 14.656 14.111 13.783 13.811 18.89 3.32 -32.08 3.32 2 GCS9 UGCS J034224.92+274215.4 +04 01 21.510 +27 12 33.20 0 14.198 13.815 13.270 12.730 12.466 12.482 26.22 4.23 -38.49 4.23 2 GCS9 UGCS J040121.51+271233.1 +03 53 57.160 +28 10 13.30 0 15.617 15.088 14.479 13.890 13.567 13.573 26.79 3.76 -35.49 3.76 2 GCS9 UGCS J035357.16+281013.2 +03 47 42.860 +28 18 59.10 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 2 GCS9 Cl* Melotte 22 DH 533 +03 43 37.560 +26 32 00.90 0 16.418 15.769 15.103 14.563 14.210 14.181 23.24 2.97 -37.05 2.97 2 GCS9 UGCS J034337.55+263200.8 +03 48 52.950 +27 11 09.20 0 12.550 12.284 11.843 11.345 11.027 11.066 26.03 2.88 -33.77 2.88 2 GCS9 UGCS J034852.95+271109.1 +03 39 28.990 +25 34 55.80 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 2 GCS9 Cl* Melotte 22 HHJ 359 +04 06 08.010 +25 42 34.60 0 16.097 15.463 14.792 14.184 13.818 13.799 11.14 3.39 -47.13 3.39 2 GCS9 UGCS J040608.00+254234.5 +03 43 56.000 +25 36 25.20 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 2 GCS9 Cl* Melotte 22 PLZJ 50 +03 52 16.560 +27 35 51.00 0 15.081 14.622 14.039 13.529 13.200 13.192 11.21 2.89 -39.30 2.89 2 GCS9 UGCS J035216.56+273551.0 +03 53 26.650 +26 11 49.50 0 12.887 12.578 12.116 11.553 11.277 11.275 9.78 2.95 -50.85 2.95 2 GCS9 UGCS J035326.65+261149.5 +03 28 11.330 +26 55 37.00 0 16.205 15.582 14.915 14.351 13.989 25.62 7.01 -37.29 7.01 2 GCS9 UGCS J032811.33+265537.0 +03 36 46.480 +28 15 05.90 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 2 GCS9 Cl* Melotte 22 DH 64 +03 51 05.080 +26 36 51.20 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 2 GCS9 Cl* Melotte 22 MBSC 93 +03 26 13.400 +25 32 35.70 0 13.384 12.899 12.323 11.923 11.539 8.41 6.87 -33.03 6.87 2 GCS9 UGCS J032613.39+253235.7 +03 47 22.280 +25 43 15.00 0 16.268 15.616 14.929 14.374 14.017 13.977 15.45 2.25 -46.31 2.25 2 GCS9 UGCS J034722.28+254315.0 +03 27 47.670 +26 57 06.10 0 15.256 14.836 14.217 13.603 13.265 20.38 6.95 -28.78 6.95 2 GCS9 UGCS J032747.66+265706.0 +03 35 50.290 +25 42 20.50 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 2 GCS9 Cl* Melotte 22 DH 49 +03 39 04.060 +27 00 04.70 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 2 GCS9 Cl* Melotte 22 DH 98 +03 39 53.750 +28 34 56.40 0 14.232 13.794 13.189 12.675 12.384 12.373 25.26 4.93 -55.59 4.93 2 GCS9 UGCS J033953.75+283456.3 +03 50 52.180 +26 11 40.10 0 15.944 15.346 14.710 14.173 13.814 13.827 23.44 2.97 -34.78 2.97 2 GCS9 UGCS J035052.18+261140.0 +04 03 40.670 +25 21 34.50 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 2 GCS9 Cl* Melotte 22 DH 908 +03 37 37.450 +25 41 49.80 0 14.994 14.527 13.953 13.399 13.101 13.070 12.66 2.96 -38.52 2.96 2 GCS9 UGCS J033737.45+254149.7 +03 50 39.330 +26 16 03.70 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 2 GCS9 Cl* Melotte 22 DH 680 +04 00 39.240 +26 44 19.00 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 2 GCS9 2MASS J04003922+2644194 +03 37 36.010 +26 32 48.40 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 2 GCS9 Cl* Melotte 22 DH 75 +03 43 04.370 +25 26 12.00 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 2 GCS9 Cl* Melotte 22 HHJ 107 +03 43 38.900 +27 01 01.20 0 15.573 15.021 14.385 13.839 13.499 13.516 25.77 2.95 -39.98 2.95 2 GCS9 UGCS J034338.89+270101.1 +03 48 43.140 +26 32 20.90 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 2 GCS9 Cl* Melotte 22 DH 588 +03 39 44.370 +26 18 18.80 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 2 GCS9 Cl* Melotte 22 MBSC 82 +03 33 08.260 +26 31 13.00 0 16.896 16.129 15.414 14.825 14.421 14.415 19.68 3.01 -38.39 3.01 2 GCS9 UGCS J033308.26+263113.0 +03 37 10.510 +25 17 34.60 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 2 GCS9 Cl* Melotte 22 DH 67 +03 36 42.460 +25 19 26.50 0 15.840 15.296 14.666 14.163 13.816 13.794 15.86 2.97 -36.53 2.97 2 GCS9 UGCS J033642.46+251926.4 +04 06 52.120 +26 21 12.20 0 15.224 14.702 14.074 13.477 13.127 13.144 10.04 2.97 -34.88 2.97 2 GCS9 UGCS J040652.11+262112.2 +03 29 07.680 +25 21 43.50 0 15.576 15.018 14.363 13.811 13.445 13.438 26.60 3.37 -35.82 3.37 2 GCS9 UGCS J032907.68+252143.5 +04 00 33.240 +25 30 20.50 0 15.605 15.106 14.432 13.755 13.426 13.420 24.06 3.32 -32.29 3.32 2 GCS9 UGCS J040033.23+253020.4 +03 32 36.290 +26 58 10.00 0 16.087 15.489 14.800 14.224 13.863 13.827 21.67 2.97 -39.87 2.97 2 GCS9 UGCS J033236.29+265810.0 +03 49 16.180 +26 49 02.90 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 2 GCS9 2MASS J03491617+2649031 +03 41 48.050 +26 47 20.70 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 2 GCS9 Cl* Melotte 22 DH 188 +03 53 29.350 +26 40 42.90 0 12.928 12.672 12.185 11.564 11.326 11.333 12.54 2.95 -40.06 2.95 2 GCS9 UGCS J035329.34+264042.9 +03 53 29.690 +26 40 49.60 0 12.976 12.631 12.090 11.488 11.193 11.211 12.01 2.95 -40.62 2.95 2 GCS9 UGCS J035329.69+264049.5 +03 24 59.740 +25 34 04.50 0 16.878 16.243 15.582 14.987 14.606 38.59 7.21 -49.57 7.21 2 GCS9 UGCS J032459.74+253404.4 +03 31 29.600 +26 30 12.10 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 2 GCS9 Cl* Melotte 22 DH 16 +03 53 04.920 +27 01 42.30 0 14.277 13.874 13.309 12.686 12.394 12.404 9.69 2.95 -37.92 2.95 2 GCS9 UGCS J035304.92+270142.2 +03 38 54.160 +24 42 15.60 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 2 GCS9 Cl* Melotte 22 HHJ 63 +03 49 28.800 +26 50 51.40 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 2 GCS9 Cl* Melotte 22 MBSC 95 +03 36 27.370 +24 41 17.10 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 2 GCS9 V* KK Tau +03 36 38.910 +24 38 48.70 0 15.678 15.146 14.550 14.008 13.684 13.736 21.00 2.64 -36.23 2.64 2 GCS9 UGCS J033638.90+243848.7 +03 51 25.880 +24 47 38.70 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 2 GCS9 V* V558 Tau +03 35 19.700 +26 33 10.60 0 14.411 13.963 13.402 12.862 12.572 12.563 15.89 3.30 -32.50 3.30 2 GCS9 UGCS J033519.70+263310.6 +03 47 05.790 +23 45 34.70 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 2 GCS9 UGCS J034705.79+234534.7 +03 43 29.900 +24 39 23.40 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 2 GCS9 Cl* Melotte 22 DH 269 +03 27 11.130 +26 00 29.20 0 14.463 14.069 13.521 12.933 12.651 9.47 6.88 -28.40 6.88 2 GCS9 UGCS J032711.12+260029.1 +03 32 51.370 +25 54 56.70 0 15.038 14.557 13.922 13.352 13.023 14.47 5.31 -36.68 5.31 2 GCS9 UGCS J033251.37+255456.7 +03 31 20.710 +25 57 33.60 1 16.847 16.068 15.309 14.745 14.321 14.328 21.73 3.39 -38.42 3.39 2 GCS9 UGCS J033120.70+255733.5 +03 37 16.710 +25 44 11.10 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 2 GCS9 Cl* Melotte 22 DH 70 +03 41 38.870 +22 16 40.30 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 2 GCS9 Cl* Melotte 22 DH 183 +03 42 10.600 +22 18 05.50 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 2 GCS9 Cl* Melotte 22 HHJ 55 +03 48 36.340 +25 15 41.20 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 2 GCS9 Cl* Melotte 22 BPL 183 +03 42 01.680 +22 23 26.60 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 2 GCS9 V* V495 Tau +04 01 28.430 +23 30 59.60 1 16.318 15.539 14.772 14.202 13.788 13.788 24.55 3.37 -39.20 3.37 2 GCS9 UGCS J040128.42+233059.6 +03 53 07.290 +25 47 21.40 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 2 GCS9 Cl* Melotte 22 DH 778 +03 34 41.550 +26 09 27.10 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 2 GCS9 Cl* Melotte 22 DH 36 +03 34 46.960 +26 05 38.40 0 14.123 13.758 13.233 12.636 12.369 9.36 5.31 -55.43 5.31 2 GCS9 UGCS J033446.95+260538.4 +03 43 34.490 +25 57 30.60 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 2 GCS9 UGCS J034334.48+255730.5 +03 56 52.310 +25 10 05.10 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2 GCS9 Cl* Melotte 22 HHJ 17 +03 41 42.410 +23 54 57.10 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 2 GCS9 Cl* Melotte 22 STAR 5 +03 40 55.050 +22 20 58.70 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 2 GCS9 V* V430 Tau +03 56 40.450 +22 08 46.50 0 15.445 14.885 14.241 13.710 13.373 13.371 21.46 2.51 -48.69 2.51 2 GCS9 UGCS J035640.44+220846.5 +03 28 26.620 +26 02 11.60 0 16.852 16.148 15.367 14.824 14.432 4.01 7.08 -51.89 7.08 2 GCS9 UGCS J032826.62+260211.5 +03 57 40.680 +25 16 04.10 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 2 GCS9 Cl* Melotte 22 DH 856 +03 43 25.970 +25 58 42.10 0 15.160 14.687 14.062 13.537 13.179 13.218 11.69 2.23 -36.19 2.23 2 GCS9 UGCS J034325.96+255842.1 +03 44 31.040 +22 15 14.90 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2 GCS9 Cl* Melotte 22 DH 344 +03 59 59.860 +22 05 29.30 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 2 GCS9 Cl* Melotte 22 DH 884 +03 27 16.170 +25 51 42.80 0 14.414 14.020 13.433 12.884 12.573 7.75 6.88 -24.82 6.88 2 GCS9 UGCS J032716.16+255142.7 +03 45 11.520 +21 14 29.30 0 16.421 15.764 15.109 14.536 14.148 14.145 25.46 2.68 -40.57 2.68 2 GCS9 UGCS J034511.51+211429.2 +03 54 24.000 +23 51 58.70 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 2 GCS9 UGCS J035423.99+235158.7 +03 26 24.300 +24 25 42.40 0 13.990 13.633 13.081 12.464 12.209 1.09 6.95 -51.34 6.95 2 GCS9 UGCS J032624.30+242542.3 +04 00 03.210 +22 24 46.00 1 16.534 15.875 15.128 14.550 14.132 14.141 15.92 2.87 -33.98 2.87 2 GCS9 UGCS J040003.20+222445.9 +03 28 02.300 +25 55 26.60 0 14.028 13.631 13.111 12.505 12.216 32.02 6.88 -55.34 6.88 2 GCS9 UGCS J032802.30+255526.5 +03 43 24.990 +23 52 01.20 0 16.867 16.116 15.368 14.820 14.365 14.16 2.34 -46.02 2.34 2 GCS9 2MASS J03432497+2352017 +04 03 30.230 +25 16 04.50 0 13.760 13.396 12.828 12.208 11.890 11.912 26.34 2.89 -36.38 2.89 2 GCS9 UGCS J040330.22+251604.5 +03 59 42.660 +21 12 14.90 0 15.123 14.590 13.965 13.401 13.060 13.062 28.07 3.76 -38.75 3.76 2 GCS9 UGCS J035942.66+211214.8 +04 02 55.110 +25 53 53.50 0 16.259 15.667 15.021 14.464 14.087 14.062 19.80 3.33 -38.23 3.33 2 GCS9 UGCS J040255.10+255353.5 +03 58 17.430 +22 11 52.70 1 16.259 15.503 14.768 14.193 13.787 13.778 20.64 2.89 -34.95 2.89 2 GCS9 UGCS J035817.43+221152.6 +04 09 15.300 +25 05 36.60 0 13.026 12.757 12.256 11.647 11.441 11.430 24.83 2.98 -38.40 2.98 2 GCS9 2MASS J04091529+2505368 +03 34 13.470 +25 25 25.30 0 14.776 14.276 13.694 13.124 12.802 25.97 5.29 -32.89 5.29 2 GCS9 UGCS J033413.46+252525.3 +03 59 59.850 +25 08 53.60 1 16.368 15.695 15.004 14.433 14.029 14.075 14.87 2.51 -35.69 2.51 2 GCS9 UGCS J035959.84+250853.6 +03 32 32.970 +22 18 12.10 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 2 GCS9 Cl* Melotte 22 DH 20 +03 56 22.310 +21 07 16.90 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 2 GCS9 Cl* Melotte 22 DH 831 +03 45 13.410 +23 31 00.70 0 13.938 13.443 12.795 12.223 11.868 11.886 12.02 2.24 -39.32 2.24 2 GCS9 V* OO Tau +03 46 03.450 +24 20 57.00 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 2 GCS9 V* V856 Tau +03 45 52.760 +23 27 54.00 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 2 GCS9 V* V747 Tau +03 40 49.400 +21 12 55.30 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 2 GCS9 Cl* Melotte 22 DH 152 +03 52 02.280 +24 21 47.90 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 2 GCS9 Cl* Melotte 22 DH 734 +03 39 10.220 +19 55 30.60 0 14.068 13.656 13.061 12.514 12.192 12.201 18.29 5.01 -32.86 5.01 2 GCS9 UGCS J033910.21+195530.5 +03 39 37.760 +19 55 53.30 0 14.324 13.934 13.412 12.811 12.532 12.528 4.36 5.02 -36.62 5.02 2 GCS9 UGCS J033937.75+195553.3 +03 47 31.130 +21 10 51.10 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 2 GCS9 Cl* Melotte 22 DH 519 +03 54 49.920 +19 50 44.90 0 14.727 14.266 13.691 13.126 12.808 12.842 8.65 6.05 -45.98 6.05 2 GCS9 UGCS J035449.91+195044.9 +04 05 50.080 +22 35 53.80 0 14.329 13.919 13.378 12.805 12.498 12.507 14.32 4.97 -32.31 4.97 2 GCS9 UGCS J040550.08+223553.8 +03 54 38.020 +19 54 22.50 0 14.169 13.723 13.136 12.596 12.291 12.305 14.93 6.05 -51.92 6.05 2 GCS9 UGCS J035438.01+195422.5 +03 37 10.870 +21 12 09.70 0 15.115 14.622 14.008 13.457 13.133 13.127 27.96 3.41 -35.33 3.41 2 GCS9 UGCS J033710.86+211209.7 +03 57 19.330 +23 27 37.60 0 15.039 14.541 13.948 13.395 13.065 13.076 17.31 3.00 -34.18 3.00 2 GCS9 2MASS J03571931+2327379 +03 42 57.780 +19 47 06.10 0 16.796 16.118 15.392 14.796 14.398 14.370 19.85 4.07 -37.58 4.07 2 GCS9 UGCS J034257.78+194706.1 +03 37 28.280 +24 24 09.10 0 12.968 12.703 12.189 11.596 11.337 11.397 15.93 2.50 -35.80 2.50 2 GCS9 UGCS J033728.27+242409.1 +03 42 13.490 +24 18 49.60 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 2 GCS9 Cl* Melotte 22 BPL 36 +03 41 51.500 +24 19 27.30 0 16.617 15.976 15.244 14.667 14.283 16.53 2.33 -37.48 2.33 2 GCS9 Cl* Melotte 22 BPL 27 +03 41 38.810 +24 23 09.20 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2 GCS9 Cl* Melotte 22 BPL 25 +03 53 53.320 +22 30 37.50 0 15.687 15.153 14.514 13.995 13.648 13.634 25.44 2.52 -43.14 2.52 2 GCS9 UGCS J035353.32+223037.5 +03 32 37.820 +23 25 59.00 0 15.028 14.538 13.929 13.363 13.041 13.050 20.57 3.42 -34.09 3.42 2 GCS9 UGCS J033237.82+232559.0 +03 41 45.070 +22 28 01.80 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2 GCS9 Cl* Melotte 22 DH 185 +03 54 15.600 +24 20 45.70 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 2 GCS9 Cl* Melotte 22 BPL 308 +04 02 41.900 +22 28 18.30 0 16.439 15.775 15.058 14.528 14.129 14.101 18.57 3.42 -47.04 3.42 2 GCS9 UGCS J040241.90+222818.2 +03 40 28.570 +23 33 42.20 0 16.820 16.083 15.334 14.796 14.375 14.420 24.86 2.54 -48.66 2.54 2 GCS9 UGCS J034028.56+233342.2 +03 42 06.940 +19 54 06.00 0 15.496 15.005 14.353 13.759 13.458 13.462 10.23 5.03 -28.73 5.03 2 GCS9 UGCS J034206.94+195405.9 +03 47 56.640 +24 15 31.70 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 2 GCS9 Cl* Melotte 22 BPL 162 +03 44 09.920 +24 16 03.90 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 2 GCS9 V* V629 Tau +03 47 55.280 +23 19 05.80 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2 GCS9 V* PS Tau +03 44 20.660 +24 15 10.70 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 2 GCS9 Cl* Melotte 22 HHJ 57 +03 40 15.930 +24 22 31.30 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 2 GCS9 Cl* Melotte 22 BPL 11 +04 01 05.810 +23 34 43.90 0 14.117 13.665 13.116 12.586 12.289 26.79 3.54 -44.13 3.54 2 GCS9 UGCS J040105.81+233443.8 +04 00 50.520 +23 43 52.90 1 16.184 15.380 14.647 14.089 13.660 24.52 3.56 -40.90 3.56 2 GCS9 UGCS J040050.52+234352.9 +03 59 12.920 +24 17 18.40 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 2 GCS9 Cl* Melotte 22 DH 873 +03 43 18.750 +20 07 44.40 0 14.292 13.846 13.306 12.791 12.491 12.478 12.00 3.42 -35.92 3.42 2 GCS9 UGCS J034318.74+200744.3 +03 28 16.070 +22 27 32.10 0 16.039 15.477 14.850 14.262 13.924 13.920 26.29 5.04 -36.31 5.04 2 GCS9 UGCS J032816.07+222732.0 +03 42 58.600 +20 12 45.00 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 2 GCS9 Cl* Melotte 22 DH 247 +03 50 43.070 +23 19 55.70 0 16.002 15.424 14.795 14.265 13.908 13.920 23.59 2.14 -41.03 2.14 2 GCS9 UGCS J035043.06+231955.6 +03 55 30.900 +23 23 50.90 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 2 GCS9 Cl* Melotte 22 DH 815 +03 41 20.000 +22 37 53.70 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 2 GCS9 Cl* Melotte 22 DH 168 +03 37 26.250 +22 34 30.90 0 16.766 16.113 15.394 14.869 14.450 14.467 17.90 2.97 -38.24 2.97 2 GCS9 UGCS J033726.25+223430.8 +04 03 49.740 +23 43 25.20 0 16.769 16.047 15.317 14.756 14.349 14.341 25.63 3.40 -44.29 3.40 2 GCS9 UGCS J040349.73+234325.1 +03 58 52.130 +24 43 48.30 0 14.398 14.003 13.466 12.869 12.581 12.617 18.41 2.48 -35.59 2.48 2 GCS9 UGCS J035852.13+244348.3 +03 38 34.490 +23 40 22.30 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 2 GCS9 Cl* Melotte 22 DH 91 +03 42 05.740 +23 07 14.40 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2 GCS9 UGCS J034205.74+230714.3 +03 26 44.060 +24 39 39.40 0 14.123 13.658 13.081 12.585 12.255 18.85 6.95 -22.57 6.95 2 GCS9 UGCS J032644.06+243939.4 +03 43 26.440 +22 42 42.50 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 2 GCS9 V* V846 Tau +03 54 31.490 +22 39 01.50 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 2 GCS9 UGCS J035431.48+223901.5 +03 45 37.760 +23 43 50.10 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2 GCS9 Cl* Melotte 22 SHF 10 +03 30 43.820 +27 27 42.80 0 16.033 15.515 14.892 14.273 13.976 13.989 4.89 4.92 -41.76 4.92 2 GCS9 UGCS J033043.82+272742.8 +03 48 57.410 +23 13 59.10 1 16.835 16.033 15.222 14.673 14.194 14.194 14.93 2.17 -36.62 2.17 2 GCS9 UGCS J034857.40+231359.0 +03 33 14.390 +24 50 08.10 0 14.746 14.237 13.646 13.119 12.824 12.808 25.31 2.63 -48.57 2.63 2 GCS9 UGCS J033314.38+245008.1 +03 44 25.510 +22 27 49.80 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 2 GCS9 2MASS J03442549+2227501 +03 44 29.500 +22 25 24.70 0 15.948 15.406 14.761 14.222 13.858 13.860 23.26 2.52 -37.84 2.52 2 GCS9 UGCS J034429.49+222524.6 +03 28 19.340 +24 49 48.70 0 13.462 13.155 12.649 12.117 11.814 11.03 6.95 -38.57 6.95 2 GCS9 UGCS J032819.34+244948.6 +03 28 05.420 +24 46 50.80 0 14.243 13.810 13.304 12.740 12.453 16.36 6.95 -28.13 6.95 2 GCS9 UGCS J032805.42+244650.7 +03 28 56.800 +24 45 20.20 0 14.988 14.482 13.880 13.310 13.039 7.77 6.96 -34.47 6.96 2 GCS9 UGCS J032856.79+244520.2 +03 37 34.180 +24 42 15.10 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2 GCS9 Cl* Melotte 22 DH 74 +03 37 03.480 +24 44 35.30 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 2 GCS9 V* KL Tau +03 56 28.630 +23 09 03.40 0 13.707 13.314 12.787 12.207 11.924 11.944 17.30 2.99 -35.17 2.99 2 GCS9 UGCS J035628.63+230903.4 +04 01 55.860 +24 44 02.30 0 14.205 13.759 13.190 12.627 12.326 12.347 16.22 2.89 -35.93 2.89 2 GCS9 UGCS J040155.85+244402.3 +03 40 56.930 +29 01 09.10 0 14.565 14.058 13.458 12.891 12.549 12.569 11.58 2.96 -44.15 2.96 2 GCS9 UGCS J034056.93+290109.1 +03 37 37.090 +23 10 57.80 0 14.626 14.203 13.619 13.102 12.764 12.763 21.66 2.98 -35.09 2.98 2 GCS9 UGCS J033737.08+231057.8 +04 06 29.990 +22 33 43.60 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 2 GCS9 Cl* Melotte 22 DH 916 +04 06 48.060 +22 26 38.90 0 15.098 14.674 14.081 13.428 13.126 13.129 5.85 5.08 -27.50 5.08 2 GCS9 UGCS J040648.05+222638.9 +04 10 20.200 +22 49 44.20 0 16.876 16.094 15.352 14.801 14.394 14.366 31.66 6.12 -35.95 6.12 2 GCS9 UGCS J041020.20+224944.2 +04 10 52.290 +22 40 50.70 0 16.595 16.009 15.382 14.707 14.334 14.306 28.77 6.10 -34.40 6.10 2 GCS9 UGCS J041052.28+224050.7 +03 38 25.430 +24 27 11.90 0 16.581 15.897 15.248 14.690 14.314 14.316 23.60 2.52 -36.23 2.52 2 GCS9 UGCS J033825.42+242711.8 +03 49 43.170 +24 39 46.40 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 2 GCS9 Cl* Melotte 22 BPL 215 +03 40 09.690 +23 10 32.40 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 2 GCS9 V* KW Tau +03 40 12.740 +23 09 35.60 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 2 GCS9 V* V429 Tau +04 02 34.770 +23 08 28.40 0 14.671 14.234 13.621 13.093 12.774 12.817 13.37 3.35 -33.49 3.35 2 GCS9 UGCS J040234.77+230828.4 +03 49 27.580 +24 24 13.70 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 2 GCS9 Cl* Melotte 22 DH 621 +03 49 27.650 +24 31 54.10 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 2 GCS9 V* V359 Tau +03 42 31.210 +24 49 21.20 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 2 GCS9 V* V611 Tau +03 41 52.320 +24 41 57.60 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 2 GCS9 Cl* Melotte 22 DH 190 +03 38 13.040 +24 38 16.80 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 2 GCS9 Cl* Melotte 22 DH 84 +04 06 49.560 +23 09 03.60 0 13.930 13.558 12.993 12.404 12.134 12.138 15.11 3.74 -32.63 3.74 2 GCS9 UGCS J040649.55+230903.6 +03 34 19.370 +22 48 42.20 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 2 GCS9 Cl* Melotte 22 DH 34 +03 57 05.170 +20 44 16.30 0 16.134 15.509 14.849 14.335 13.970 13.945 23.22 4.01 -41.34 4.01 2 GCS9 UGCS J035705.17+204416.3 +03 50 09.720 +20 41 54.00 0 16.759 16.075 15.362 14.808 14.395 14.384 17.58 3.49 -35.38 3.49 2 GCS9 UGCS J035009.71+204154.0 +03 25 45.370 +22 56 36.80 0 13.572 13.247 12.749 12.147 11.900 11.922 8.91 5.07 -37.88 5.07 2 GCS9 UGCS J032545.37+225636.8 +03 25 38.730 +22 57 39.80 1 15.890 15.269 14.601 13.974 13.618 13.628 24.45 5.11 -47.02 5.11 2 GCS9 UGCS J032538.72+225739.8 +03 50 24.960 +20 42 17.80 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 2 GCS9 Cl* Melotte 22 DH 668 +04 01 50.950 +22 59 15.50 0 16.363 15.631 14.906 14.311 13.912 13.906 20.72 3.38 -38.98 3.38 2 GCS9 UGCS J040150.95+225915.5 +03 41 13.500 +20 51 17.20 0 13.069 12.653 12.096 11.582 11.204 11.227 22.59 3.42 -36.61 3.42 2 GCS9 UGCS J034113.49+205117.2 +03 44 08.810 +23 04 47.50 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 2 GCS9 V* MV Tau +03 53 43.800 +27 50 19.20 0 16.584 16.006 15.337 14.720 14.344 14.344 13.37 3.36 -34.48 3.36 2 GCS9 UGCS J035343.79+275019.1 +03 47 15.660 +19 42 10.50 0 14.049 13.614 13.070 12.494 12.226 12.227 8.84 5.04 -28.59 5.04 2 GCS9 UGCS J034715.66+194210.4 +03 52 30.540 +19 40 39.60 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 2 GCS9 Cl* Melotte 22 DH 753 +03 59 16.640 +19 44 27.20 0 14.532 14.106 13.533 12.964 12.659 12.670 11.83 3.04 -34.49 3.04 2 GCS9 UGCS J035916.64+194427.1 +03 55 23.970 +19 36 48.70 0 12.665 12.398 11.904 11.358 11.013 11.061 27.55 6.05 -29.31 6.05 2 GCS9 UGCS J035523.96+193648.7 +03 40 35.340 +21 33 33.30 0 14.187 13.742 13.238 12.559 12.246 12.253 9.37 3.58 -32.98 3.58 2 GCS9 UGCS J034035.33+213333.3 +03 40 47.810 +21 43 21.70 0 15.850 15.249 14.624 14.026 13.626 13.638 20.22 3.60 -35.96 3.60 2 GCS9 UGCS J034047.80+214321.6 +03 55 50.110 +21 43 27.70 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 2 GCS9 Cl* Melotte 22 DH 819 +03 58 50.060 +21 36 52.90 0 15.384 14.775 14.109 13.558 13.179 13.201 24.07 3.76 -38.06 3.76 2 GCS9 UGCS J035850.06+213652.8 +03 44 52.870 +21 34 16.10 0 14.371 13.850 13.252 12.733 12.411 12.398 16.20 2.65 -35.83 2.65 2 GCS9 UGCS J034452.86+213416.0 +03 40 10.500 +21 44 45.50 0 14.286 13.854 13.282 12.790 12.436 12.464 26.94 3.58 -45.88 3.58 2 GCS9 UGCS J034010.50+214445.4 +03 56 33.510 +19 33 25.20 0 16.665 15.941 15.228 14.681 14.293 14.284 22.45 6.10 -29.57 6.10 2 GCS9 UGCS J035633.50+193325.1 +03 56 49.390 +19 30 05.50 0 13.596 13.242 12.727 12.205 11.878 11.890 12.45 6.05 -26.16 6.05 2 GCS9 UGCS J035649.38+193005.4 +03 56 23.930 +19 23 53.40 0 14.848 14.335 13.702 13.158 12.840 12.830 12.00 6.05 -30.52 6.05 2 GCS9 UGCS J035623.92+192353.3 +03 45 46.940 +21 44 49.00 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 2 GCS9 Cl* Melotte 22 HHJ 128 +03 41 14.460 +19 20 30.70 0 12.714 12.465 12.013 11.465 11.274 11.312 32.96 5.01 -35.20 5.01 2 GCS9 UGCS J034114.45+192030.7 +03 57 09.810 +21 36 27.20 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 2 GCS9 Cl* Melotte 22 DH 850 +03 50 29.740 +19 21 01.50 0 16.507 15.848 15.150 14.568 14.214 14.238 13.83 4.07 -37.81 4.07 2 GCS9 UGCS J035029.73+192101.5 +03 30 57.410 +27 16 46.30 0 16.086 15.546 14.839 14.272 13.954 5.81 7.01 -21.54 7.01 2 GCS9 UGCS J033057.40+271646.2 +03 42 38.220 +19 28 57.40 0 15.981 15.445 14.822 14.201 13.860 13.854 7.36 4.00 -35.21 4.00 2 GCS9 UGCS J034238.21+192857.4 +03 49 22.520 +21 37 56.00 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 2 GCS9 Cl* Melotte 22 DH 619 +03 39 57.850 +25 55 29.70 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 2 GCS9 Cl* Melotte 22 DH 120 +03 40 11.930 +25 52 32.30 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 2 GCS9 Cl* Melotte 22 HHJ 29 +03 32 11.550 +21 27 55.70 1 16.385 15.652 14.906 14.339 13.949 13.920 13.64 3.89 -40.00 3.89 2 GCS9 UGCS J033211.55+212755.7 +03 43 27.900 +25 01 40.90 0 15.717 15.051 14.370 13.818 13.439 10.73 2.98 -49.42 2.98 2 GCS9 UGCS J034327.89+250140.9 +03 44 11.280 +24 52 34.20 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 2 GCS9 Cl* Melotte 22 HHJ 59 +03 39 03.500 +21 25 19.90 0 14.680 14.219 13.605 13.055 12.701 12.734 25.01 3.41 -38.17 3.41 2 GCS9 UGCS J033903.49+212519.9 +03 57 14.880 +21 31 01.30 0 13.657 13.248 12.707 12.190 11.892 11.934 25.20 3.36 -39.29 3.36 2 GCS9 UGCS J035714.87+213101.3 +04 09 48.530 +24 54 00.50 0 15.210 14.746 14.051 13.310 12.940 21.67 4.44 -35.52 4.44 2 GCS9 UGCS J040948.53+245400.4 +03 43 43.710 +24 29 15.50 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2 GCS9 Cl* Melotte 22 DH 285 +03 38 24.800 +26 15 23.50 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 2 GCS9 Cl* Melotte 22 DH 87 +04 05 41.700 +24 31 35.10 0 16.378 15.833 15.175 14.498 14.178 14.167 24.40 2.97 -34.22 2.97 2 GCS9 UGCS J040541.70+243135.0 +03 41 58.860 +26 12 21.10 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 2 GCS9 V* KQ Tau +03 33 32.390 +26 08 10.20 0 15.097 14.615 13.992 13.411 13.059 15.67 5.32 -27.22 5.32 2 GCS9 UGCS J033332.39+260810.1 +03 43 16.110 +24 22 28.00 0 16.600 15.927 15.220 14.697 14.293 14.308 13.35 2.16 -47.16 2.16 2 GCS9 UGCS J034316.11+242227.9 +03 49 36.320 +26 02 16.30 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 2 GCS9 Cl* Melotte 22 DH 633 +03 46 52.970 +24 15 07.80 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 2 GCS9 Cl* Melotte 22 MHO 7 +03 47 01.850 +24 13 28.10 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 2 GCS9 Cl* Melotte 22 MHO 10 +03 47 15.460 +24 23 31.00 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 2 GCS9 Cl* Melotte 22 MHO 12 +03 52 55.920 +24 57 41.80 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2 GCS9 Cl* Melotte 22 BPL 275 +03 44 25.580 +22 40 07.90 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 2 GCS9 UGCS J034425.58+224007.8 +03 37 30.690 +24 50 54.30 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 2 GCS9 Cl* Melotte 22 HHJ 110 +04 00 15.860 +25 01 46.30 0 13.460 13.089 12.543 11.954 11.659 11.681 10.70 2.89 -39.78 2.89 2 GCS9 UGCS J040015.86+250146.2 +03 49 05.180 +22 04 52.70 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 2 GCS9 UGCS J034905.17+220452.6 +04 04 38.550 +21 46 47.70 0 13.894 13.477 12.956 12.429 12.123 12.120 29.74 5.07 -28.67 5.07 2 GCS9 UGCS J040438.55+214647.7 +04 07 29.340 +21 46 09.80 0 14.170 13.699 13.175 12.658 12.334 12.339 12.98 4.97 -51.85 4.97 2 GCS9 UGCS J040729.34+214609.8 +03 59 39.840 +21 54 27.70 0 13.543 13.158 12.617 12.028 11.764 11.777 10.82 2.84 -38.32 2.84 2 GCS9 UGCS J035939.84+215427.7 +03 40 11.980 +21 48 31.80 1 16.578 15.884 15.169 14.580 14.187 14.178 24.16 2.54 -40.67 2.54 2 GCS9 UGCS J034011.98+214831.7 +03 45 57.710 +24 03 04.90 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 2 GCS9 Cl* Melotte 22 HHJ 27 +03 37 53.260 +20 23 01.30 0 14.549 14.107 13.550 13.014 12.698 12.732 23.35 3.86 -53.40 3.86 2 GCS9 UGCS J033753.25+202301.2 +03 32 00.120 +20 23 45.90 0 16.268 15.657 15.038 14.458 14.131 14.118 27.13 6.12 -30.46 6.12 2 GCS9 UGCS J033200.11+202345.8 +03 49 56.070 +20 22 52.30 0 14.520 14.010 13.453 12.888 12.591 12.601 27.18 3.42 -44.80 3.42 2 GCS9 UGCS J034956.07+202252.2 +03 46 26.090 +24 05 09.50 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 2 GCS9 2MASS J03462608+2405096 +03 51 04.740 +20 14 06.10 0 14.534 14.114 13.550 12.881 12.645 12.663 15.57 3.42 -33.25 3.42 2 GCS9 UGCS J035104.73+201406.1 +03 50 33.870 +20 19 38.30 0 16.290 15.620 14.949 14.435 14.012 14.021 24.49 3.44 -42.26 3.44 2 GCS9 UGCS J035033.86+201938.3 +03 55 34.430 +23 58 28.30 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 2 GCS9 Cl* Melotte 22 DH 818 +03 36 19.260 +24 10 52.30 0 13.345 13.022 12.518 11.979 11.704 11.709 25.71 2.60 -41.47 2.60 2 GCS9 UGCS J033619.26+241052.3 +03 42 40.230 +23 59 21.50 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 2 GCS9 V* LO Tau +03 28 35.630 +24 00 43.70 0 16.995 16.267 15.527 14.966 14.552 14.537 19.57 3.89 -36.94 3.89 2 GCS9 UGCS J032835.63+240043.7 +03 46 09.880 +21 05 52.40 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 2 GCS9 Cl* Melotte 22 DH 425 +03 46 22.310 +21 05 20.00 0 16.232 15.519 14.873 14.327 13.935 13.935 22.61 2.67 -45.95 2.67 2 GCS9 UGCS J034622.31+210519.9 +03 57 52.350 +21 02 15.80 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 2 GCS9 Cl* Melotte 22 DH 859 +03 40 35.330 +20 57 56.60 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 2 GCS9 Cl* Melotte 22 DH 146 +03 32 35.580 +27 03 36.70 0 17.100 16.282 15.495 14.865 14.489 14.499 23.23 5.01 -52.46 5.01 2 GCS9 UGCS J033235.57+270336.6 +03 43 28.050 +26 29 55.50 0 17.173 16.372 15.618 15.060 14.682 14.649 17.49 3.01 -38.50 3.01 2 GCS9 UGCS J034328.04+262955.5 +03 36 53.300 +26 34 27.90 1 17.046 16.171 15.379 14.804 14.355 14.353 15.54 3.33 -34.04 3.33 2 GCS9 UGCS J033653.30+263427.9 +03 46 22.250 +23 52 26.60 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 2 GCS9 Cl* Melotte 22 SHF 31 +04 00 11.720 +23 23 08.90 0 17.275 16.419 15.671 15.086 14.642 14.623 21.83 3.45 -37.29 3.45 2 GCS9 UGCS J040011.71+232308.9 +03 46 05.110 +23 45 34.90 1 17.229 16.347 15.522 14.851 14.385 14.404 15.88 2.25 -39.21 2.25 2 GCS9 Cl* Melotte 22 SHF 25 +03 58 00.620 +21 18 20.80 1 17.399 16.380 15.545 14.931 14.445 14.423 24.81 3.43 -32.13 3.43 2 GCS9 UGCS J035800.61+211820.8 +04 05 23.120 +22 29 03.30 0 17.120 16.364 15.605 15.015 14.577 14.567 24.92 5.10 -41.55 5.10 2 GCS9 UGCS J040523.12+222903.3 +03 47 20.480 +19 54 25.50 1 17.011 16.054 15.210 14.615 14.152 14.140 28.11 5.10 -39.39 5.10 2 GCS9 UGCS J034720.47+195425.5 +04 02 43.660 +22 43 42.50 0 17.125 16.400 15.711 15.021 14.640 14.615 22.00 3.44 -31.92 3.44 2 GCS9 UGCS J040243.65+224342.5 +03 40 45.160 +27 50 40.40 1 17.074 16.218 15.404 14.816 14.321 14.333 12.37 3.34 -40.37 3.34 2 GCS9 UGCS J034045.16+275040.4 +03 42 47.300 +23 00 40.30 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 2 GCS9 UGCS J034247.29+230040.3 +03 45 19.730 +19 15 47.40 0 17.061 16.401 15.697 15.151 14.745 14.731 8.46 4.11 -32.05 4.11 2 GCS9 UGCS J034519.72+191547.4 +03 47 39.020 +24 36 22.20 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 2 GCS9 2MASS J03473900+2436226 +03 51 05.970 +24 36 16.90 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 2 GCS9 2MASS J03510597+2436171 +03 34 38.610 +24 51 28.50 1 17.097 16.247 15.459 14.920 14.445 14.466 13.39 2.69 -37.21 2.69 2 GCS9 UGCS J033438.61+245128.4 +04 09 17.800 +26 03 31.20 1 17.120 16.480 15.759 15.003 14.603 6.56 4.49 -33.73 4.49 2 GCS9 UGCS J040917.79+260331.2 +03 43 33.120 +27 55 49.60 0 17.880 16.894 16.092 15.463 15.044 15.026 17.70 4.09 -38.59 4.09 2 GCS9 UGCS J034333.12+275549.5 +04 00 18.830 +27 09 46.80 0 17.560 16.743 16.002 15.448 15.005 15.036 10.06 3.09 -34.16 3.09 2 GCS9 UGCS J040018.82+270946.7 +03 33 30.460 +27 32 55.40 0 17.238 16.553 15.809 15.260 14.841 14.846 18.69 3.90 -36.97 3.90 2 GCS9 UGCS J033330.46+273255.4 +04 04 24.850 +28 39 03.60 0 17.526 16.894 16.132 15.388 14.992 15.035 18.79 3.11 -35.18 3.11 2 GCS9 UGCS J040424.84+283903.5 +03 34 50.820 +25 34 46.40 0 17.916 16.901 16.095 15.494 15.066 21.10 5.70 -37.46 5.70 2 GCS9 UGCS J033450.81+253446.3 +04 03 01.220 +25 24 23.00 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 2 GCS9 Cl* Melotte 22 DH 905 +03 52 15.630 +25 31 09.10 0 17.722 16.782 15.959 15.395 14.936 14.897 13.63 3.10 -42.16 3.10 2 GCS9 UGCS J035215.63+253109.1 +03 53 41.460 +26 42 22.30 0 17.559 16.958 16.192 15.590 15.213 15.193 20.55 3.12 -38.29 3.12 2 GCS9 UGCS J035341.45+264222.3 +03 48 38.370 +22 33 51.80 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 2 GCS9 UGCS J034838.37+223351.7 +03 28 20.710 +25 15 40.00 0 17.594 16.841 16.097 15.503 15.097 11.14 7.37 -20.79 7.37 2 GCS9 UGCS J032820.71+251540.0 +03 58 22.560 +22 28 36.30 0 17.356 16.631 15.918 15.234 14.804 9.62 6.01 -58.05 6.01 2 GCS9 UGCS J035822.55+222836.3 +03 38 43.690 +24 11 24.50 0 17.599 16.762 15.913 15.343 14.861 14.851 24.38 2.57 -36.40 2.57 2 GCS9 UGCS J033843.68+241124.5 +03 40 35.500 +23 13 07.40 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 2 GCS9 UGCS J034035.49+231307.3 +03 56 21.530 +23 08 41.50 0 17.934 16.982 16.151 15.536 15.055 15.042 18.32 3.20 -36.21 3.20 2 GCS9 UGCS J035621.52+230841.4 +03 56 40.930 +22 59 40.60 0 17.830 16.872 16.002 15.424 14.921 14.950 13.47 3.14 -35.44 3.14 2 GCS9 UGCS J035640.92+225940.5 +03 51 25.970 +20 36 12.10 0 17.108 16.526 15.822 15.270 14.881 14.835 15.46 3.52 -35.21 3.52 2 GCS9 UGCS J035125.96+203612.0 +03 37 27.990 +23 02 19.60 0 17.750 16.811 15.973 15.390 14.893 14.904 19.88 3.09 -49.46 3.09 2 GCS9 UGCS J033727.98+230219.5 +04 07 12.860 +27 26 05.40 0 17.420 16.620 15.882 15.297 14.866 14.886 11.86 3.06 -46.68 3.06 2 GCS9 UGCS J040712.86+272605.4 +03 31 17.910 +27 27 46.10 0 17.576 16.828 16.102 15.467 15.101 2.80 7.41 -33.43 7.41 2 GCS9 UGCS J033117.91+272746.1 +03 51 35.200 +19 26 25.80 0 17.395 16.730 16.037 15.430 15.101 15.089 13.46 4.25 -36.40 4.25 2 GCS9 UGCS J035135.19+192625.8 +03 48 19.020 +24 25 12.70 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 2 GCS9 2MASS J03481900+2425130 +03 40 53.660 +28 21 11.50 1 18.880 17.570 16.490 15.852 15.194 15.199 13.91 4.06 -31.57 4.06 2 GCS9 UGCS J034053.65+282111.5 +03 34 04.410 +25 31 33.60 0 18.393 17.293 16.390 15.781 15.254 31.63 5.79 -38.18 5.79 2 GCS9 UGCS J033404.41+253133.5 +03 33 49.220 +19 59 52.00 1 18.539 18.104 16.453 15.767 15.433 15.492 1.67 6.44 -40.37 6.44 2 GCS9 UGCS J033349.21+195952.0 +03 57 30.820 +23 41 22.60 0 18.532 17.359 16.440 15.839 15.265 15.297 20.84 3.11 -46.90 3.11 2 GCS9 UGCS J035730.82+234122.5 +04 07 10.040 +24 12 32.40 0 18.235 17.375 16.480 15.868 15.404 15.382 20.30 3.50 -47.43 3.50 2 GCS9 UGCS J040710.04+241232.3 +03 39 14.770 +23 23 31.70 0 18.455 17.428 16.499 15.853 15.312 15.326 17.63 3.27 -35.01 3.27 2 GCS9 UGCS J033914.77+232331.7 +03 50 37.960 +23 03 05.00 0 18.420 17.267 16.354 15.734 15.191 15.195 15.37 2.36 -44.53 2.36 2 GCS9 UGCS J035037.95+230305.0 +03 36 03.850 +22 52 03.50 1 18.072 16.873 15.913 15.240 14.679 14.679 22.46 3.15 -46.19 3.15 2 GCS9 UGCS J033603.84+225203.4 +03 43 40.310 +24 30 11.20 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2 GCS9 2MASS J03434028+2430113 +03 52 18.640 +24 04 28.10 0 18.327 17.280 16.395 15.738 15.202 15.236 15.37 2.40 -46.07 2.40 2 GCS9 2MASS J03521864+2404283 +03 45 45.360 +27 59 27.60 0 19.568 18.074 17.104 16.454 15.870 15.859 22.97 3.88 -34.58 3.88 2 GCS9 UGCS J034545.35+275927.5 +03 53 00.850 +28 39 04.10 0 20.062 19.110 17.754 17.030 16.313 16.293 15.53 6.33 -39.81 6.33 2 GCS9 UGCS J035300.85+283904.1 +03 45 24.330 +29 23 10.90 0 19.979 18.833 17.716 17.085 16.422 16.407 4.16 7.63 -22.76 7.63 2 GCS9 UGCS J034524.32+292310.8 +03 49 16.680 +27 40 21.80 0 18.545 17.513 16.598 15.957 15.425 15.434 17.98 3.18 -33.47 3.18 2 GCS9 UGCS J034916.67+274021.8 +03 54 06.750 +25 37 45.30 0 18.855 17.669 16.724 16.098 15.522 15.498 15.81 3.44 -37.94 3.44 2 GCS9 UGCS J035406.75+253745.3 +03 50 08.270 +25 30 51.70 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 2 GCS9 UGCS J035008.27+253051.6 +03 47 04.410 +24 47 27.30 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2 GCS9 UGCS J034704.41+244727.3 +03 47 46.780 +25 35 16.60 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 2 GCS9 UGCS J034746.77+253516.5 +03 51 47.660 +24 39 58.90 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 2 GCS9 UGCS J035147.65+243958.9 +03 56 16.380 +23 54 51.30 0 18.685 17.753 16.776 16.182 15.634 15.596 9.87 3.22 -39.24 3.22 2 GCS9 UGCS J035616.37+235451.3 +03 58 05.150 +22 17 27.90 0 20.161 18.583 17.333 16.594 15.971 15.972 23.29 3.91 -48.55 3.91 2 GCS9 UGCS J035805.15+221727.8 +03 48 27.360 +23 46 16.20 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 2 GCS9 UGCS J034827.36+234616.2 +03 40 06.820 +25 15 49.20 0 18.988 17.792 16.838 16.183 15.664 15.610 20.86 2.86 -34.63 2.86 2 GCS9 UGCS J034006.81+251549.2 +03 55 27.630 +25 49 40.60 0 18.871 17.904 16.953 16.336 15.855 15.810 21.98 3.53 -52.06 3.53 2 GCS9 UGCS J035527.62+254940.6 +03 56 44.750 +25 30 10.70 0 18.802 17.819 16.859 16.230 15.664 15.687 25.10 3.01 -34.70 3.01 2 GCS9 UGCS J035644.74+253010.6 +03 45 04.410 +24 15 16.60 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 2 GCS9 UGCS J034504.41+241516.6 +03 33 00.000 +24 18 24.70 0 20.076 18.520 17.447 16.641 16.125 16.084 18.97 3.41 -44.11 3.41 2 GCS9 UGCS J033259.99+241824.6 +03 56 00.910 +22 29 08.70 0 18.397 17.477 16.589 15.928 15.448 15.432 21.10 2.75 -37.24 2.75 2 GCS9 UGCS J035600.91+222908.7 +03 36 18.940 +23 33 17.70 0 20.060 18.441 17.328 16.631 16.003 16.073 17.36 3.52 -38.71 3.52 2 GCS9 UGCS J033618.93+233317.7 +03 47 48.910 +24 17 06.50 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 2 GCS9 UGCS J034748.90+241706.5 +03 43 50.160 +24 12 29.70 0 20.264 18.895 17.579 16.861 16.149 20.17 3.10 -36.90 3.10 2 GCS9 UGCS J034350.15+241229.7 +03 44 05.250 +22 50 13.50 0 20.066 18.839 17.666 16.895 16.134 16.158 19.70 3.13 -45.22 3.13 2 GCS9 Cl* Melotte 22 IPL 57 +03 48 11.270 +23 17 25.20 0 19.477 18.335 17.220 16.665 16.007 16.019 24.70 3.10 -48.41 3.10 2 GCS9 UGCS J034811.27+231725.2 +03 27 04.710 +24 40 13.70 0 20.770 19.508 18.154 17.395 16.720 -20.28 14.33 -21.25 14.33 2 GCS9 UGCS J032704.71+244013.7 +03 44 18.350 +23 15 22.30 0 18.547 17.519 16.552 15.957 15.400 15.438 14.83 2.46 -47.82 2.46 2 GCS9 UGCS J034418.34+231522.3 +03 54 14.070 +23 17 51.90 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 2 GCS9 UGCS J035414.06+231751.9 +03 46 55.490 +23 11 16.00 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 2 GCS9 UGCS J034655.48+231116.0 +03 48 30.740 +22 44 50.20 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 2 GCS9 UGCS J034830.74+224450.2 +03 48 31.530 +24 34 37.20 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 2 GCS9 UGCS J034831.52+243437.2 +03 51 05.220 +23 15 37.70 0 20.207 19.022 17.842 17.041 16.374 16.313 17.05 3.13 -40.03 3.13 2 GCS9 UGCS J035105.21+231537.7 +03 52 02.100 +23 15 45.40 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 2 GCS9 2MASS J03520209+2315451 +03 43 07.150 +20 41 57.00 0 19.556 18.299 17.188 16.411 15.877 15.884 23.63 4.17 -37.64 4.17 2 GCS9 UGCS J034307.15+204156.9 +03 33 30.240 +21 20 50.50 0 19.630 18.597 17.546 16.802 16.378 16.403 3.11 5.57 -44.71 5.57 2 GCS9 UGCS J033330.23+212050.5 +03 54 12.540 +19 15 05.80 0 20.321 18.802 17.562 16.719 16.048 16.072 7.03 6.90 -37.98 6.90 2 GCS9 UGCS J035412.54+191505.7 +03 39 18.810 +24 27 37.80 0 20.092 18.529 17.384 16.652 15.944 16.010 21.70 3.21 -42.51 3.21 2 GCS9 UGCS J033918.80+242737.8 +03 58 31.810 +26 47 38.70 0 20.342 18.971 17.810 17.035 16.342 16.469 14.01 3.98 -35.22 3.98 2 GCS9 UGCS J035831.80+264738.7 +03 38 05.890 +26 15 03.70 0 20.001 18.573 17.436 16.664 16.051 16.064 19.43 3.91 -39.39 3.91 2 GCS9 UGCS J033805.88+261503.7 +03 48 40.630 +26 17 09.70 0 20.203 18.726 17.655 17.168 16.407 16.359 19.80 4.09 -32.05 4.09 2 GCS9 UGCS J034840.63+261709.7 +03 32 58.900 +26 08 38.40 0 20.923 19.834 18.160 17.335 16.613 2.05 10.05 -30.53 10.05 2 GCS9 UGCS J033258.89+260838.3 +03 46 32.130 +24 23 14.60 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 2 GCS9 Cl* Melotte 22 SHF 34 +03 46 20.270 +23 58 18.90 1 19.259 18.174 17.034 16.269 15.650 15.585 12.97 2.40 -35.00 2.40 2 GCS9 UGCS J034620.27+235818.8 +03 44 18.230 +24 05 47.20 0 20.411 19.235 17.772 16.984 16.316 20.09 3.23 -35.92 3.23 2 GCS9 UGCS J034418.23+240547.1 +03 34 34.800 +27 40 36.50 0 12.822 12.654 12.246 11.697 11.560 11.604 19.82 3.74 -37.63 3.74 0.83 1 GCS9 UGCS J033434.79+274036.5 +03 52 14.060 +28 24 40.90 0 12.345 12.198 11.835 11.446 11.311 11.332 15.22 2.93 -38.30 2.93 0.73 1 GCS9 UGCS J035214.06+282440.8 +03 58 18.450 +25 33 56.00 0 12.415 12.278 11.950 11.653 11.462 11.478 14.63 2.47 -38.40 2.47 0.69 1 GCS9 UGCS J035818.45+253356.0 +03 26 13.750 +25 27 46.80 0 12.564 12.357 11.926 11.507 11.265 20.99 6.87 -37.32 6.87 0.79 1 GCS9 UGCS J032613.74+252746.7 +03 33 38.020 +25 20 16.90 0 12.908 12.498 11.930 11.512 11.225 24.95 5.30 -41.14 5.30 0.65 1 GCS9 UGCS J033338.02+252016.8 +03 44 30.080 +25 35 46.80 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 0.81 1 GCS9 V* V513 Tau +03 43 05.540 +24 49 28.30 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 0.86 1 GCS9 V* LS Tau +03 43 52.150 +24 50 29.50 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 0.60 1 GCS9 V* MR Tau +03 49 02.350 +25 43 24.10 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 600 +03 42 02.850 +22 22 42.90 0 12.613 12.430 12.021 11.494 11.211 18.81 7.95 -45.14 7.95 0.79 1 GCS9 Cl* Melotte 22 SK 711 +03 52 42.510 +25 07 02.70 0 12.809 12.594 12.168 11.644 11.471 11.491 22.89 2.23 -36.88 2.23 0.67 1 GCS9 Cl* Melotte 22 SRS 33701 +03 52 10.580 +29 01 12.10 0 12.489 12.328 11.917 11.584 11.201 11.269 21.59 3.07 -35.82 3.07 0.64 1 GCS9 UGCS J035210.58+290112.1 +03 41 57.740 +23 40 05.80 0 12.352 12.196 11.846 11.578 11.384 11.414 14.80 2.12 -38.46 2.12 0.71 1 GCS9 Cl* Melotte 22 SRS 83446 +03 55 32.840 +23 19 08.00 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 0.61 1 GCS9 Cl* Melotte 22 DH 816 +03 48 10.170 +23 00 03.90 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 554 +03 56 08.590 +21 45 47.70 0 12.574 11.870 11.320 11.082 11.146 18.71 2.62 -38.21 2.62 0.85 1 GCS9 V* V581 Tau +03 53 45.760 +21 48 52.50 0 12.830 12.662 12.296 11.818 11.689 11.729 19.80 2.50 -39.40 2.50 0.88 1 GCS9 UGCS J035345.75+214852.5 +03 54 34.800 +21 53 01.70 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 0.87 1 GCS9 V* V577 Tau +03 43 42.140 +24 34 23.10 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 0.87 1 GCS9 V* MO Tau +04 00 59.070 +24 08 32.20 0 12.785 12.566 12.116 11.564 11.384 13.85 3.54 -39.58 3.54 0.66 1 GCS9 UGCS J040059.07+240832.2 +03 46 12.880 +24 03 15.60 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 0.81 1 GCS9 V* V1189 Tau +03 45 44.080 +24 04 26.60 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 0.78 1 GCS9 V* V787 Tau +03 42 10.930 +24 05 08.40 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 0.85 1 GCS9 V* LM Tau +04 00 21.250 +28 16 49.70 0 13.141 12.934 12.512 12.011 11.880 11.905 21.55 3.86 -37.30 3.86 0.64 1 GCS9 UGCS J040021.24+281649.7 +03 51 47.460 +28 26 24.60 0 13.148 12.951 12.542 11.990 11.840 11.865 13.41 2.93 -47.11 2.93 0.68 1 GCS9 UGCS J035147.45+282624.6 +04 00 02.390 +25 39 34.00 0 13.797 13.476 12.981 12.320 12.070 12.076 20.75 3.31 -46.98 3.31 0.85 1 GCS9 UGCS J040002.38+253933.9 +03 37 18.450 +25 03 44.90 0 13.853 13.520 13.052 12.497 12.198 12.229 21.92 2.48 -45.60 2.48 0.86 1 GCS9 UGCS J033718.45+250344.8 +03 36 47.500 +26 38 27.60 0 13.112 12.943 12.541 12.080 11.931 11.931 18.09 3.30 -37.78 3.30 0.79 1 GCS9 UGCS J033647.50+263827.5 +04 00 49.330 +25 12 10.60 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.89 1 GCS9 Cl* Melotte 22 DH 895 +03 54 48.000 +25 12 30.20 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.12 2.23 0.87 1 GCS9 Cl* Melotte 22 BPL 318 +03 35 44.280 +22 27 09.70 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.90 1 GCS9 Cl* Melotte 22 DH 47 +03 33 40.840 +20 09 48.00 0 13.488 13.221 12.781 12.282 12.017 12.032 21.02 6.09 -45.62 6.09 0.89 1 GCS9 UGCS J033340.83+200948.0 +03 36 29.050 +20 11 19.60 0 13.927 13.706 13.216 12.603 12.461 12.464 16.96 3.85 -40.17 3.85 0.90 1 GCS9 UGCS J033629.05+201119.6 +03 59 25.170 +24 14 56.40 0 13.930 13.724 13.271 12.606 12.467 12.483 16.99 2.96 -44.63 2.96 0.93 1 GCS9 UGCS J035925.17+241456.3 +03 37 17.950 +22 28 18.00 0 13.830 13.597 13.115 12.467 12.257 12.268 20.09 2.94 -37.15 2.94 0.70 1 GCS9 UGCS J033717.94+222817.9 +03 51 54.520 +23 33 31.30 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 0.70 1 GCS9 V* V389 Tau +03 51 03.830 +22 50 37.90 0 13.298 13.029 12.569 11.989 11.797 11.789 15.03 2.13 -40.74 2.13 0.87 1 GCS9 UGCS J035103.83+225037.9 +03 43 24.400 +23 13 30.20 0 13.528 12.672 12.085 11.771 11.765 20.41 2.30 -41.63 2.30 0.92 1 GCS9 UGCS J034324.40+231330.1 +03 32 15.300 +20 47 41.70 0 13.718 13.478 13.020 12.433 12.215 12.228 17.60 6.09 -40.17 6.09 0.91 1 GCS9 UGCS J033215.29+204741.6 +04 03 24.940 +22 52 17.00 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.92 1 GCS9 Cl* Melotte 22 DH 907 +03 56 10.400 +23 02 23.70 0 13.356 12.519 11.944 11.625 11.650 15.48 3.06 -38.16 3.06 0.74 1 GCS9 UGCS J035610.39+230223.7 +03 52 25.930 +21 50 31.50 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 0.83 1 GCS9 Cl* Melotte 22 DH 752 +03 55 35.350 +27 46 29.00 0 13.589 13.393 12.952 12.392 12.253 12.273 20.70 3.30 -40.12 3.30 0.89 1 GCS9 UGCS J035535.35+274628.9 +03 35 38.230 +21 51 25.40 0 13.279 13.079 12.659 12.077 11.911 11.936 19.33 2.93 -40.04 2.93 0.90 1 GCS9 UGCS J033538.23+215125.4 +03 47 21.730 +19 18 28.70 0 13.854 13.603 13.121 12.524 12.312 12.319 17.93 5.04 -48.08 5.04 0.82 1 GCS9 UGCS J034721.73+191828.6 +03 51 51.900 +21 49 21.70 0 13.872 13.425 12.851 12.281 12.493 12.027 24.20 2.51 -40.53 2.51 0.69 1 GCS9 UGCS J035151.89+214921.6 +03 54 00.710 +23 58 59.80 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.02 2.24 0.73 1 GCS9 Cl* Melotte 22 BPL 298 +03 51 31.610 +29 15 13.10 0 14.394 14.080 13.598 12.909 12.733 12.731 19.57 3.07 -37.15 3.07 0.76 1 GCS9 UGCS J035131.60+291513.1 +03 29 19.780 +27 12 30.00 0 14.890 14.572 14.060 13.447 13.169 16.72 6.94 -44.58 6.94 0.93 1 GCS9 UGCS J032919.78+271229.9 +04 00 28.350 +27 20 54.50 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.81 1 GCS9 Cl* Melotte 22 DH 893 +03 43 47.710 +28 15 59.30 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.90 1 GCS9 Cl* Melotte 22 DH 289 +03 53 49.640 +27 30 40.10 0 14.731 14.415 13.869 13.241 12.976 12.976 24.57 3.30 -41.14 3.30 0.74 1 GCS9 UGCS J035349.63+273040.0 +03 59 05.730 +26 24 26.60 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.93 1 GCS9 Cl* Melotte 22 DH 871 +03 39 43.060 +28 31 56.40 0 14.162 13.895 13.403 12.759 12.563 12.544 20.48 4.93 -36.37 4.93 0.64 1 GCS9 UGCS J033943.05+283156.4 +03 58 28.350 +26 12 55.90 0 14.361 14.017 13.521 12.968 12.697 12.687 20.39 2.48 -41.41 2.48 0.93 1 GCS9 UGCS J035828.34+261255.8 +03 49 56.770 +25 22 22.60 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 0.93 1 GCS9 Cl* Melotte 22 DH 643 +03 30 22.480 +26 24 32.00 0 14.792 14.449 13.980 13.319 13.102 18.94 6.94 -40.18 6.94 0.92 1 GCS9 UGCS J033022.47+262432.0 +03 47 45.920 +24 38 01.30 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 1 GCS9 V* QZ Tau +03 54 03.200 +25 54 51.90 0 14.437 14.189 13.723 13.123 12.856 12.865 12.69 2.94 -39.94 2.94 0.66 1 GCS9 UGCS J035403.20+255451.9 +03 52 06.670 +22 01 14.30 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 0.62 1 GCS9 Cl* Melotte 22 DH 741 +03 45 02.710 +21 18 39.70 0 14.934 14.640 14.136 13.464 13.315 13.317 22.56 2.65 -46.52 2.65 0.82 1 GCS9 UGCS J034502.71+211839.6 +03 51 33.720 +29 00 34.70 0 14.788 14.525 14.036 13.372 13.189 13.178 12.05 3.07 -41.43 3.07 0.65 1 GCS9 UGCS J035133.72+290034.7 +03 42 19.290 +23 31 54.20 0 14.725 14.361 13.832 13.208 12.974 12.935 25.07 2.13 -45.62 2.13 0.64 1 GCS9 UGCS J034219.28+233154.1 +04 02 22.620 +24 48 24.00 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.89 1 GCS9 Cl* Melotte 22 DH 903 +04 02 26.020 +20 01 20.20 0 14.492 14.212 13.752 13.090 12.861 12.872 11.66 3.13 -43.18 3.13 0.62 1 GCS9 UGCS J040226.01+200120.2 +04 00 14.110 +24 48 51.40 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.77 1 GCS9 Cl* Melotte 22 DH 889 +03 57 21.710 +19 08 03.30 0 14.153 13.952 13.488 12.848 12.624 12.659 13.07 3.04 -47.13 3.04 0.63 1 GCS9 UGCS J035721.71+190803.2 +03 42 27.850 +20 36 34.50 0 14.470 14.181 13.664 13.020 12.780 12.791 20.91 3.42 -38.69 3.42 0.85 1 GCS9 UGCS J034227.84+203634.5 +03 44 44.440 +22 55 51.50 0 14.304 13.961 13.438 12.776 12.510 12.514 17.50 2.24 -39.82 2.24 0.91 1 GCS9 UGCS J034444.43+225551.4 +03 38 58.120 +21 39 37.00 0 14.879 14.515 13.971 13.347 13.097 13.072 12.89 3.41 -45.84 3.41 0.70 1 GCS9 UGCS J033858.11+213936.9 +03 39 53.530 +25 46 46.20 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.92 1 GCS9 Cl* Melotte 22 DH 119 +03 37 22.250 +24 54 16.00 0 14.602 14.248 13.722 13.181 12.852 12.843 21.94 2.49 -40.27 2.49 0.88 1 GCS9 UGCS J033722.24+245415.9 +04 04 06.690 +24 06 38.90 0 14.458 14.219 13.754 13.172 13.023 13.019 18.45 3.38 -41.12 3.38 0.93 1 GCS9 UGCS J040406.69+240638.9 +03 47 59.670 +29 33 38.30 0 15.313 14.132 13.593 13.251 13.270 19.41 5.91 -43.49 5.91 0.89 1 GCS9 UGCS J034759.67+293338.3 +03 37 39.560 +27 58 59.00 0 15.396 15.042 14.537 13.913 13.660 13.707 17.33 4.96 -37.63 4.96 0.72 1 GCS9 UGCS J033739.56+275858.9 +04 01 54.700 +28 42 17.70 0 15.596 15.171 14.595 13.980 13.674 13.692 12.35 2.97 -40.08 2.97 0.60 1 GCS9 UGCS J040154.70+284217.7 +03 57 41.460 +28 16 35.80 0 15.528 15.146 14.578 13.949 13.678 13.674 15.59 3.87 -48.50 3.87 0.62 1 GCS9 UGCS J035741.46+281635.8 +03 51 29.350 +28 30 49.30 0 15.859 15.469 14.923 14.340 14.053 14.035 20.59 2.97 -37.30 2.97 0.63 1 GCS9 UGCS J035129.34+283049.2 +03 50 32.900 +26 42 57.20 0 15.534 15.123 14.574 13.937 13.670 13.669 21.11 2.97 -41.24 2.97 0.84 1 GCS9 UGCS J035032.90+264257.2 +04 05 57.780 +26 14 28.00 0 15.023 14.686 14.199 13.587 13.362 13.356 14.79 3.39 -37.90 3.39 0.65 1 GCS9 UGCS J040557.77+261428.0 +03 49 12.180 +25 20 31.70 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 0.89 1 GCS9 UGCS J034912.17+252031.7 +03 47 10.210 +24 43 35.30 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 0.90 1 GCS9 UGCS J034710.20+244335.3 +03 42 52.360 +26 29 09.40 0 15.538 15.176 14.662 14.108 13.827 13.821 18.20 2.95 -44.40 2.95 0.89 1 GCS9 UGCS J034252.35+262909.3 +03 45 36.120 +25 16 30.30 0 15.683 15.221 14.656 14.111 13.784 13.795 15.41 2.22 -40.16 2.22 0.83 1 GCS9 UGCS J034536.12+251630.2 +03 46 13.000 +27 02 11.70 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 0.82 1 GCS9 Cl* Melotte 22 DH 430 +04 01 12.640 +23 50 20.20 0 15.772 15.257 14.698 14.117 13.795 17.28 3.56 -46.77 3.56 0.81 1 GCS9 UGCS J040112.64+235020.2 +03 38 10.270 +25 11 32.90 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 0.88 1 GCS9 Cl* Melotte 22 HHJ 35 +03 56 11.670 +22 05 52.60 0 15.610 15.181 14.631 14.105 13.807 13.799 13.87 2.52 -42.35 2.52 0.81 1 GCS9 UGCS J035611.66+220552.5 +04 10 08.800 +25 45 52.30 0 15.752 14.719 14.169 13.826 13.848 15.83 3.05 -42.20 3.05 0.88 1 GCS9 UGCS J041008.80+254552.3 +04 04 11.640 +22 15 20.70 0 15.315 14.979 14.419 13.706 13.470 13.485 22.99 3.41 -38.88 3.41 0.60 1 GCS9 UGCS J040411.64+221520.6 +03 33 39.010 +22 21 08.50 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.87 1 GCS9 Cl* Melotte 22 DH 29 +03 57 55.850 +21 16 10.80 0 15.354 14.971 14.436 13.805 13.526 13.536 16.86 3.37 -36.80 3.37 0.62 1 GCS9 Cl* Melotte 22 DH 860 +03 56 55.470 +22 08 24.30 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.80 1 GCS9 Cl* Melotte 22 DH 847 +03 45 15.270 +28 49 08.30 0 15.861 15.413 14.849 14.234 13.940 13.925 16.55 5.87 -46.40 5.87 0.82 1 GCS9 UGCS J034515.27+284908.2 +03 54 16.800 +22 00 16.60 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 0.67 1 GCS9 Cl* Melotte 22 DH 799 +03 38 06.920 +24 14 55.50 0 15.747 15.267 14.652 14.133 13.761 13.766 22.57 2.51 -42.72 2.51 0.78 1 GCS9 UGCS J033806.92+241455.5 +03 44 47.310 +19 55 41.40 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.85 1 GCS9 Cl* Melotte 22 DH 354 +03 39 05.600 +24 12 41.20 0 15.799 15.460 14.905 14.352 14.029 14.047 14.71 2.51 -42.46 2.51 0.85 1 GCS9 UGCS J033905.60+241241.2 +03 58 23.360 +24 41 57.20 0 15.291 14.922 14.431 13.755 13.554 13.562 17.18 2.49 -38.00 2.49 0.76 1 GCS9 UGCS J035823.36+244157.2 +03 57 06.210 +23 13 00.70 0 15.484 14.319 13.773 13.434 13.441 15.53 3.07 -41.00 3.07 0.86 1 GCS9 UGCS J035706.21+231300.7 +04 08 59.010 +24 26 26.40 0 15.392 15.053 14.539 13.891 13.594 13.605 17.10 2.99 -46.14 2.99 0.84 1 GCS9 UGCS J040859.01+242626.3 +03 48 48.620 +24 30 15.40 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 0.70 1 GCS9 UGCS J034848.61+243015.3 +03 34 41.090 +22 42 27.00 0 15.091 14.772 14.252 13.508 13.294 13.320 12.65 3.05 -42.76 3.05 0.72 1 GCS9 UGCS J033441.08+224226.9 +03 44 05.630 +23 03 42.40 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 0.90 1 GCS9 UGCS J034405.62+230342.4 +03 29 48.420 +22 57 51.60 0 15.744 15.377 14.828 14.156 13.922 13.928 17.79 3.43 -41.86 3.43 0.90 1 GCS9 UGCS J032948.41+225751.6 +03 44 24.000 +21 24 20.80 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 0.89 1 GCS9 Cl* Melotte 22 DH 332 +03 38 25.720 +21 39 49.20 0 15.288 14.944 14.425 13.874 13.590 13.648 16.06 3.42 -43.01 3.42 0.89 1 GCS9 UGCS J033825.71+213949.1 +03 54 06.960 +19 19 14.30 0 15.335 14.930 14.318 13.814 13.497 13.503 18.13 6.06 -42.85 6.06 0.90 1 GCS9 UGCS J035406.96+191914.2 +04 03 16.560 +24 35 19.60 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.83 1 GCS9 Cl* Melotte 22 DH 906 +04 10 46.420 +24 32 13.90 0 15.786 15.329 14.767 14.099 13.762 13.766 15.88 2.99 -46.34 2.99 0.81 1 GCS9 UGCS J041046.41+243213.8 +03 45 36.750 +25 58 55.50 0 15.934 15.514 14.978 14.442 14.152 14.150 14.21 2.25 -38.02 2.25 0.62 1 GCS9 UGCS J034536.74+255855.5 +03 57 23.790 +24 08 50.00 0 15.309 14.982 14.445 13.865 13.611 13.611 13.28 2.97 -45.28 2.97 0.72 1 GCS9 UGCS J035723.79+240850.0 +03 27 27.940 +24 04 58.90 0 15.259 14.831 14.285 13.791 13.497 13.490 19.47 3.85 -48.39 3.85 0.66 1 GCS9 UGCS J032727.94+240458.8 +03 56 28.160 +28 03 48.20 0 16.015 15.511 14.948 14.304 14.050 14.022 16.65 3.90 -45.48 3.90 0.68 1 GCS9 UGCS J035628.16+280348.2 +03 39 03.310 +25 48 18.80 0 16.452 16.052 15.461 14.883 14.588 14.571 16.29 3.03 -46.33 3.03 0.62 1 GCS9 UGCS J033903.30+254818.8 +04 00 38.150 +19 49 22.40 0 16.562 16.095 15.532 15.014 14.678 14.708 14.99 3.08 -42.23 3.08 0.68 1 GCS9 UGCS J040038.14+194922.3 +03 45 03.180 +23 06 58.40 0 16.937 15.492 14.946 14.495 14.527 20.81 2.32 -40.36 2.32 0.62 1 GCS9 2MASS J03450316+2306586 +03 52 34.560 +24 25 22.20 0 16.121 15.669 15.096 14.512 14.246 14.236 16.91 2.25 -44.16 2.25 0.73 1 GCS9 UGCS J035234.55+242522.2 +03 30 35.390 +23 03 07.90 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.63 1 GCS9 Cl* Melotte 22 DH 12 +03 40 28.370 +21 33 06.70 0 16.564 16.082 15.471 14.901 14.509 14.509 15.69 3.66 -45.19 3.66 0.66 1 GCS9 UGCS J034028.36+213306.7 +03 42 30.350 +25 34 26.80 0 17.540 16.951 16.269 15.675 15.305 15.293 15.12 2.38 -41.28 2.38 0.62 1 GCS9 UGCS J034230.35+253426.7 +03 51 17.870 +25 29 25.40 0 17.777 18.391 15.245 14.674 14.519 14.519 20.51 2.28 -39.66 2.28 0.65 1 GCS9 UGCS J035117.86+252925.4 +03 53 00.280 +25 40 08.50 0 17.276 16.722 16.169 15.583 15.273 15.241 18.78 3.13 -41.94 3.13 0.73 1 GCS9 UGCS J035300.28+254008.4 +04 06 09.290 +26 15 33.50 0 17.463 16.942 16.310 15.716 15.358 15.337 18.62 3.15 -39.15 3.15 0.64 1 GCS9 UGCS J040609.29+261533.5 +04 03 43.310 +23 56 52.90 0 17.197 16.623 15.998 15.428 15.061 15.045 15.09 3.44 -41.73 3.44 0.63 1 GCS9 UGCS J040343.31+235652.9 +03 36 57.480 +24 19 47.70 0 17.070 16.465 15.833 15.324 14.922 14.937 21.72 2.60 -40.73 2.60 0.64 1 GCS9 UGCS J033657.47+241947.7 +03 44 02.270 +28 51 32.30 0 17.372 16.793 16.123 15.540 15.176 15.169 19.81 6.04 -41.64 6.04 0.72 1 GCS9 UGCS J034402.26+285132.2 +04 03 04.580 +23 33 10.70 0 17.246 16.654 16.054 15.455 15.146 19.09 3.78 -42.76 3.78 0.73 1 GCS9 UGCS J040304.58+233310.6 +03 46 23.720 +22 50 16.40 0 17.310 15.710 15.189 14.722 14.694 20.26 2.35 -45.42 2.35 0.64 1 GCS9 2MASS J03462371+2250167 +04 11 03.840 +23 15 48.90 0 17.766 17.254 16.599 15.986 15.599 15.623 18.36 6.81 -46.34 6.81 0.61 1 GCS9 UGCS J041103.83+231548.9 +03 50 07.500 +19 37 06.20 0 17.466 16.910 16.332 15.761 15.401 15.371 17.29 4.51 -41.20 4.51 0.70 1 GCS9 UGCS J035007.50+193706.2 +03 37 45.480 +21 49 49.20 0 17.574 16.949 16.292 15.711 15.375 15.341 15.76 2.70 -40.52 2.70 0.63 1 GCS9 UGCS J033745.47+214949.2 +03 49 01.600 +24 54 30.20 0 17.757 17.163 16.566 16.016 15.638 15.597 20.82 2.38 -42.11 2.38 0.70 1 GCS9 UGCS J034901.60+245430.2 +03 43 53.770 +21 58 28.20 0 17.955 17.227 16.574 15.985 15.582 15.564 17.05 2.89 -42.45 2.89 0.71 1 GCS9 UGCS J034353.76+215828.1 +03 43 53.960 +21 58 24.40 0 17.255 16.562 15.936 15.332 14.986 14.994 20.06 2.65 -43.75 2.65 0.70 1 GCS9 UGCS J034353.95+215824.3 +03 59 40.860 +27 04 35.50 0 19.361 18.308 17.418 16.792 16.276 16.196 23.02 3.86 -48.45 3.86 0.63 1 GCS9 UGCS J035940.86+270435.4 +04 00 48.820 +28 32 53.60 0 19.819 18.841 18.112 17.507 16.848 16.975 23.80 5.55 -42.37 5.55 0.64 1 GCS9 UGCS J040048.81+283253.6 +04 10 54.540 +26 01 42.40 0 19.642 17.748 17.002 16.417 17.38 5.99 -47.11 5.99 0.66 1 GCS9 UGCS J041054.54+260142.3 +03 46 17.010 +27 55 27.80 0 19.334 18.660 17.847 17.282 17.030 17.052 16.50 7.11 -47.80 7.11 0.63 1 GCS9 UGCS J034617.01+275527.7 +03 49 04.260 +21 30 48.40 0 19.535 18.513 17.649 16.945 16.333 16.414 16.76 4.01 -42.72 4.01 0.65 1 GCS9 UGCS J034904.25+213048.3 +03 40 44.530 +28 27 22.00 0 20.858 19.520 18.607 17.739 17.353 17.491 13.92 12.21 -49.16 12.21 0.62 1 GCS9 UGCS J034044.53+282721.9 +04 03 33.870 +23 19 28.20 0 20.446 19.748 18.418 18.040 17.402 17.405 12.29 11.98 -53.62 11.98 0.60 1 GCS9 UGCS J040333.86+231928.2 +03 43 03.110 +19 55 13.30 0 20.210 19.510 18.492 18.006 17.591 17.575 17.22 11.92 -47.33 11.92 0.60 1 GCS9 UGCS J034303.11+195513.2 +03 25 30.920 +24 51 39.90 0 20.316 19.478 18.600 18.210 18.170 10.72 31.33 -45.76 31.33 0.60 1 GCS9 UGCS J032530.91+245139.8 +04 08 03.700 +25 03 18.60 0 20.585 19.668 18.717 17.693 17.074 7.72 8.93 -49.54 8.93 0.60 1 GCS9 UGCS J040803.69+250318.5 +03 41 51.050 +24 10 06.60 0 20.465 19.418 18.729 18.205 17.578 12.60 6.35 -49.41 6.35 0.62 1 GCS9 UGCS J034151.04+241006.5 +03 32 30.600 +27 09 39.20 0 20.343 18.971 18.172 16.968 17.036 7.52 10.85 -21.23 10.85 3 GCS9 UGCS J033230.59+270939.2 +03 29 01.870 +27 16 23.30 0 20.590 19.126 18.101 17.366 96.07 32.77 -88.72 32.77 3 GCS9 UGCS J032901.87+271623.2 +03 42 59.310 +25 37 38.90 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 3 GCS9 UGCS J034259.30+253738.9 +03 35 28.030 +25 17 23.50 0 19.445 18.046 17.289 16.458 41.24 10.18 -21.88 10.18 3 GCS9 UGCS J033528.02+251723.4 +03 40 03.140 +25 40 05.50 0 19.571 18.217 17.364 16.488 16.514 13.38 5.06 -31.42 5.06 3 GCS9 UGCS J034003.14+254005.5 +03 33 24.060 +25 50 16.00 0 20.543 18.806 17.811 16.808 11.92 13.52 -32.48 13.52 3 GCS9 UGCS J033324.06+255015.9 +03 35 13.620 +25 07 52.90 0 19.550 18.161 17.249 16.645 16.523 24.66 4.39 -42.83 4.39 3 GCS9 UGCS J033513.62+250752.9 +03 48 15.650 +25 50 08.90 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 3 GCS9 UGCS J034815.64+255008.9 +03 47 38.470 +23 56 27.70 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 3 GCS9 UGCS J034738.47+235627.6 +03 28 30.710 +23 54 01.20 0 19.736 18.362 17.562 16.807 16.867 9.65 6.31 -32.88 6.31 3 GCS9 UGCS J032830.71+235401.2 +03 50 15.960 +24 23 28.60 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 3 GCS9 UGCS J035015.96+242328.6 +03 45 33.300 +23 34 34.30 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 3 GCS9 UGCS J034533.30+233434.2 +03 52 27.190 +23 12 08.00 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 3 GCS9 UGCS J035227.18+231208.0 +03 45 14.520 +22 29 28.60 0 19.756 18.383 17.410 16.493 16.629 14.00 4.20 -53.10 4.20 3 GCS9 Cl* Melotte 22 IPL 69 +03 52 39.150 +24 46 29.40 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 3 GCS9 UGCS J035239.15+244629.4 +03 46 29.110 +22 59 47.60 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 3 GCS9 UGCS J034629.11+225947.6 +03 50 08.100 +23 00 16.60 0 20.272 18.537 17.648 16.732 16.829 12.67 6.26 -29.23 6.26 3 GCS9 UGCS J035008.10+230016.5 +03 53 18.940 +19 24 24.20 0 20.069 18.680 17.810 16.861 16.872 27.44 8.91 -38.90 8.91 3 GCS9 UGCS J035318.93+192424.1 +03 50 39.550 +25 02 54.60 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 3 GCS9 UGCS J035039.54+250254.6 +03 51 29.470 +24 00 37.40 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3 GCS9 Cl* Melotte 22 PLIZ 161 +03 45 58.480 +23 41 53.90 0 18.591 17.562 16.741 16.714 19.16 3.22 -40.46 3.22 3 GCS9 Cl* Melotte 22 NPNPL 4 \ No newline at end of file diff --git a/docs/paper_examples/Figure 2.ipynb b/docs/paper_examples/Hosek+20/Figure 2.ipynb similarity index 98% rename from docs/paper_examples/Figure 2.ipynb rename to docs/paper_examples/Hosek+20/Figure 2.ipynb index a6ba9fa9..52b2e00e 100644 --- a/docs/paper_examples/Figure 2.ipynb +++ b/docs/paper_examples/Hosek+20/Figure 2.ipynb @@ -181,7 +181,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -195,9 +195,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.11.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/paper_examples/Figure 3.ipynb b/docs/paper_examples/Hosek+20/Figure 3.ipynb similarity index 100% rename from docs/paper_examples/Figure 3.ipynb rename to docs/paper_examples/Hosek+20/Figure 3.ipynb diff --git a/docs/paper_examples/Figure 4.ipynb b/docs/paper_examples/Hosek+20/Figure 4.ipynb similarity index 100% rename from docs/paper_examples/Figure 4.ipynb rename to docs/paper_examples/Hosek+20/Figure 4.ipynb diff --git a/docs/paper_examples/Figure 5.ipynb b/docs/paper_examples/Hosek+20/Figure 5.ipynb similarity index 100% rename from docs/paper_examples/Figure 5.ipynb rename to docs/paper_examples/Hosek+20/Figure 5.ipynb diff --git a/docs/paper_examples/Figure 6.ipynb b/docs/paper_examples/Hosek+20/Figure 6.ipynb similarity index 100% rename from docs/paper_examples/Figure 6.ipynb rename to docs/paper_examples/Hosek+20/Figure 6.ipynb diff --git a/docs/paper_examples/Figure 7.ipynb b/docs/paper_examples/Hosek+20/Figure 7.ipynb similarity index 100% rename from docs/paper_examples/Figure 7.ipynb rename to docs/paper_examples/Hosek+20/Figure 7.ipynb diff --git a/docs/paper_examples/README b/docs/paper_examples/Hosek+20/README similarity index 100% rename from docs/paper_examples/README rename to docs/paper_examples/Hosek+20/README diff --git a/docs/requirements.txt b/docs/requirements.txt index 285b1e61..7a2f1159 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,7 +1,9 @@ matplotlib -numpy +numpy <= 1.24.4 numpydoc astropy pysynphot scipy +pandas +setuptools<=81.0 docutils<0.18 diff --git a/filt_func/bessell/B.dat b/filt_func/bessell/B.dat new file mode 100644 index 00000000..9d2eb167 --- /dev/null +++ b/filt_func/bessell/B.dat @@ -0,0 +1,21 @@ +3600.0 0.0000000000 +3700.0 0.0300000000 +3800.0 0.1340000000 +3900.0 0.5670000000 +4000.0 0.9200000000 +4100.0 0.9780000000 +4200.0 1.0000000000 +4300.0 0.9780000000 +4400.0 0.9350000000 +4500.0 0.8530000000 +4600.0 0.7400000000 +4700.0 0.6400000000 +4800.0 0.5360000000 +4900.0 0.4240000000 +5000.0 0.3250000000 +5100.0 0.2350000000 +5200.0 0.1500000000 +5300.0 0.0950000000 +5400.0 0.0430000000 +5500.0 0.0090000000 +5600.0 0.0000000000 diff --git a/filt_func/bessell/I.dat b/filt_func/bessell/I.dat new file mode 100644 index 00000000..67ba0255 --- /dev/null +++ b/filt_func/bessell/I.dat @@ -0,0 +1,23 @@ +7000.0 0.0000000000 +7100.0 0.0240000000 +7200.0 0.2320000000 +7300.0 0.5550000000 +7400.0 0.7850000000 +7500.0 0.9100000000 +7600.0 0.9650000000 +7700.0 0.9850000000 +7800.0 0.9900000000 +7900.0 0.9950000000 +8000.0 1.0000000000 +8100.0 1.0000000000 +8200.0 0.9900000000 +8300.0 0.9800000000 +8400.0 0.9500000000 +8500.0 0.9100000000 +8600.0 0.8600000000 +8700.0 0.7500000000 +8800.0 0.5600000000 +8900.0 0.3300000000 +9000.0 0.1500000000 +9100.0 0.0300000000 +9200.0 0.0000000000 diff --git a/filt_func/bessell/R.dat b/filt_func/bessell/R.dat new file mode 100644 index 00000000..17882885 --- /dev/null +++ b/filt_func/bessell/R.dat @@ -0,0 +1,24 @@ +5500.0 0.0000000000 +5600.0 0.2300000000 +5700.0 0.7400000000 +5800.0 0.9100000000 +5900.0 0.9800000000 +6000.0 1.0000000000 +6100.0 0.9800000000 +6200.0 0.9600000000 +6300.0 0.9300000000 +6400.0 0.9000000000 +6500.0 0.8600000000 +6600.0 0.8100000000 +6700.0 0.7800000000 +6800.0 0.7200000000 +6900.0 0.6700000000 +7000.0 0.6100000000 +7100.0 0.5600000000 +7200.0 0.5100000000 +7300.0 0.4600000000 +7400.0 0.4000000000 +7500.0 0.3500000000 +8000.0 0.1400000000 +8500.0 0.0300000000 +9000.0 0.0000000000 diff --git a/filt_func/bessell/README.txt b/filt_func/bessell/README.txt new file mode 100755 index 00000000..b951df6c --- /dev/null +++ b/filt_func/bessell/README.txt @@ -0,0 +1,4 @@ +These are the Johnson-Cousin filters (UBVRI) from Bessell (1990). + +The wavelength is angstrom, the transmission is a fraction. + diff --git a/filt_func/bessell/U.dat b/filt_func/bessell/U.dat new file mode 100644 index 00000000..1fccf4f6 --- /dev/null +++ b/filt_func/bessell/U.dat @@ -0,0 +1,25 @@ +3000.0 0.0000000000 +3050.0 0.0160000000 +3100.0 0.0680000000 +3150.0 0.1670000000 +3200.0 0.2870000000 +3250.0 0.4230000000 +3300.0 0.5600000000 +3350.0 0.6730000000 +3400.0 0.7720000000 +3450.0 0.8410000000 +3500.0 0.9050000000 +3550.0 0.9430000000 +3600.0 0.9810000000 +3650.0 0.9930000000 +3700.0 1.0000000000 +3750.0 0.9890000000 +3800.0 0.9160000000 +3850.0 0.8040000000 +3900.0 0.6250000000 +3950.0 0.4230000000 +4000.0 0.2380000000 +4050.0 0.1140000000 +4100.0 0.0510000000 +4150.0 0.0190000000 +4200.0 0.0000000000 diff --git a/filt_func/bessell/V.dat b/filt_func/bessell/V.dat new file mode 100644 index 00000000..241fc922 --- /dev/null +++ b/filt_func/bessell/V.dat @@ -0,0 +1,24 @@ +4700.0 0.0000000000 +4800.0 0.0300000000 +4900.0 0.1630000000 +5000.0 0.4580000000 +5100.0 0.7800000000 +5200.0 0.9670000000 +5300.0 1.0000000000 +5400.0 0.9730000000 +5500.0 0.8980000000 +5600.0 0.7920000000 +5700.0 0.6840000000 +5800.0 0.5740000000 +5900.0 0.4610000000 +6000.0 0.3590000000 +6100.0 0.2700000000 +6200.0 0.1970000000 +6300.0 0.1350000000 +6400.0 0.0810000000 +6500.0 0.0450000000 +6600.0 0.0250000000 +6700.0 0.0170000000 +6800.0 0.0130000000 +6900.0 0.0090000000 +7000.0 0.0000000000 diff --git a/filt_func/decam/DECam_filters.txt b/filt_func/decam/DECam_filters.txt index f0f38fa8..c3091a2a 100755 --- a/filt_func/decam/DECam_filters.txt +++ b/filt_func/decam/DECam_filters.txt @@ -1 +1,802 @@ -wavelength u g r i z Y atm 300 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00313 301 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00562 302 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00855 303 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.01480 304 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.01870 305 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.02910 306 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.03670 307 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.04870 308 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.05870 309 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.06840 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99.99 99.99 99.99 99.99 99.99 99.99 +1082.00 99.99 99.99 99.99 99.99 99.99 99.99 +1083.00 99.99 99.99 99.99 99.99 99.99 99.99 +1084.00 99.99 99.99 99.99 99.99 99.99 99.99 +1085.00 99.99 99.99 99.99 99.99 99.99 99.99 +1086.00 99.99 99.99 99.99 99.99 99.99 99.99 +1087.00 99.99 99.99 99.99 99.99 99.99 99.99 +1088.00 99.99 99.99 99.99 99.99 99.99 99.99 +1089.00 99.99 99.99 99.99 99.99 99.99 99.99 +1090.00 99.99 99.99 99.99 99.99 99.99 99.99 +1091.00 99.99 99.99 99.99 99.99 99.99 99.99 +1092.00 99.99 99.99 99.99 99.99 99.99 99.99 +1093.00 99.99 99.99 99.99 99.99 99.99 99.99 +1094.00 99.99 99.99 99.99 99.99 99.99 99.99 +1095.00 99.99 99.99 99.99 99.99 99.99 99.99 +1096.00 99.99 99.99 99.99 99.99 99.99 99.99 +1097.00 99.99 99.99 99.99 99.99 99.99 99.99 +1098.00 99.99 99.99 99.99 99.99 99.99 99.99 +1099.00 99.99 99.99 99.99 99.99 99.99 99.99 +1100.00 99.99 99.99 99.99 99.99 99.99 99.99 diff --git a/filt_func/gaia/edr3/ReadMe b/filt_func/gaia/edr3/ReadMe new file mode 100644 index 00000000..bd3e2f27 --- /dev/null +++ b/filt_func/gaia/edr3/ReadMe @@ -0,0 +1,134 @@ +J/A+A/649/A3 Gaia Early Data Release 3 photometric passbands (Riello+, 2021) +================================================================================ +Gaia Early Data Release 3: Photometric content and validation. + Riello M., De Angeli F., Evans D.W., Montegriffo P., Carrasco J.M, + Busso G., Palaversa L., Burgess P., Diener C., Davidson M., Rowell N., + Fabricius C., Jordi C., Bellazzini M., Pancino E., Harrison D.L., + Cacciari C., van Leeuwen F., Hambly N.C., Hodgkin S.T., Osborne P.J., + Altavilla G., Barstow M.A., Brown A.G.A., Castellani M., Cowell S., + De Luise F., Gilmore G., Giuffrida G., Hidalgo S., Holland G., Marinoni S., + Pagani C., Piersimoni A.M., Pulone L., Ragaini S., Rainer M., Richards P.J., + Sanna N., Walton N.A., Weiler M., Yoldas A. + + =2021A&A...649A...3R (SIMBAD/NED BibCode) +================================================================================ +ADC_Keywords: Surveys ; Photometry, G band +Keywords: catalogues - surveys - instrumentation: photometers - + techniques: photometric - Galaxy: general + +Abstract: + Gaia Early Data Release 3 (Gaia EDR3) contains astrometry and + photometry results for about 1.8 billion sources based on + observations collected by the European Space Agency Gaia satellite + during the first 34 months of its operational phase. + + In this paper, we focus on the photometric content, describing the + input data, the algorithms, the processing, and the validation of the + results. Particular attention is given to the quality of the data and + to a number of features that users may need to take into account to + make the best use of the Gaia EDR3 catalogue. + + The processing broadly followed the same procedure as for Gaia DR2, + but with significant improvements in several aspects of the blue and + red photometer (BP and RP) preprocessing and in the photometric + calibration process. In particular, the treatment of the BP and RP + background has been updated to include a better estimation of the + local background, and the detection of crowding effects has been used + to exclude affected data from the calibrations. The photometric + calibration models have also been updated to account for flux loss + over the whole magnitude range. Significant improvements in the + modelling and calibration of the Gaia point and line spread functions + have also helped to reduce a number of instrumental effects that were + still present in DR2. + + Gaia EDR3 contains 1.806 billion sources with G-band photometry and + 1.540 billion sources with GBP and GRP photometry. The median + uncertainty in the G-band photometry, as measured from the standard + deviation of the internally calibrated mean photometry for a given + source, is 0.2mmag at magnitude G=10 to 14, 0.8mmag at G~17, and + 2.6mmag at G~19. The significant magnitude term found in the Gaia DR2 + photometry is no longer visible, and overall there are no trends + larger than 1mmag/mag. Using one passband over the whole colour and + magnitude range leaves no systematics above the 1% level in magnitude + in any of the bands, and a larger systematic is present for a very + small sample of bright and blue sources. A detailed description of the + residual systematic effects is provided. Overall the quality of the + calibrated mean photometry in Gaia EDR3 is superior with respect to + DR2 for all bands. + +Description: + These tabular data describes the photometric system defined by the G, + G_BP_ and G_RP_ Gaia bands for Gaia Early Data Release 3. + + The tables provide the full passband and the corresponding zero point + for each of the photometric bands. The zero points are available both + in the VEGAMAG and AB systems. + + The passband calibration is based on the modelling of a set of + corrections applied to pre-launch knowledge of the instrument, to find + the best match between observed and synthetic photometry for a set of + calibrators. For EDR3 a large set of calibrators covering a wide range + of spectra types was used for the passband calibration. + + For these sources reconstructed SEDs were obtained from externally + calibrated Gaia BP/RP spectra. + +File Summary: +-------------------------------------------------------------------------------- + FileName Lrecl Records Explanations +-------------------------------------------------------------------------------- +ReadMe 80 . This file +passband.dat 103 781 G, G_BP_ anf G_RP_ passbands used to generate + the magnitudes and astrophysical parameters + included in Gaia EDR3 +zeropt.dat 97 2 G, G_BP_ and G_RP_ zero points used to generate + the magnitudes and astrophysical parameters + included in Gaia EDR3 +-------------------------------------------------------------------------------- + +See also: + I/350 : Gaia EDR3 (Gaia Collaboration, 2020) + J/A+A/649/A6 : Gaia Catalogue of Nearby Stars - GCNS (Gaia collaboration, 2021) + J/A+A/649/A7 : MC structure and properties (Gaia Collaboration+, 2021) + +Byte-by-byte Description of file: passband.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 1- 7 F7.2 nm lambda Wavelength + 10- 23 E14.9 mag GPb ?=99.99 G transmissivity curve at the + corresponding wavelength (1) + 26- 39 E14.9 mag e_GPb ?=99.99 Uncertainty on the G transmissivity + curve (1) + 42- 55 E14.9 mag BPPb ?=99.99 BP transmissivity curve at the + corresponding wavelength (1) + 58- 71 E14.9 mag e_BPPb ?=99.99 Uncertainty on the BP transmissivity + curve (1) + 74- 87 E14.9 mag RPPb ?=99.99 RP transmissivity curve at the + corresponding wavelength (1) + 90-103 E14.9 mag e_RPPb ?=99.99 Uncertainty on the RP transmissivity + curve (1) +-------------------------------------------------------------------------------- +Note (1): In correspondence to wavelength values where the passband is not + defined, both transmissivity and uncertainty are set to 99.99. +-------------------------------------------------------------------------------- + +Byte-by-byte Description of file: zeropt.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 2- 14 F13.10 --- GZp G band zero point + 18- 29 F12.10 --- e_GZp G band zero point uncertainty + 32- 44 F13.10 --- BPZp G_BP_ band zero point + 48- 59 F12.10 --- e_BPZp G_BP_ band zero point uncertainty + 62- 74 F13.10 --- RPZp G_RP_ band zero point + 78- 89 F12.10 --- e_RPZp G_RP_ band zero point uncertainty + 91- 97 A7 --- System [VEGAMAG, AB] Photometric system +-------------------------------------------------------------------------------- + +Acknowledgements: + Marco Riello, mriello(at)ast.cam.ac.uk + Francesca De Angeli, fda(at)ast.cam.ac.uk + +================================================================================ +(End) Francesca De Angeli [IoA, UK], Patricia Vannier [CDS] 05-Jan-2021 diff --git a/filt_func/hipparcos/Hp.dat b/filt_func/hipparcos/Hp.dat new file mode 100644 index 00000000..87376f59 --- /dev/null +++ b/filt_func/hipparcos/Hp.dat @@ -0,0 +1,115 @@ +# Hipparcos HP filter from ADPS +# wavelength (Angstrom), transmission +3350.0 0.0000000000 +3400.0 0.0060000000 +3450.0 0.0230000000 +3500.0 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/dev/null +++ b/filt_func/tess/tess.dat @@ -0,0 +1,857 @@ +# TESS Instrument Response Function +# Source: Roland Vanderspek, POC, Kavli/MIT +# Version: 2.0 +# Date: 2020/07/30 +# Feedback: tesshelp@bigbang.gsfc.nasa.gov +# Wavelength (nm), lambda Transmission +300,0 +302,0 +304,0 +306,0 +308,0 +310,0 +312,0 +314,0 +316,0 +318,0 +320,0 +322,0 +324,0 +326,0 +328,0 +330,0 +332,0 +334,0 +336,0 +338,0 +340,0 +342,0 +344,0 +346,0 +348,0 +350,0 +352,0 +354,0 +356,0 +358,0 +360,0 +362,0 +364,0 +366,0 +368,0 +370,0 +372,0 +374,0 +376,0 +378,0 +380,0 +382,0 +384,0 +386,0 +388,0 +390,0 +392,0 +394,0 +396,0 +398,0 +400,0 +402,0 +404,0 +406,0 +408,0 +410,0 +412,0 +414,0 +416,0 +418,0 +420,0 +422,0 +424,0 +426,0 +428,0 +430,0 +432,0 +434,0 +436,0 +438,0 +440,0 +442,0 +444,0 +446,0 +448,0 +450,0 +452,0 +454,0 +456,0 +458,0 +460,0 +462,0 +464,0 +466,0 +468,0 +470,0 +472,0 +474,0 +476,0 +478,0 +480,0 +482,0 +484,0 +486,0 +488,0 +490,0 +492,0 +494,0 +496,0 +498,0 +500,0 +502,0 +504,0 +506,0 +508,0 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+1806,0 +1808,0 +1810,0 +1812,0 +1814,0 +1816,0 +1818,0 +1820,0 +1822,0 +1824,0 +1826,0 +1828,0 +1830,0 +1832,0 +1834,0 +1836,0 +1838,0 +1840,0 +1842,0 +1844,0 +1846,0 +1848,0 +1850,0 +1852,0 +1854,0 +1856,0 +1858,0 +1860,0 +1862,0 +1864,0 +1866,0 +1868,0 +1870,0 +1872,0 +1874,0 +1876,0 +1878,0 +1880,0 +1882,0 +1884,0 +1886,0 +1888,0 +1890,0 +1892,0 +1894,0 +1896,0 +1898,0 +1900,0 +1902,0 +1904,0 +1906,0 +1908,0 +1910,0 +1912,0 +1914,0 +1916,0 +1918,0 +1920,0 +1922,0 +1924,0 +1926,0 +1928,0 +1930,0 +1932,0 +1934,0 +1936,0 +1938,0 +1940,0 +1942,0 +1944,0 +1946,0 +1948,0 +1950,0 +1952,0 +1954,0 +1956,0 +1958,0 +1960,0 +1962,0 +1964,0 +1966,0 +1968,0 +1970,0 +1972,0 +1974,0 +1976,0 +1978,0 +1980,0 +1982,0 +1984,0 +1986,0 +1988,0 +1990,0 +1992,0 +1994,0 +1996,0 +1998,0 +2000,0 diff --git a/filt_func/tycho/B.dat b/filt_func/tycho/B.dat new file mode 100644 index 00000000..04a3bf2b --- /dev/null +++ b/filt_func/tycho/B.dat @@ -0,0 +1,34 @@ +# Tycho B filter from ADPS +# wavelength (Angstrom), transmission +3500.0 0.0000000000 +3550.0 0.0140000000 +3600.0 0.0580000000 +3650.0 0.1230000000 +3700.0 0.2060000000 +3750.0 0.3050000000 +3800.0 0.4160000000 +3850.0 0.5300000000 +3900.0 0.6360000000 +3950.0 0.7240000000 +4000.0 0.7870000000 +4050.0 0.8300000000 +4100.0 0.8610000000 +4150.0 0.8890000000 +4200.0 0.9200000000 +4250.0 0.9530000000 +4300.0 0.9820000000 +4350.0 1.0020000000 +4400.0 0.9760000000 +4450.0 0.8610000000 +4500.0 0.6850000000 +4550.0 0.4890000000 +4600.0 0.3170000000 +4650.0 0.2020000000 +4700.0 0.1360000000 +4750.0 0.1010000000 +4800.0 0.0800000000 +4850.0 0.0590000000 +4900.0 0.0360000000 +4950.0 0.0160000000 +5000.0 0.0030000000 +5050.0 0.0000000000 diff --git a/filt_func/tycho/V.dat b/filt_func/tycho/V.dat new file mode 100644 index 00000000..4313338b --- /dev/null +++ b/filt_func/tycho/V.dat @@ -0,0 +1,47 @@ +# Tycho V filter from ADPS +# wavelength (Angstrom), transmission +4550.0 0.0000000000 +4600.0 0.0220000000 +4650.0 0.1150000000 +4700.0 0.3010000000 +4750.0 0.5300000000 +4800.0 0.7370000000 +4850.0 0.8700000000 +4900.0 0.9400000000 +4950.0 0.9730000000 +5000.0 0.9900000000 +5050.0 0.9960000000 +5100.0 0.9910000000 +5150.0 0.9750000000 +5200.0 0.9490000000 +5250.0 0.9160000000 +5300.0 0.8780000000 +5350.0 0.8370000000 +5400.0 0.7940000000 +5450.0 0.7490000000 +5500.0 0.7040000000 +5550.0 0.6580000000 +5600.0 0.6120000000 +5650.0 0.5650000000 +5700.0 0.5180000000 +5750.0 0.4710000000 +5800.0 0.4240000000 +5850.0 0.3790000000 +5900.0 0.3350000000 +5950.0 0.2930000000 +6000.0 0.2540000000 +6050.0 0.2180000000 +6100.0 0.1860000000 +6150.0 0.1590000000 +6200.0 0.1350000000 +6250.0 0.1140000000 +6300.0 0.0970000000 +6350.0 0.0820000000 +6400.0 0.0690000000 +6450.0 0.0580000000 +6500.0 0.0470000000 +6550.0 0.0380000000 +6600.0 0.0280000000 +6650.0 0.0180000000 +6700.0 0.0080000000 +6750.0 0.0000000000 diff --git a/filt_func/ukirt/README.txt b/filt_func/ukirt/README.txt index b4f3b253..87a63d3f 100755 --- a/filt_func/ukirt/README.txt +++ b/filt_func/ukirt/README.txt @@ -1,2 +1,2 @@ -These are the UKIRT filters (JHK) from this site: +These are the UKIRT filters (ZYJHK) from this site: http://www.ukidss.org/technical/photom/photom.html diff --git a/filt_func/ukirt/Y.dat b/filt_func/ukirt/Y.dat new file mode 100644 index 00000000..a440de18 --- /dev/null +++ b/filt_func/ukirt/Y.dat @@ -0,0 +1,603 @@ +# Table 3 System throughput curve for the WFCAM Y filter +# 1.0mm water vapour, 1.3 airmass +# Col 1: wavelength (microns) +# Col 2: throughput +# Wavelength(microns) Transmission + 0.9005 0.0000 + 0.9010 0.0000 + 0.9015 0.0000 + 0.9020 0.0000 + 0.9025 0.0000 + 0.9030 0.0000 + 0.9035 0.0000 + 0.9040 0.0000 + 0.9045 0.0000 + 0.9050 0.0000 + 0.9055 0.0000 + 0.9060 0.0000 + 0.9065 0.0000 + 0.9070 0.0000 + 0.9075 0.0000 + 0.9080 0.0000 + 0.9085 0.0000 + 0.9090 0.0000 + 0.9095 0.0000 + 0.9100 0.0000 + 0.9105 0.0000 + 0.9110 0.0000 + 0.9115 0.0000 + 0.9120 0.0000 + 0.9125 0.0000 + 0.9130 0.0000 + 0.9135 0.0000 + 0.9140 0.0000 + 0.9145 0.0000 + 0.9150 0.0000 + 0.9155 0.0000 + 0.9160 0.0000 + 0.9165 0.0000 + 0.9170 0.0000 + 0.9175 0.0000 + 0.9180 0.0000 + 0.9185 0.0000 + 0.9190 0.0000 + 0.9195 0.0000 + 0.9200 0.0000 + 0.9205 0.0000 + 0.9210 0.0000 + 0.9215 0.0000 + 0.9220 0.0000 + 0.9225 0.0000 + 0.9230 0.0000 + 0.9235 0.0000 + 0.9240 0.0000 + 0.9245 0.0000 + 0.9250 0.0000 + 0.9255 0.0000 + 0.9260 0.0000 + 0.9265 0.0000 + 0.9270 0.0000 + 0.9275 0.0000 + 0.9280 0.0000 + 0.9285 0.0000 + 0.9290 0.0000 + 0.9295 0.0000 + 0.9300 0.0000 + 0.9305 0.0001 + 0.9310 0.0001 + 0.9315 0.0001 + 0.9320 0.0000 + 0.9325 0.0001 + 0.9330 0.0001 + 0.9335 0.0001 + 0.9340 0.0001 + 0.9345 0.0001 + 0.9350 0.0001 + 0.9355 0.0001 + 0.9360 0.0001 + 0.9365 0.0001 + 0.9370 0.0001 + 0.9375 0.0001 + 0.9380 0.0001 + 0.9385 0.0001 + 0.9390 0.0001 + 0.9395 0.0001 + 0.9400 0.0001 + 0.9405 0.0001 + 0.9410 0.0001 + 0.9415 0.0001 + 0.9420 0.0001 + 0.9425 0.0002 + 0.9430 0.0001 + 0.9435 0.0002 + 0.9440 0.0002 + 0.9445 0.0001 + 0.9450 0.0002 + 0.9455 0.0002 + 0.9460 0.0002 + 0.9465 0.0002 + 0.9470 0.0003 + 0.9475 0.0003 + 0.9480 0.0003 + 0.9485 0.0003 + 0.9490 0.0004 + 0.9495 0.0004 + 0.9500 0.0004 + 0.9505 0.0004 + 0.9510 0.0005 + 0.9515 0.0005 + 0.9520 0.0005 + 0.9525 0.0005 + 0.9530 0.0006 + 0.9535 0.0007 + 0.9540 0.0007 + 0.9545 0.0008 + 0.9550 0.0009 + 0.9555 0.0009 + 0.9560 0.0010 + 0.9565 0.0011 + 0.9570 0.0011 + 0.9575 0.0014 + 0.9580 0.0015 + 0.9585 0.0016 + 0.9590 0.0017 + 0.9595 0.0018 + 0.9600 0.0021 + 0.9605 0.0024 + 0.9610 0.0026 + 0.9615 0.0029 + 0.9620 0.0032 + 0.9625 0.0033 + 0.9630 0.0038 + 0.9635 0.0044 + 0.9640 0.0044 + 0.9645 0.0053 + 0.9650 0.0058 + 0.9655 0.0067 + 0.9660 0.0074 + 0.9665 0.0077 + 0.9670 0.0091 + 0.9675 0.0103 + 0.9680 0.0116 + 0.9685 0.0127 + 0.9690 0.0145 + 0.9695 0.0162 + 0.9700 0.0181 + 0.9705 0.0199 + 0.9710 0.0228 + 0.9715 0.0256 + 0.9720 0.0283 + 0.9725 0.0314 + 0.9730 0.0354 + 0.9735 0.0392 + 0.9740 0.0422 + 0.9745 0.0470 + 0.9750 0.0525 + 0.9755 0.0578 + 0.9760 0.0616 + 0.9765 0.0679 + 0.9770 0.0752 + 0.9775 0.0812 + 0.9780 0.0867 + 0.9785 0.0903 + 0.9790 0.0983 + 0.9795 0.1011 + 0.9800 0.1088 + 0.9805 0.1111 + 0.9810 0.1181 + 0.9815 0.1216 + 0.9820 0.1250 + 0.9825 0.1278 + 0.9830 0.1306 + 0.9835 0.1332 + 0.9840 0.1360 + 0.9845 0.1373 + 0.9850 0.1389 + 0.9855 0.1409 + 0.9860 0.1422 + 0.9865 0.1434 + 0.9870 0.1442 + 0.9875 0.1451 + 0.9880 0.1463 + 0.9885 0.1474 + 0.9890 0.1483 + 0.9895 0.1491 + 0.9900 0.1500 + 0.9905 0.1512 + 0.9910 0.1522 + 0.9915 0.1532 + 0.9920 0.1543 + 0.9925 0.1554 + 0.9930 0.1562 + 0.9935 0.1576 + 0.9940 0.1588 + 0.9945 0.1599 + 0.9950 0.1611 + 0.9955 0.1622 + 0.9960 0.1634 + 0.9965 0.1646 + 0.9970 0.1656 + 0.9975 0.1668 + 0.9980 0.1677 + 0.9985 0.1688 + 0.9990 0.1697 + 0.9995 0.1705 + 1.0000 0.1714 + 1.0005 0.1723 + 1.0010 0.1730 + 1.0015 0.1736 + 1.0020 0.1739 + 1.0025 0.1747 + 1.0030 0.1753 + 1.0035 0.1757 + 1.0040 0.1759 + 1.0045 0.1763 + 1.0050 0.1766 + 1.0055 0.1768 + 1.0060 0.1770 + 1.0065 0.1771 + 1.0070 0.1772 + 1.0075 0.1773 + 1.0080 0.1772 + 1.0085 0.1773 + 1.0090 0.1772 + 1.0095 0.1770 + 1.0100 0.1772 + 1.0105 0.1772 + 1.0110 0.1771 + 1.0115 0.1771 + 1.0120 0.1769 + 1.0125 0.1769 + 1.0130 0.1769 + 1.0135 0.1768 + 1.0140 0.1768 + 1.0145 0.1767 + 1.0150 0.1767 + 1.0155 0.1766 + 1.0160 0.1767 + 1.0165 0.1767 + 1.0170 0.1767 + 1.0175 0.1767 + 1.0180 0.1768 + 1.0185 0.1769 + 1.0190 0.1769 + 1.0195 0.1768 + 1.0200 0.1771 + 1.0205 0.1771 + 1.0210 0.1773 + 1.0215 0.1774 + 1.0220 0.1775 + 1.0225 0.1776 + 1.0230 0.1777 + 1.0235 0.1778 + 1.0240 0.1778 + 1.0245 0.1781 + 1.0250 0.1783 + 1.0255 0.1784 + 1.0260 0.1785 + 1.0265 0.1787 + 1.0270 0.1788 + 1.0275 0.1788 + 1.0280 0.1791 + 1.0285 0.1792 + 1.0290 0.1793 + 1.0295 0.1794 + 1.0300 0.1795 + 1.0305 0.1797 + 1.0310 0.1798 + 1.0315 0.1799 + 1.0320 0.1801 + 1.0325 0.1802 + 1.0330 0.1803 + 1.0335 0.1804 + 1.0340 0.1805 + 1.0345 0.1807 + 1.0350 0.1808 + 1.0355 0.1809 + 1.0360 0.1811 + 1.0365 0.1812 + 1.0370 0.1813 + 1.0375 0.1815 + 1.0380 0.1816 + 1.0385 0.1818 + 1.0390 0.1819 + 1.0395 0.1821 + 1.0400 0.1822 + 1.0405 0.1824 + 1.0410 0.1826 + 1.0415 0.1827 + 1.0420 0.1829 + 1.0425 0.1831 + 1.0430 0.1832 + 1.0435 0.1834 + 1.0440 0.1835 + 1.0445 0.1837 + 1.0450 0.1838 + 1.0455 0.1840 + 1.0460 0.1841 + 1.0465 0.1842 + 1.0470 0.1844 + 1.0475 0.1845 + 1.0480 0.1846 + 1.0485 0.1846 + 1.0490 0.1847 + 1.0495 0.1847 + 1.0500 0.1847 + 1.0505 0.1848 + 1.0510 0.1849 + 1.0515 0.1848 + 1.0520 0.1848 + 1.0525 0.1848 + 1.0530 0.1848 + 1.0535 0.1847 + 1.0540 0.1847 + 1.0545 0.1846 + 1.0550 0.1845 + 1.0555 0.1845 + 1.0560 0.1844 + 1.0565 0.1843 + 1.0570 0.1842 + 1.0575 0.1841 + 1.0580 0.1841 + 1.0585 0.1840 + 1.0590 0.1839 + 1.0595 0.1839 + 1.0600 0.1839 + 1.0605 0.1839 + 1.0610 0.1839 + 1.0615 0.1840 + 1.0620 0.1840 + 1.0625 0.1841 + 1.0630 0.1842 + 1.0635 0.1843 + 1.0640 0.1844 + 1.0645 0.1846 + 1.0650 0.1848 + 1.0655 0.1850 + 1.0660 0.1852 + 1.0665 0.1854 + 1.0670 0.1856 + 1.0675 0.1857 + 1.0680 0.1856 + 1.0685 0.1859 + 1.0690 0.1860 + 1.0695 0.1858 + 1.0700 0.1856 + 1.0705 0.1854 + 1.0710 0.1848 + 1.0715 0.1841 + 1.0720 0.1830 + 1.0725 0.1819 + 1.0730 0.1803 + 1.0735 0.1787 + 1.0740 0.1762 + 1.0745 0.1734 + 1.0750 0.1704 + 1.0755 0.1674 + 1.0760 0.1634 + 1.0765 0.1592 + 1.0770 0.1544 + 1.0775 0.1486 + 1.0780 0.1441 + 1.0785 0.1386 + 1.0790 0.1326 + 1.0795 0.1263 + 1.0800 0.1197 + 1.0805 0.1133 + 1.0810 0.1069 + 1.0815 0.1002 + 1.0820 0.0943 + 1.0825 0.0884 + 1.0830 0.0826 + 1.0835 0.0760 + 1.0840 0.0712 + 1.0845 0.0658 + 1.0850 0.0612 + 1.0855 0.0565 + 1.0860 0.0518 + 1.0865 0.0477 + 1.0870 0.0441 + 1.0875 0.0406 + 1.0880 0.0374 + 1.0885 0.0344 + 1.0890 0.0316 + 1.0895 0.0290 + 1.0900 0.0268 + 1.0905 0.0244 + 1.0910 0.0227 + 1.0915 0.0210 + 1.0920 0.0193 + 1.0925 0.0175 + 1.0930 0.0163 + 1.0935 0.0151 + 1.0940 0.0139 + 1.0945 0.0128 + 1.0950 0.0118 + 1.0955 0.0110 + 1.0960 0.0100 + 1.0965 0.0094 + 1.0970 0.0088 + 1.0975 0.0081 + 1.0980 0.0075 + 1.0985 0.0070 + 1.0990 0.0065 + 1.0995 0.0060 + 1.1000 0.0056 + 1.1005 0.0052 + 1.1010 0.0048 + 1.1015 0.0046 + 1.1020 0.0042 + 1.1025 0.0040 + 1.1030 0.0037 + 1.1035 0.0035 + 1.1040 0.0033 + 1.1045 0.0030 + 1.1050 0.0029 + 1.1055 0.0027 + 1.1060 0.0025 + 1.1065 0.0023 + 1.1070 0.0022 + 1.1075 0.0021 + 1.1080 0.0019 + 1.1085 0.0018 + 1.1090 0.0018 + 1.1095 0.0017 + 1.1100 0.0016 + 1.1105 0.0014 + 1.1110 0.0014 + 1.1115 0.0012 + 1.1120 0.0013 + 1.1125 0.0012 + 1.1130 0.0010 + 1.1135 0.0010 + 1.1140 0.0009 + 1.1145 0.0010 + 1.1150 0.0008 + 1.1155 0.0008 + 1.1160 0.0008 + 1.1165 0.0005 + 1.1170 0.0006 + 1.1175 0.0006 + 1.1180 0.0007 + 1.1185 0.0005 + 1.1190 0.0005 + 1.1195 0.0005 + 1.1200 0.0005 + 1.1205 0.0004 + 1.1210 0.0005 + 1.1215 0.0004 + 1.1220 0.0003 + 1.1225 0.0003 + 1.1230 0.0004 + 1.1235 0.0003 + 1.1240 0.0003 + 1.1245 0.0003 + 1.1250 0.0003 + 1.1255 0.0001 + 1.1260 0.0003 + 1.1265 0.0003 + 1.1270 0.0003 + 1.1275 0.0002 + 1.1280 0.0002 + 1.1285 0.0002 + 1.1290 0.0002 + 1.1295 0.0002 + 1.1300 0.0002 + 1.1305 0.0002 + 1.1310 0.0002 + 1.1315 0.0002 + 1.1320 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1.1635 0.0000 + 1.1640 0.0001 + 1.1645 0.0001 + 1.1650 0.0000 + 1.1655 0.0000 + 1.1660 0.0000 + 1.1665 0.0000 + 1.1670 0.0000 + 1.1675 0.0000 + 1.1680 0.0000 + 1.1685 0.0000 + 1.1690 0.0000 + 1.1695 0.0000 + 1.1700 0.0000 + 1.1705 0.0000 + 1.1710 0.0000 + 1.1715 0.0000 + 1.1720 0.0000 + 1.1725 0.0000 + 1.1730 0.0000 + 1.1735 0.0000 + 1.1740 0.0000 + 1.1745 0.0000 + 1.1750 0.0000 + 1.1755 0.0000 + 1.1760 0.0000 + 1.1765 0.0000 + 1.1770 0.0000 + 1.1775 0.0000 + 1.1780 0.0000 + 1.1785 0.0000 + 1.1790 0.0000 + 1.1795 0.0000 + 1.1800 0.0000 + 1.1805 0.0000 + 1.1810 0.0000 + 1.1815 0.0000 + 1.1820 0.0000 + 1.1825 0.0000 + 1.1830 0.0000 + 1.1835 0.0000 + 1.1840 0.0000 + 1.1845 0.0000 + 1.1850 0.0000 + 1.1855 0.0000 + 1.1860 0.0000 + 1.1865 0.0000 + 1.1870 0.0000 + 1.1875 0.0000 + 1.1880 0.0000 + 1.1885 0.0000 + 1.1890 0.0000 + 1.1895 0.0000 + 1.1900 0.0000 + 1.1905 0.0000 + 1.1910 0.0000 + 1.1915 0.0000 + 1.1920 0.0000 + 1.1925 0.0000 + 1.1930 0.0000 + 1.1935 0.0000 + 1.1940 0.0000 + 1.1945 0.0000 + 1.1950 0.0000 + 1.1955 0.0000 + 1.1960 0.0000 + 1.1965 0.0000 + 1.1970 0.0000 + 1.1975 0.0000 + 1.1980 0.0000 + 1.1985 0.0000 + 1.1990 0.0000 diff --git a/filt_func/ukirt/Z.dat b/filt_func/ukirt/Z.dat new file mode 100644 index 00000000..7b56f0a9 --- /dev/null +++ b/filt_func/ukirt/Z.dat @@ -0,0 +1,466 @@ +# Table 2 System throughput curve for the WFCAM Z filter +# 1.0mm water vapour, 1.3 airmass +# Col 1: wavelength (microns) +# Col 2: throughput +# Wavelength(microns) Transmission + 0.8000 0.0003 + 0.8005 0.0003 + 0.8010 0.0003 + 0.8015 0.0004 + 0.8020 0.0004 + 0.8025 0.0004 + 0.8030 0.0005 + 0.8035 0.0005 + 0.8040 0.0005 + 0.8045 0.0006 + 0.8050 0.0006 + 0.8055 0.0007 + 0.8060 0.0007 + 0.8065 0.0007 + 0.8070 0.0008 + 0.8075 0.0008 + 0.8080 0.0009 + 0.8085 0.0010 + 0.8090 0.0010 + 0.8095 0.0011 + 0.8100 0.0011 + 0.8105 0.0012 + 0.8110 0.0013 + 0.8115 0.0014 + 0.8120 0.0015 + 0.8125 0.0016 + 0.8130 0.0017 + 0.8135 0.0018 + 0.8140 0.0020 + 0.8145 0.0021 + 0.8150 0.0023 + 0.8155 0.0025 + 0.8160 0.0028 + 0.8165 0.0029 + 0.8170 0.0033 + 0.8175 0.0036 + 0.8180 0.0038 + 0.8185 0.0043 + 0.8190 0.0046 + 0.8195 0.0051 + 0.8200 0.0055 + 0.8205 0.0062 + 0.8210 0.0067 + 0.8215 0.0074 + 0.8220 0.0081 + 0.8225 0.0090 + 0.8230 0.0093 + 0.8235 0.0108 + 0.8240 0.0122 + 0.8245 0.0137 + 0.8250 0.0154 + 0.8255 0.0172 + 0.8260 0.0189 + 0.8265 0.0213 + 0.8270 0.0237 + 0.8275 0.0257 + 0.8280 0.0284 + 0.8285 0.0311 + 0.8290 0.0338 + 0.8295 0.0375 + 0.8300 0.0410 + 0.8305 0.0443 + 0.8310 0.0481 + 0.8315 0.0521 + 0.8320 0.0558 + 0.8325 0.0599 + 0.8330 0.0647 + 0.8335 0.0692 + 0.8340 0.0736 + 0.8345 0.0787 + 0.8350 0.0834 + 0.8355 0.0882 + 0.8360 0.0930 + 0.8365 0.0979 + 0.8370 0.1028 + 0.8375 0.1074 + 0.8380 0.1118 + 0.8385 0.1163 + 0.8390 0.1205 + 0.8395 0.1243 + 0.8400 0.1279 + 0.8405 0.1314 + 0.8410 0.1346 + 0.8415 0.1379 + 0.8420 0.1409 + 0.8425 0.1436 + 0.8430 0.1462 + 0.8435 0.1484 + 0.8440 0.1506 + 0.8445 0.1524 + 0.8450 0.1541 + 0.8455 0.1553 + 0.8460 0.1566 + 0.8465 0.1574 + 0.8470 0.1582 + 0.8475 0.1588 + 0.8480 0.1593 + 0.8485 0.1597 + 0.8490 0.1601 + 0.8495 0.1604 + 0.8500 0.1608 + 0.8505 0.1610 + 0.8510 0.1614 + 0.8515 0.1614 + 0.8520 0.1618 + 0.8525 0.1620 + 0.8530 0.1621 + 0.8535 0.1623 + 0.8540 0.1624 + 0.8545 0.1625 + 0.8550 0.1626 + 0.8555 0.1628 + 0.8560 0.1630 + 0.8565 0.1632 + 0.8570 0.1635 + 0.8575 0.1638 + 0.8580 0.1643 + 0.8585 0.1647 + 0.8590 0.1652 + 0.8595 0.1658 + 0.8600 0.1664 + 0.8605 0.1670 + 0.8610 0.1676 + 0.8615 0.1682 + 0.8620 0.1688 + 0.8625 0.1694 + 0.8630 0.1700 + 0.8635 0.1705 + 0.8640 0.1710 + 0.8645 0.1714 + 0.8650 0.1718 + 0.8655 0.1721 + 0.8660 0.1725 + 0.8665 0.1727 + 0.8670 0.1730 + 0.8675 0.1731 + 0.8680 0.1733 + 0.8685 0.1734 + 0.8690 0.1735 + 0.8695 0.1736 + 0.8700 0.1737 + 0.8705 0.1738 + 0.8710 0.1739 + 0.8715 0.1740 + 0.8720 0.1742 + 0.8725 0.1744 + 0.8730 0.1746 + 0.8735 0.1750 + 0.8740 0.1753 + 0.8745 0.1756 + 0.8750 0.1760 + 0.8755 0.1764 + 0.8760 0.1769 + 0.8765 0.1773 + 0.8770 0.1777 + 0.8775 0.1781 + 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1.0030 0.0000 + 1.0035 0.0000 + 1.0040 0.0000 + 1.0045 0.0000 + 1.0050 0.0000 + 1.0055 0.0000 + 1.0060 0.0000 + 1.0065 0.0000 + 1.0070 0.0000 + 1.0075 0.0000 + 1.0080 0.0000 + 1.0085 0.0000 + 1.0090 0.0000 + 1.0095 0.0000 + 1.0100 0.0000 + 1.0105 0.0000 + 1.0110 0.0000 + 1.0115 0.0000 + 1.0120 0.0000 + 1.0125 0.0000 + 1.0130 0.0000 + 1.0135 0.0000 + 1.0140 0.0000 + 1.0145 0.0000 + 1.0150 0.0000 + 1.0155 0.0000 + 1.0160 0.0000 + 1.0165 0.0000 + 1.0170 0.0000 + 1.0175 0.0000 + 1.0180 0.0000 + 1.0185 0.0000 + 1.0190 0.0000 + 1.0195 0.0000 + 1.0200 0.0000 + 1.0205 0.0000 + 1.0210 0.0000 + 1.0215 0.0000 + 1.0220 0.0000 + 1.0225 0.0000 + 1.0230 0.0000 + 1.0235 0.0000 + 1.0240 0.0000 + 1.0245 0.0000 + 1.0250 0.0000 + 1.0255 0.0000 + 1.0260 0.0000 + 1.0265 0.0000 + 1.0270 0.0000 + 1.0275 0.0000 + 1.0280 0.0000 + 1.0285 0.0000 + 1.0290 0.0000 + 1.0295 0.0000 + 1.0300 0.0000 diff --git a/filt_func/washington/C.dat b/filt_func/washington/C.dat new file mode 100644 index 00000000..005d6e6b --- /dev/null +++ b/filt_func/washington/C.dat @@ -0,0 +1,24 @@ +# C filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +300,0.000 +310,0.030 +320,0.157 +330,0.364 +340,0.591 +350,0.762 +360,0.860 +370,0.935 +380,0.984 +390,1.004 +400,0.987 +410,0.920 +420,0.819 +430,0.682 +440,0.542 +450,0.366 +460,0.168 +470,0.049 +480,0.000 +750,0.042 +760,0.024 +770,0.000 \ No newline at end of file diff --git a/filt_func/washington/M.dat b/filt_func/washington/M.dat new file mode 100644 index 00000000..8f61e17a --- /dev/null +++ b/filt_func/washington/M.dat @@ -0,0 +1,21 @@ +# M filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +430,0.000 +440,0.015 +450,0.158 +460,0.448 +470,0.708 +480,0.866 +490,0.950 +500,0.993 +510,0.996 +520,0.944 +530,0.886 +540,0.800 +550,0.682 +560,0.518 +570,0.339 +580,0.190 +590,0.084 +600,0.016 +610,0.000 diff --git a/filt_func/washington/T1.dat b/filt_func/washington/T1.dat new file mode 100644 index 00000000..115ee779 --- /dev/null +++ b/filt_func/washington/T1.dat @@ -0,0 +1,21 @@ +# T1 filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +560,0.000 +570,0.037 +580,0.103 +590,0.197 +600,0.663 +610,0.968 +620,1.011 +630,0.937 +640,0.842 +650,0.744 +660,0.656 +670,0.556 +680,0.448 +690,0.359 +700,0.291 +710,0.241 +720,0.164 +730,0.118 +740,0.069 diff --git a/filt_func/washington/T2.dat b/filt_func/washington/T2.dat new file mode 100644 index 00000000..ba9834a7 --- /dev/null +++ b/filt_func/washington/T2.dat @@ -0,0 +1,20 @@ +# T2 filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +720,0.000 +730,0.130 +740,0.540 +750,0.698 +760,0.803 +770,0.875 +780,0.911 +790,0.932 +800,0.954 +810,0.975 +820,0.987 +830,0.999 +840,0.999 +850,0.979 +860,0.945 +870,0.844 +880,0.620 +890,0.000 \ No newline at end of file diff --git a/licenses/LICENSE.rst b/licenses/LICENSE.rst deleted file mode 100644 index c19dffca..00000000 --- a/licenses/LICENSE.rst +++ /dev/null @@ -1,708 +0,0 @@ -Copyright (C) 2020, Matthew Hosek Jr., Jessica Lu, Casey Lam, Abhimat Gautam, Kelly Lockhart, Dongwon Kim, Siyao Jia - -This program is free software: you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation, either version 3 of the License, or -any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -You should have received a copy of the GNU General Public License -along with this program. If not, see . - ---- - -****************************************************************************** -GNU General Public License -****************************************************************************** - -Version 3, 29 June 2007 - -Copyright (c) 2007 Free Software Foundation, Inc. <`http://fsf.org/`_> - -Everyone is permitted to copy and distribute verbatim copies of this license -document, but changing it is not allowed. - -.. contents:: - -Preamble -============================================================================== - -The GNU General Public License is a free, copyleft license for software and -other kinds of works. - -The licenses for most software and other practical works are designed to take -away your freedom to share and change the works. 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For more -information on this, and how to apply and follow the GNU GPL, see -<`http://www.gnu.org/licenses/`_>. - -The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you may -consider it more useful to permit linking proprietary applications with the -library. If this is what you want to do, use the GNU Lesser General Public -License instead of this License. But first, please read -<`http://www.gnu.org/philosophy/why-not-lgpl.html`_>. - -.. _http://fsf.org/: http://fsf.org/ -.. _http://www.gnu.org/licenses/: http://www.gnu.org/licenses/ -.. _http://www.gnu.org/philosophy/why-not-lgpl.html: - http://www.gnu.org/philosophy/why-not-lgpl.html diff --git a/licenses/README.rst b/licenses/README.rst deleted file mode 100644 index fa84ca8e..00000000 --- a/licenses/README.rst +++ /dev/null @@ -1,9 +0,0 @@ -Licenses -======== - -This directory holds license and credit information for the package, -works the package is derived from, and/or datasets. - -Ensure that you pick a package license which is in this folder and it matches -the one mentioned in the top level README.rst file. If you are using the -pre-rendered version of this template check for the word 'Other' in the README. diff --git a/licenses/TEMPLATE_LICENCE.rst b/licenses/TEMPLATE_LICENCE.rst deleted file mode 100644 index a460a728..00000000 --- a/licenses/TEMPLATE_LICENCE.rst +++ /dev/null @@ -1,31 +0,0 @@ -This project is based upon the Astropy package template -(https://github.com/astropy/package-template/) which is licensed under the terms -of the following license. - ---- - -Copyright (c) 2018, Astropy Developers -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. -* Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the documentation and/or - other materials provided with the distribution. -* Neither the name of the Astropy Team nor the names of its contributors may be - used to endorse or promote products derived from this software without - specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/pyproject.toml b/pyproject.toml index 6c989ab1..5b46d1d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,110 @@ -[build-system] +[project] +name = "SPISEA" +description = "SPISEA (Stellar Population Interface for Synthetic Evolution and Atmospheres) is a package that generates single-age, single-metallicity populations (i.e. star clusters)" +version = "2.4.1" +authors = [ + {name = "Matt Hosek Jr.", email = "mwhosek@astro.ucla.edu"}, + {name = "Jessica R. Lu", email = "jlu.astro@berkeley.edu"}, + {name = "Samantha Rose"}, + {name = "Casey Lam"}, + {name = "Natasha Abrams"}, + {name = "Michael Medford"}, + {name = "Lingfeng Wei"}, + {name = "Macy Huston"}, + {name = "Abhimat Gautam"}, + {name = "Kelly Lockhart"}, + {name = "Dongwon Kim"}, + {name = "Siyao Jia"} +] +requires-python = ">=3.11" +readme = "README.md" +license = "GPL-3.0-or-later" +license-files = ["LICENSE*"] + +dependencies = [ + "numpy>=1.24.3", + "scipy>=1.10.1", + "matplotlib", + "astropy", + "pysynphot" +] + +optional-dependencies.dev = [ + "setuptools>=61.0", + "setuptools-scm", + "wheel", + "numpydoc", + "sphinx_rtd_theme", + "pytest" +] -requires = ["setuptools", - "setuptools_scm", - "wheel", - "extension-helpers", - "oldest-supported-numpy", - "cython==0.29.14"] +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Programming Language :: Python :: 3.11", + "Operating System :: OS Independent", + "Intended Audience :: Developers" +] +[project.urls] +Homepage = "https://github.com/astropy/SPISEA" +Issues = "https://github.com/astropy/SPISEA/issues" + +[build-system] +requires = [ + "setuptools>=61.0", + "setuptools-scm", + "wheel", + "numpydoc", + "sphinx_rtd_theme", + "pytest" +] build-backend = 'setuptools.build_meta' + +[tool.setuptools] +packages = ["spisea"] + +[tool.setuptools.package-data] +spisea = ["data/*"] + +[tool.pytest] +minversion = "9.0" +addopts = ["-ra", "-q"] +testpaths = [ + "spisea", + "docs", +] + +### Stuff from setup.cfg that hasn't been ported over. +### Not sure if anyone was using test coverage. +#[coverage:run] +#omit = +# spisea/_astropy_init* +# spisea/conftest.py +# spisea/*setup_package* +# spisea/tests/* +# spisea/*/tests/* +# spisea/extern/* +# spisea/version* +# */spisea/_astropy_init* +# */spisea/conftest.py +# */spisea/*setup_package* +# */spisea/tests/* +# */spisea/*/tests/* +# */spisea/extern/* +# */spisea/version* +# +#[coverage:report] +#exclude_lines = +# # Have to re-enable the standard pragma +# pragma: no cover +# # Don't complain about packages we have installed +# except ImportError +# # Don't complain if tests don't hit assertions +# raise AssertionError +# raise NotImplementedError +# # Don't complain about script hooks +# def main\(.*\): +# # Ignore branches that don't pertain to this version of Python +# pragma: py{ignore_python_version} +# # Don't complain about IPython completion helper +# def _ipython_key_completions_ diff --git a/setup.cfg b/setup.cfg deleted file mode 100755 index 4d69fa9c..00000000 --- a/setup.cfg +++ /dev/null @@ -1,77 +0,0 @@ -[metadata] -name = spisea -author = Matthew Hosek Jr., Jessica Lu, Casey Lam, Abhimat Gautam, Kelly Lockhart, Dongwon Kim, Siyao Jia -author_email = mwhosek@astro.ucla.edu -license = GNU GPL v3+ -license_file = licenses/LICENSE.rst -url = https://github.com/astropy/SPISEA -description = SPISEA is an python package that generates single-age, single-metallicity populations (i.e. star clusters). -long_description = file: README.rst -long_description_content_type = text/x-rst -edit_on_github = False -github_project = astropy/astropy - -[options] -zip_safe = False -packages = find: -python_requires = >=3.7 -setup_requires = setuptools_scm -install_requires = - astropy - pysynphot - scipy - numpy - matplotlib - -[options.entry_points] -console_scripts = - astropy-package-template-example = packagename.example_mod:main - -[options.extras_require] -test = - pytest-astropy -docs = - sphinx-astropy - -[options.package_data] -spisea = data/* - -[tool:pytest] -testpaths = "spisea" "docs" -astropy_header = true -doctest_plus = enabled -text_file_format = rst -#addopts = --doctest-rst - -[coverage:run] -omit = - spisea/_astropy_init* - spisea/conftest.py - spisea/*setup_package* - spisea/tests/* - spisea/*/tests/* - spisea/extern/* - spisea/version* - */spisea/_astropy_init* - */spisea/conftest.py - */spisea/*setup_package* - */spisea/tests/* - */spisea/*/tests/* - */spisea/extern/* - */spisea/version* - -[coverage:report] -exclude_lines = - # Have to re-enable the standard pragma - pragma: no cover - # Don't complain about packages we have installed - except ImportError - # Don't complain if tests don't hit assertions - raise AssertionError - raise NotImplementedError - # Don't complain about script hooks - def main\(.*\): - # Ignore branches that don't pertain to this version of Python - pragma: py{ignore_python_version} - # Don't complain about IPython completion helper - def _ipython_key_completions_ diff --git a/setup.py b/setup.py deleted file mode 100755 index 5f83aee4..00000000 --- a/setup.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python -# Licensed under a 3-clause BSD style license - see LICENSE.rst - -# NOTE: The configuration for the package, including the name, version, and -# other information are set in the setup.cfg file. - -import os -import sys - -from setuptools import setup - - -# First provide helpful messages if contributors try and run legacy commands -# for tests or docs. - -TEST_HELP = """ -Note: running tests is no longer done using 'python setup.py test'. Instead -you will need to run: - - tox -e test - -If you don't already have tox installed, you can install it with: - - pip install tox - -If you only want to run part of the test suite, you can also use pytest -directly with:: - - pip install -e .[test] - pytest - -For more information, see: - - http://docs.astropy.org/en/latest/development/testguide.html#running-tests -""" - -if 'test' in sys.argv: - print(TEST_HELP) - sys.exit(1) - -DOCS_HELP = """ -Note: building the documentation is no longer done using -'python setup.py build_docs'. Instead you will need to run: - - tox -e build_docs - -If you don't already have tox installed, you can install it with: - - pip install tox - -You can also build the documentation with Sphinx directly using:: - - pip install -e .[docs] - cd docs - make html - -For more information, see: - - http://docs.astropy.org/en/latest/install.html#builddocs -""" - -if 'build_docs' in sys.argv or 'build_sphinx' in sys.argv: - print(DOCS_HELP) - sys.exit(1) - -VERSION_TEMPLATE = """ -# Note that we need to fall back to the hard-coded version if either -# setuptools_scm can't be imported or setuptools_scm can't determine the -# version, so we catch the generic 'Exception'. -try: - from setuptools_scm import get_version - version = get_version(root='..', relative_to=__file__) -except Exception: - version = '{version}' -""".lstrip() - -setup(use_scm_version={'write_to': os.path.join('spisea', 'version.py'), - 'write_to_template': VERSION_TEMPLATE}) diff --git a/spisea/__init__.py b/spisea/__init__.py index 80caa065..57871c83 100755 --- a/spisea/__init__.py +++ b/spisea/__init__.py @@ -1,14 +1,7 @@ -# Licensed under a 3-clause BSD style license - see LICENSE.rst +import importlib.metadata -# Packages may add whatever they like to this file, but -# should keep this content at the top. -# ---------------------------------------------------------------------------- -from ._astropy_init import * # noqa -# ---------------------------------------------------------------------------- - -__all__ = [] -from spisea import * # noqa -# Then you can be explicit to control what ends up in the namespace, -__all__ += ['do_primes'] # noqa -# or you can keep everything from the subpackage with the following instead -# __all__ += example_mod.__all__ +try: + __version__ = importlib.metadata.version(__package__) +except importlib.metadata.PackageNotFoundError: + # Fallback for development mode if not installed + __version__ = "2.4.1" diff --git a/spisea/_astropy_init.py b/spisea/_astropy_init.py deleted file mode 100755 index 58189c4b..00000000 --- a/spisea/_astropy_init.py +++ /dev/null @@ -1,51 +0,0 @@ -# Licensed under a 3-clause BSD style license - see LICENSE.rst - -__all__ = ['__version__'] - -# this indicates whether or not we are in the package's setup.py -try: - _ASTROPY_SETUP_ -except NameError: - import builtins - builtins._ASTROPY_SETUP_ = False - -try: - from .version import version as __version__ -except ImportError: - __version__ = '' - -if not _ASTROPY_SETUP_: # noqa - import os - from warnings import warn - #from astropy.config.configuration import ( - # update_default_config, - # ConfigurationDefaultMissingError, - # ConfigurationDefaultMissingWarning) - - # Create the test function for self test - from astropy.tests.runner import TestRunner - test = TestRunner.make_test_runner_in(os.path.dirname(__file__)) - test.__test__ = False - __all__ += ['test'] - - # add these here so we only need to cleanup the namespace at the end - #config_dir = None - - #if not os.environ.get('ASTROPY_SKIP_CONFIG_UPDATE', False): - # config_dir = os.path.dirname(__file__) - # config_template = os.path.join(config_dir, __package__ + ".cfg") - # if os.path.isfile(config_template): - # try: - # update_default_config( - # __package__, config_dir, version=__version__) - # except TypeError as orig_error: - # try: - # update_default_config(__package__, config_dir) - # except ConfigurationDefaultMissingError as e: - # wmsg = (e.args[0] + - # " Cannot install default profile. If you are " - # "importing from source, this is expected.") - # warn(ConfigurationDefaultMissingWarning(wmsg)) - # del e - # except Exception: - # raise orig_error diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 0f94453e..4d884677 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -12,7 +12,7 @@ log = logging.getLogger('atmospheres') -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): """ Given atmosphere model, get temperature and gravity bounds """ @@ -22,11 +22,11 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 metal_list = np.unique(np.array(z_arr)) metal_idx = np.argmin(np.abs(metal_list - metallicity)) metallicity_new = metal_list[metal_idx] - + z_filt = np.where(z_arr == metal_list[metal_idx]) teff_arr = teff_arr[z_filt] logg_arr = logg_arr[z_filt] - + # # Now find the closest atmosphere in parameter space to # # the one we want. We'll find the match with the lowest # # fractional difference @@ -38,45 +38,46 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 # # temperature_new = teff_arr[idx_f] # gravity_new = logg_arr[idx_f] - + # First check if temperature within bounds temperature_new = temperature if temperature > np.max(teff_arr): temperature_new = np.max(teff_arr) if temperature < np.min(teff_arr): temperature_new = np.min(teff_arr) - + # If temperature within bounds, then check if metallicity within bounds teff_diff = np.abs(teff_arr - temperature) sorted_min_diffs = np.unique(teff_diff) - + ## Find two closest temperatures teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - + ## Switch to most conservative bound of logg out of two closest temps gravity_new = gravity if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) + + if verbose: + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) if metallicity_new != metallicity: logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - + return (temperature_new, gravity_new, metallicity_new) def get_atmosphere_grid(model_dir): @@ -120,7 +121,7 @@ def get_atmosphere_grid(model_dir): def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): """ - Return atmosphere from the Kurucz pysnphot grid + Return atmosphere from the Kurucz pysnphot grid (`Kurucz 1993 `_). Grid Range: @@ -139,7 +140,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal gravity: float The stellar gravity, in cgs units - + rebin: boolean Always false for this particular function """ @@ -152,7 +153,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) # Do some error checking @@ -181,10 +182,10 @@ def get_kurucz_atmosphere_grid(): def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): """ - Return atmospheres from the pysynphot ATLAS9 atlas + Return atmospheres from the pysynphot ATLAS9 atlas (`Castelli & Kurucz 2004 `_). - Grid Range: + Grid Range: * Teff: 3500 - 50000 K * gravity: 0 - 5.0 cgs @@ -200,7 +201,7 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -217,9 +218,9 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -262,7 +263,7 @@ def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=Fal metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) # Do some error checking @@ -315,7 +316,7 @@ def get_amesdusty_atmosphere_grid(): def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): """ - Return atmosphere from the pysynphot + Return atmosphere from the pysynphot `PHOENIX atlas `_. Parameters @@ -328,7 +329,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -343,7 +344,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) # Do some error checking @@ -365,7 +366,7 @@ def get_phoenix_atmosphere_grid(): teff_arr, z_arr, logg_arr = get_atmosphere_grid('phoenix') return teff_arr, z_arr, logg_arr -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): """ metallicity = [M/H] (def = 0) temperature = Kelvin (def = 24000) @@ -377,14 +378,15 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi # Take care of atmospheres outside the catalog boundaries logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' if gravity > 4.3: - print( logg_msg.format(4.3, temperature, gravity)) + if verbose: + print( logg_msg.format(4.3, temperature, gravity)) gravity = 4.3 - + if rebin: sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) else: sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -413,7 +415,7 @@ def get_cmfgenRot_atmosphere_grid(rebin=True): def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): """ - For a given stellar atmosphere, get extract the closest possible match in + For a given stellar atmosphere, get extract the closest possible match in Teff/logg space. Note that this is different from the normal routine which interpolates along the input grid to get final spectrum. We can't do this here because the Fierro+15 atmosphere grid is so sparse @@ -448,7 +450,7 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4 # fractional difference teff_diff = (teff_arr - temperature) / temperature logg_diff = (logg_arr - gravity) / gravity - + diff_tot = abs(teff_diff) + abs(logg_diff) idx_f = np.where(diff_tot == min(diff_tot))[0][0] @@ -456,7 +458,7 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4 # pysynphot object infile = cat[idx_f]['FILENAME'].split('.') spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the # ATLAS models are in FLAM at the surface. So, we need to multiply the # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. @@ -470,13 +472,13 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4 radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm radius /= 3.08*10**18 # in pc - + # Make the pysynphot spectrum w = spec['Wavelength'] f = spec['Flux'] * (1000 / radius)**2. sp = pysynphot.ArraySpectrum(w,f) - + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) # Print out parameters of match, if desired @@ -500,7 +502,7 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, r sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) else: sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -555,9 +557,9 @@ def get_cmfgenNoRot_atmosphere_grid(): def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + Models originally downloaded via `ftp `_. Solar metallicity and [alpha/Fe] is used. @@ -577,7 +579,7 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -598,9 +600,9 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -640,14 +642,14 @@ def get_phoenixv16_atmosphere_grid(rebin=True): def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, `Baraffe et al. 2015 `_ ) Grid originally downloaded from `website `_. Grid Range: - + * Teff: 1200 - 7000 K * gravity: 2.5 - 5.5 cgs * [M/H] = 0 @@ -662,11 +664,11 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. + which is often sufficient synthetic photometry in most cases. """ if rebin == True: atm_name = 'BTSettl_2015_rebin' @@ -681,10 +683,10 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - + + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -724,7 +726,7 @@ def get_BTSettl_2015_atmosphere_grid(rebin=True): def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): """ - Return atmosphere from CIFIST2011 grid + Return atmosphere from CIFIST2011 grid (`Allard et al. 2012 `_) Grid originally downloaded `here `_ @@ -732,16 +734,16 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T Notes ------ Grid Range: - + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - + Teff and gravity ranges depend on metallicity: [M/H] = -2.5 * Teff: 2600 - 4600 K * gravity: 4.5 - 5.5 - + [M/H] = -2.0 * Teff: 2600 - 7000 @@ -755,7 +757,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T [M/H] = -1.0 * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 [M/H] = -0.5 @@ -789,11 +791,15 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, which is often sufficient synthetic photometry in most cases. + + **PRINT STATEMENTS TO DEBUG + **check get_atmosphere_bounds + **comment out try/except clause and check break """ if rebin == True: atm_name = 'BTSettl_rebin' @@ -808,14 +814,92 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - +def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Meisner2023 grid + (`Meisner et al. 2023 `_) + + Grid originally downloaded `here `_ + + Grid range: + * Teff = 250 - 1200 K + * gravity: 2.5 - 5.5 cgs (in steps of 0.5) + * [M/H] = -1.0, -0.5, +0, +0.3 + + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') + print( 'Could not find Meisner2023 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_Meisner2023_atmosphere_grid(rebin=True): + """ + Return atmosphere grid from Meisner2023. + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + teff_arr, z_arr, logg_arr = get_atmosphere_grid(atm_name) + return teff_arr, z_arr, logg_arr + +def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Phillips et al., 2020 using ATMO model + (`Phillips et al. 2020 `_) + + Grid originally downloaded `here `_ + + Grid Range: + * Teff: 200 - 3000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + """ + if rebin == True: + atm_name = 'Phillips2020_rebin' + else: + atm_name = 'Phillips2020' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Phillips2020 atmosphere model for') print( ' temperature = %d' % temperature) print( ' metallicity = %.1f' % metallicity) print( ' log gravity = %.1f' % gravity) @@ -894,7 +978,7 @@ def get_BTSettl_atmosphere_grid(rebin=True): def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): """ - Return white dwarf atmospheres from + Return white dwarf atmospheres from `Koester et al. 2010 `_ Parameters @@ -907,7 +991,7 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -923,7 +1007,7 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): print( ' temperature = %d' % temperature) print( ' metallicity = %.1f' % metallicity) print( ' log gravity = %.1f' % gravity) - + return sp def get_wdKoester_atmosphere_grid(): @@ -938,7 +1022,7 @@ def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): """ Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 """ try: @@ -949,7 +1033,7 @@ def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) # Do some error checking @@ -976,7 +1060,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model and phoenixV16. - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 """ try: sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) @@ -986,7 +1070,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) # Do some error checking @@ -999,6 +1083,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): return sp + def get_BTSettl_phoenix_atmosphere_grid(): """ Return atmosphere grid that is a linear merge of BTSettl_CITFITS2011_2015 model @@ -1010,11 +1095,49 @@ def get_BTSettl_phoenix_atmosphere_grid(): return teff_arr, z_arr, logg_arr +def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl-Meisner merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_meisner_atmosphere_grid(): + """ + Return atmosphere grid that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + teff_arr, z_arr, logg_arr = get_atmosphere_grid('merged_BTSettl_meisner') + return teff_arr, z_arr, logg_arr + #---------------------------------------------------------------------# def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, rebin=True): """ - Return a stellar atmosphere from a suite of different model grids, + Return a stellar atmosphere from a suite of different model grids, depending on the input temperature, (all values in K). Parameters @@ -1027,7 +1150,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -1038,7 +1161,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose Notes ----- - The underlying stellar model grid used changes as a function of + The underlying stellar model grid used changes as a function of stellar temperature (in K): * T > 20,000: ATLAS @@ -1049,14 +1172,16 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose For T < 3800, there is an additional gravity and metallicity dependence: - If T < 3800 and [M/H] = 0: - + If T < 3800 and [M/H] = 0: + * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1000 < T <= 1200, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 250 < T <= 1000, logg > 2.5: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: - + * T < 3800: PHOENIX v16 References: @@ -1064,22 +1189,23 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) * PHOENIXv16 (`Husser et al. 2013 `_) * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - LTE WARNING: + LTE WARNING: The ATLAS atmospheres are calculated with LTE, and so they are less accurate when non-LTE conditions apply (e.g. T > 20,000 K). Ultimately we'd like to add a non-LTE atmosphere grid for the hottest stars in the future. - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to ensure a smooth transition. """ @@ -1087,6 +1213,30 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid + if (temperature < 1000): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 1200) & (temperature >= 1000): + if (gravity >= 3.5): + if verbose: + print( 'BTSettl/Meisner2023 merged atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (gravity < 3.5) & (gravity >=2.5): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (temperature <= 3800) & (metallicity == 0): # High gravity are in BTSettl regime if (temperature <= 3200) & (gravity > 2.5): @@ -1096,7 +1246,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose temperature=temperature, gravity=gravity, rebin=rebin) - + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): if verbose: print( 'BTSettl/Phoenixv16 merged atmosphere') @@ -1112,7 +1262,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose temperature=temperature, gravity=gravity, rebin=rebin) - + if (temperature <= 3800) & (metallicity != 0): if verbose: print( 'Phoenixv16 atmosphere') @@ -1135,7 +1285,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose return get_atlas_phoenix_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) - + if (temperature >= 5500) & (temperature < 20000): if verbose: print( 'ATLAS merged atmosphere') @@ -1155,10 +1305,12 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # temperature=temperature, # gravity=gravity) - + def get_merged_atmosphere_grid(rebin=True): # temp array, metallicity array, logg array + Meisner2023_atmosphere_arrs = np.array(get_Meisner2023_atmosphere_grid(rebin)) + BTSettl_2015_atmosphere_arrs = np.array(get_BTSettl_2015_atmosphere_grid(rebin)) BTSettl_phoenix_atmosphere_arrs = np.array(get_BTSettl_phoenix_atmosphere_grid()) @@ -1168,7 +1320,11 @@ def get_merged_atmosphere_grid(rebin=True): castelli_atmosphere_arrs = np.array(get_castelli_atmosphere_grid()) - BTSettl_2015_atmosphere_idxs = np.where((BTSettl_2015_atmosphere_arrs[0] <= 3200) &\ + Meisner2023_atmosphere_idxs = np.where(Meisner2023_atmosphere_arrs[0] <= 1200) + Meisner2023_atmosphere_arrs = Meisner2023_atmosphere_arrs[:,Meisner2023_atmosphere_idxs] + + BTSettl_2015_atmosphere_idxs = np.where((BTSettl_2015_atmosphere_arrs[0] > 1200) &\ + (BTSettl_2015_atmosphere_arrs[0] <= 3200) &\ (BTSettl_2015_atmosphere_arrs[1] == 0) &\ (BTSettl_2015_atmosphere_arrs[2] > 2.5)) BTSettl_2015_atmosphere_arrs = BTSettl_2015_atmosphere_arrs[:,BTSettl_2015_atmosphere_idxs] @@ -1190,15 +1346,18 @@ def get_merged_atmosphere_grid(rebin=True): castelli_atmosphere_idxs = np.where(castelli_atmosphere_arrs[0] >= 5500) castelli_atmosphere_arrs = castelli_atmosphere_arrs[:,castelli_atmosphere_idxs] - super_tarr = np.concatenate((BTSettl_2015_atmosphere_arrs[0][0], BTSettl_phoenix_atmosphere_arrs[0][0], + super_tarr = np.concatenate((Meisner2023_atmosphere_arrs[0][0], BTSettl_2015_atmosphere_arrs[0][0], + BTSettl_phoenix_atmosphere_arrs[0][0], phoenixv16_atmosphere_arrs[0][0], atlas_phoenix_atmosphere_arrs[0][0], castelli_atmosphere_arrs[0][0])) - - super_zarr = np.concatenate((BTSettl_2015_atmosphere_arrs[1][0], BTSettl_phoenix_atmosphere_arrs[1][0], + + super_zarr = np.concatenate((Meisner2023_atmosphere_arrs[1][0], BTSettl_2015_atmosphere_arrs[1][0], + BTSettl_phoenix_atmosphere_arrs[1][0], phoenixv16_atmosphere_arrs[1][0], atlas_phoenix_atmosphere_arrs[1][0], castelli_atmosphere_arrs[1][0])) - super_loggarr = np.concatenate((BTSettl_2015_atmosphere_arrs[2][0], BTSettl_phoenix_atmosphere_arrs[2][0], + super_loggarr = np.concatenate((Meisner2023_atmosphere_arrs[2][0], BTSettl_2015_atmosphere_arrs[2][0], + BTSettl_phoenix_atmosphere_arrs[2][0], phoenixv16_atmosphere_arrs[2][0], atlas_phoenix_atmosphere_arrs[2][0], castelli_atmosphere_arrs[2][0])) @@ -1247,7 +1406,9 @@ def get_merged_atmosphere_w_bb_supplement(metallicity=0, temperature=20000, grav * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 3200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1000 <= T <= 1200, logg >= 2.5: Meisner2023 + * 250 <= T < 1000: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -1258,6 +1419,7 @@ def get_merged_atmosphere_w_bb_supplement(metallicity=0, temperature=20000, grav * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) * PHOENIXv16 (`Husser et al. 2013 `_) * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) LTE WARNING: @@ -1277,110 +1439,51 @@ def get_merged_atmosphere_w_bb_supplement(metallicity=0, temperature=20000, grav ensure a smooth transition. """ - if (temperature <= 1000): - print('BB atmosphere') - return get_bb_atmosphere(temperature=temperature, - metallicity=metallicity, - gravity=gravity, - verbose=verbose) if (gravity >= 9.8): - print('BB atmosphere') + if verbose: + print('BB atmosphere') return get_bb_atmosphere(temperature=temperature, metallicity=metallicity, gravity=gravity, verbose=verbose) if (temperature < 4.6e3) & (gravity >= 6.5): - print('BB atmosphere') + if verbose: + print('BB atmosphere') return get_bb_atmosphere(temperature=temperature, metallicity=metallicity, gravity=gravity, verbose=verbose) if (temperature < 3.5e3) & (gravity < 6.5) & (gravity > 6): - print('BB atmosphere') + if verbose: + print('BB atmosphere') return get_bb_atmosphere(temperature=temperature, metallicity=metallicity, gravity=gravity, verbose=verbose) - - - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Returns BB if outside of WD defined atmospheres - else: + if gravity > 6: if verbose: print('WD or BB atmosphere') return get_wd_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) + temperature=temperature, + gravity=gravity, + verbose=verbose) + + return get_merged_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + verbose=verbose, + rebin=rebin) def get_merged_atmosphere_w_bb_supplement_grid(bb_supplement_tarr='default', bb_supplement_zarr='default', bb_supplement_loggarr='default', rebin=True): + """ + Return the atmosphere parameter grid used by + get_merged_atmosphere_w_bb_supplement. + This is the standard merged atmosphere grid plus the white dwarf grid and + blackbody supplement points for high-gravity regions. + """ super_tarr, super_zarr, super_loggarr = get_merged_atmosphere_grid(rebin=rebin) wd_tarr, wd_zarr, wd_loggarr = get_wdKoester_atmosphere_grid() @@ -1389,14 +1492,15 @@ def get_merged_atmosphere_w_bb_supplement_grid(bb_supplement_tarr='default', bb_ super_loggarr = np.concatenate((super_loggarr, wd_loggarr)) if bb_supplement_tarr == 'default': - X, Y = np.meshgrid(np.logspace(np.log10(2e3), np.log10(4.6e3), 20), np.linspace(6.5, 8.7, 10)) - X1, Y1 = np.meshgrid(np.logspace(np.log10(2e3), np.log10(3.5e3), 15), np.linspace(6, 6.25, 2)) - #X2, Y2 = np.meshgrid(np.logspace(np.log10(2e2), np.log10(1.1e3), 20), np.linspace(3, 4.5, 4)) - X3, Y3 = np.meshgrid(np.logspace(np.log10(8e3), np.log10(2e4), 25), np.linspace(9.8, 11.6, 8)) - bb_supplement_tarr = np.concatenate((X.ravel(), X1.ravel(), X3.ravel())) - bb_supplement_loggarr = np.concatenate((Y.ravel(), Y1.ravel(), Y3.ravel())) - #bb_supplement_tarr = np.concatenate((X.ravel(), X1.ravel(), X2.ravel(), X3.ravel())) - #bb_supplement_loggarr = np.concatenate((Y.ravel(), Y1.ravel(), Y2.ravel(), Y3.ravel())) + X_highg, Y_highg = np.meshgrid(np.logspace(np.log10(2e3), np.log10(2e4), 35), + np.linspace(9.8, 11.6, 8)) + X_cool_highg, Y_cool_highg = np.meshgrid(np.logspace(np.log10(2e3), np.log10(4.6e3), 20), + np.linspace(6.5, 9.7, 10)) + X_cool_midg, Y_cool_midg = np.meshgrid(np.logspace(np.log10(2e3), np.log10(3.5e3), 15), + np.linspace(6.1, 6.4, 4)) + + bb_supplement_tarr = np.concatenate((X_highg.ravel(), X_cool_highg.ravel(), X_cool_midg.ravel())) + bb_supplement_loggarr = np.concatenate((Y_highg.ravel(), Y_cool_highg.ravel(), Y_cool_midg.ravel())) bb_supplement_zarr = np.zeros(len(bb_supplement_tarr)) @@ -1408,9 +1512,9 @@ def get_merged_atmosphere_w_bb_supplement_grid(bb_supplement_tarr='default', bb_ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are outside of grid, return a blackbody spectrum instead Parameters @@ -1423,7 +1527,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -1439,12 +1543,52 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False return get_wdKoester_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) - + except pysynphot.exceptions.ParameterOutOfBounds: # Use a black-body atmosphere. bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec + +def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): + """ + Return the brown dwarf atmosphere from + `Meisner et al. 2023 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('Meisner2023 atmosphere') + + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, verbose=False, rebin=None, wave_min=500, wave_max=50_000, wave_num=20_000): @@ -1470,29 +1614,29 @@ def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, warnings.warn( 'Only `temperature` keyword is used for black-body atmosphere' ) - + if verbose: print('Black-body atmosphere') - + # Modify pysynphot's default waveset to specified bounds pysynphot.refs.set_default_waveset( minwave=wave_min, maxwave=wave_max, num=wave_num ) - + # Get black-body atmosphere for specified temperature from pysynphot bbspec = pysynphot.spectrum.BlackBody(temperature) - + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` bbspec.convert('flam') - + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. # Need to remove this normalization for SPISEA by multiplying bbspec # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - + return bbspec - + #--------------------------------------# # Atmosphere formatting functions #--------------------------------------# @@ -1508,7 +1652,7 @@ def download_CMFGEN_atmospheres(Table_rot, Table_norot): Fierro+15 paper Website addresses are hardcoded - + Puts downloaded models in the current working directory. """ print( 'WARNING: THIS DOES NOT COMPLETELY WORK') @@ -1577,7 +1721,7 @@ def organize_CMFGEN_atmospheres(path_to_dir): """ # First, record current working directory to return to later start_dir = os.getcwd() - + # Enter atmosphere directory, collect rotating and non-rotating # file names (assumed to all start with "t") os.chdir(path_to_dir) @@ -1602,10 +1746,10 @@ def organize_CMFGEN_atmospheres(path_to_dir): # Also move Tables with model parameters into correct directory os.system('mv Table_rot.txt cmfgenF15_rot') os.system('mv Table_noRot.txt cmfgenF15_noRot') - + # Return to original directory os.chdir(start_dir) - + return def make_CMFGEN_catalog(path_to_dir): @@ -1627,10 +1771,10 @@ def make_CMFGEN_catalog(path_to_dir): """ # Record current working directory for later start_dir = os.getcwd() - + # Enter atmosphere directory os.chdir(path_to_dir) - + # Extract parameters for each atmosphere # Note: can't rely on filename for this because not precise enough!! @@ -1645,7 +1789,7 @@ def make_CMFGEN_catalog(path_to_dir): # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun # mass = float(lumtmp[0][5:-1]) # In M_sun - + # Need to calculate log g from T and L (cgs) # lum_sun = 3.846 * 10**33 # erg/s # M_sun = 2 * 10**33 # g @@ -1673,18 +1817,18 @@ def make_CMFGEN_catalog(path_to_dir): #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# #path = path_to_dir + '/' + names[i] path = names[i] + '.fits[Flux]' - + index_str.append(index) name_str.append(path) - + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits') - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): @@ -1720,23 +1864,23 @@ def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): unique = np.unique(wave, return_index=True) wave = wave[unique[1]] flux = flux[unique[1]] - - # Make fits table from individual columns. + + # Make fits table from individual columns. c0 = fits.Column(name='Wavelength', format='D', array=wave) c1 = fits.Column(name='Flux', format='E', array=flux) cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) - #Adding unit keywords + #Adding unit keywords tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' prihdu = fits.PrimaryHDU() - + finalhdu = fits.HDUList([prihdu, tbhdu]) finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) # Return to original directory, copy over new .fits files to cdbs directory @@ -1753,7 +1897,7 @@ def rebin_cmfgen(cdbs_path, rot=True): cdbs_path: path to cdbs directory rot=True for rotating models (cmfgen_rot), False for non-rotating models - + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin """ # Get an atlas ck04 model, we will use this to set wavelength grid @@ -1769,7 +1913,7 @@ def rebin_cmfgen(cdbs_path, rot=True): tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' path = cdbs_path+'/grid/cmfgen_norot_rebin/' orig_path = cdbs_path+'/grid/cmfgen_norot/' - + cmfgen_hdu = fits.open(tmp) header0 = cmfgen_hdu[0].header # Create rebin directories if they don't already exist. Copy over @@ -1784,7 +1928,7 @@ def rebin_cmfgen(cdbs_path, rot=True): files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) # For each catalog.fits entry, read the unbinned spectrum and rebin to # the atlas resolution. Make a new fits file in rebin directory @@ -1796,16 +1940,16 @@ def rebin_cmfgen(cdbs_path, rot=True): temp = float(vals[0]) metal = float(vals[1]) grav = float(vals[2]) - + # Fetch the spectrum - if rot == True: + if rot == True: sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) else: sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) # Rebin flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) # Make the FITS file from the columns with header cols = fits.ColDefs([c0,c1]) @@ -1830,7 +1974,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): path_to_dir is the path to the directory containing all of the downloaded files - + met_str is the name of the current metallicity Creates new fits files for each atmosphere: phoenix_.fits, @@ -1847,7 +1991,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): pass else: os.mkdir(sub_dir) - + # Extract wavelength array, make column for later wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') wave = wavefile[0].data @@ -1870,7 +2014,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): for f in files: # Extract the logg out of filename logg = f[9:13] - + # Extract fluxes from file spectrum = fits.open(f) flux = spectrum[0].data @@ -1879,11 +2023,11 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): # Make Column object with fluxes, add to table col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) t.add_column(col) - + # Now, construct final fits file for the given temp outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + # Progress counter for user i += 1 print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) @@ -1900,18 +2044,18 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): path_to_directory is the path to the directory with the reformatted models (i.e. the output from construct_atmospheres, phoenix[met_str]) - + Puts catalog.fits file in directory the user starts in """ # Save starting directory for later, move into working directory start_dir = os.getcwd() os.chdir(path_to_dir) - + # Extract metallicity from metallicity string met = float(met_str[1]) + (float(met_str[2]) * 0.1) if 'm' in met_str: met *= -1. - + # Collect the filenames. Each is a unique temp with many different log g's files = glob.glob('phoenix*.fits') files.sort() @@ -1924,7 +2068,7 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): t = Table.read(i, format='fits') keys = t.keys() logg_vals = keys[1:] - + # Extract temp from filename name = i.split('_') temp = float(name[1][:-5]) @@ -1937,20 +2081,20 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): filename_arr.append(filename) catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - + # Return to starting directory, write catalog os.chdir(start_dir) - + if os.path.exists('catalog.fits'): from astropy.table import vstack - + prev_catalog = Table.read('catalog.fits', format='fits') joined_catalog = vstack([prev_catalog, catalog]) - + joined_catalog.write('catalog.fits', format='fits', overwrite=True) else: catalog.write('catalog.fits', format='fits', overwrite=True) - + return def cdbs_PHOENIXv16(path_to_cdbs_dir): @@ -1971,7 +2115,7 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir): # Collect the filenames, make necessary changes to each one files = glob.glob('phoenix*.fits') - + ## Need to sort filenames; glob doesn't always give them in order files.sort() @@ -1979,28 +2123,28 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir): counter = 0 for i in files: counter += 1 - + # Read in current FITS table cur_table = Table.read(i, format='fits') - + cur_table.columns[0].name = 'Wavelength' - + num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion + + # Multiplying each flux column by 10^-8 for conversion for cur_col_index in range(1, num_cols, 1): cur_col_name = cur_table.colnames[cur_col_index] cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - + + # Construct new FITS file based on old one hdu = fits.open(i) header_0 = hdu[0].header header_1 = hdu[1].header sci = hdu[1].data - + tbhdu = fits.table_to_hdu(cur_table) - + # Copying over the older headers, adding unit keywords prihdu = fits.PrimaryHDU(header=header_0) tbhdu.header['TUNIT1'] = 'ANGSTROM' @@ -2017,17 +2161,17 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir): tbhdu.header['TUNIT12'] = 'FLAM' tbhdu.header['TUNIT13'] = 'FLAM' tbhdu.header['TUNIT14'] = 'FLAM' - + # Construct and write out final FITS file finalhdu = fits.HDUList([prihdu, tbhdu]) finalhdu.writeto(i, overwrite=True) - + hdu.close() print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - + # Change back to starting directory os.chdir(start_dir) - + return def rebin_phoenixV16(cdbs_path): @@ -2052,7 +2196,7 @@ def rebin_phoenixV16(cdbs_path): path = cdbs_path+'/grid/phoenix_v16_rebin/' if not os.path.exists(path): os.mkdir(path) - + # Read in the existing catalog.fits file and rebin every spectrum. cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') @@ -2067,51 +2211,51 @@ def rebin_phoenixV16(cdbs_path): temp_arr[ff] = float(vals[0]) metal_arr[ff] = float(vals[1]) logg_arr[ff] = float(vals[2]) - + metal_uniq = np.unique(metal_arr) temp_uniq = np.unique(temp_arr) - + for mm in range(len(metal_uniq)): metal = metal_uniq[mm] # metallicity - + # Construct str for metallicity (for appropriate directory name) met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) if metal > 0: met_str = 'p' + met_str else: met_str = 'm' + met_str - + # Make directory for current metallicity if it does not exist yet if not os.path.exists(path + 'phoenix' + met_str): os.mkdir(path + 'phoenix' + met_str) - + for tt in range(len(temp_uniq)): temp = temp_uniq[tt] # temperature - # Pick out the list of gravities for this T, Z combo + # Pick out the list of gravities for this T, Z combo idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] logg_exist = logg_arr[idx] - + # All gravities will go in one file. Here is the output # file name. outfile = path + files_all[idx[0]].split('[')[0] - + ## If the rebinned file already exists, continue if os.path.exists(outfile): continue - + # Build a columns array. One column for each gravity. cols_arr = [] # Make the wavelength column, which is first in the cols array. c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) cols_arr.append(c0) - + for gg in range(len(logg_exist)): grav = logg_exist[gg] # gravity - # Fetch the spectrum + # Fetch the spectrum sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) @@ -2119,7 +2263,7 @@ def rebin_phoenixV16(cdbs_path): name = 'g{0:3.1f}'.format(grav) col = fits.Column(name=name, format='E', array=flux_rebin) cols_arr.append(col) - + # Make the FITS file from the columns with header. cols = fits.ColDefs(cols_arr) @@ -2135,7 +2279,7 @@ def rebin_phoenixV16(cdbs_path): finalhdu.writeto(outfile) print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - + return @@ -2150,7 +2294,7 @@ def rebin_spec(wave, specin, wavnew): f = np.ones(len(wave)) filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - + return obs.binflux def organize_BTSettl_2015_atmospheres(path_to_dir): @@ -2181,7 +2325,7 @@ def organize_BTSettl_2015_atmospheres(path_to_dir): spec = hdu[1].data header_0 = hdu[0].header header_1 = hdu[1].header - + wave = spec.field(0) flux = spec.field(1) @@ -2202,13 +2346,13 @@ def organize_BTSettl_2015_atmospheres(path_to_dir): tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' hdu_new = fits.HDUList([prihdu, tbhdu]) - + # Write new fits table in cdbs directory hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) hdu.close() hdu_new.close() - + # Return to original directory os.chdir(start_dir) return @@ -2225,10 +2369,10 @@ def make_BTSettl_2015_catalog(path_to_dir): """ # Record current working directory for later start_dir = os.getcwd() - + # Enter atmosphere directory os.chdir(path_to_dir) - + # Extract parameters for each atmosphere from the filename, # construct columns for catalog file files = glob.glob("*spec.fits") @@ -2247,10 +2391,10 @@ def make_BTSettl_2015_catalog(path_to_dir): # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits', overwrite=True) - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): @@ -2293,8 +2437,8 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): # Make new output c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) prihdu = fits.PrimaryHDU(header=header0) @@ -2303,7 +2447,7 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): outfile = path + files_all[ff].split('[')[0] finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) + finalhdu.writeto(outfile, overwrite=True) return @@ -2322,7 +2466,7 @@ def make_wavelength_unique(files, dirname): if len(t) != len(test[0]): t = t[test[1]] - + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) c1 = fits.Column(name='Flux', format='E', array=t['Flux']) cols = fits.ColDefs([c0, c1]) @@ -2360,14 +2504,14 @@ def organize_BTSettl_atmospheres(): """ Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online + individual model files have been downloaded from online (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. + and processed into python-readable ascii files. """ orig_dir = os.getcwd() dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - + # Go through each directory, turning each spectrum into a cdbs-ready file. # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, @@ -2399,13 +2543,13 @@ def organize_BTSettl_atmospheres(): tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' hdu_new = fits.HDUList([prihdu, tbhdu]) - + # Write new fits table in cdbs directory hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) hdu_new.close() count += 1 print('Done {0} of {1}'.format(count, len(files))) - + # Now, clean up all the files made when unzipping the spectra cmd1 = 'rm *.bz2' cmd2 = 'rm *.tmp' @@ -2416,7 +2560,7 @@ def organize_BTSettl_atmospheres(): print('==============================') print('Done {0}'.format(ii)) print('==============================') - + # Go back to original directory, move to next metallicity directory os.chdir(orig_dir) @@ -2452,7 +2596,7 @@ def make_BTSettl_catalog(): metal_flag = -1 * float(ii[3:])*0.1 else: metal_flag = float(ii[3:])*0.1 - + # Now collect the info from the files for jj in files: tmp = jj.split('-') @@ -2466,7 +2610,7 @@ def make_BTSettl_catalog(): else: temp = float(tmp[0][3:]) * 100.0 # In kelvin logg = float(tmp[1]) - + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) name_str.append('{0}/{1}[Flux]'.format(ii, jj)) @@ -2479,10 +2623,10 @@ def make_BTSettl_catalog(): # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits', overwrite=True) - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def rebin_BTSettl(make_unique=False): @@ -2513,7 +2657,7 @@ def rebin_BTSettl(make_unique=False): # tmp.append(ii) #files_all = tmp #=============================# - + print( 'Rebinning BTSettl spectra') if make_unique: print('Making unique') @@ -2533,14 +2677,14 @@ def rebin_BTSettl(make_unique=False): # Make new output c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) prihdu = fits.PrimaryHDU() tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' - + outfile = path + files_all[ff].split('[')[0] finalhdu = fits.HDUList([prihdu, tbhdu]) finalhdu.writeto(outfile, overwrite=True) @@ -2550,11 +2694,188 @@ def rebin_BTSettl(make_unique=False): outfile = path + files_all[ff].split('[')[0] cmd = 'cp {0} {1}'.format(orig_file, outfile) os.system(cmd) - + print('Done {0} of {1}'.format(ff, len(files_all))) - + + return + +def organize_all_Meisner2023_atmospheres(): + """ + Construct cdbs-ready atmospheres for the Meisner2023 grid. + The code expects tp be run in cdbs/grid/Meisner2023, and expects that the + individual model files have been downloaded from online + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Save as a fits file, for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.fits') + count=0 + for jj in files: + # Open each .fits file and read the data + with fits.open(jj) as hdul: + data = hdul[1].data + wavelength = data['Wavelength'] + flux = data['Flux'] + + # Make flux independent of R&D + flux_new = flux / 5e-20 + + # Create new columns with desired format + c0 = fits.Column(name='Wavelength', format='D', array=wavelength) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write the new fits table in the cdbs directory + output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension + hdu_new.writeto(output_filename, overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + return +def make_Meisner2023_catalog(): + """ + Create cdbs catalog.fits of Meisner2023 grid. + THIS IS STEP 2, after organize_Meisner2023_atmospheres has + been run. + + Code expects to be run in cdbs/grid/Meisner2023 + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('spec_jwst_*.fits') + + for jj in files: + # Parse temperature, log(g), and metallicity from filename + temp_str = jj.split('_')[2] + logg_str = jj.split('_')[3] + metal_str = jj.split('_')[4] + + # Extract temperature, surface gravity, and metallicity + temp = float(temp_str[1:]) # Temperature in Kelvin + logg = float(logg_str[1:]) # Surface gravity log(g) + + # Build metallicity value + if metal_str.startswith('m'): + metallicity = -1 * float(metal_str[1:]) + else: + metallicity = float(metal_str[1:]) + + # Construct index and filename strings + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + print('Processed directory:', ii) + os.chdir(start_dir) + + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_Meisner2023(make_unique=False): + """ + Rebin Meisner2023 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: Meisner2023_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create a directory for rebinned Meisner2023 models + rebin_path = 'Meisner2023_rebin/' + if not os.path.exists(rebin_path): + os.mkdir(rebin_path) + + # Load the catalog.fits file and extract all spectra file paths + cat = Table.read('Meisner2023/catalog.fits') + files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] + + print('Rebinning Meisner2023 spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'Meisner2023') + print('Done') + + for ff, file in enumerate(files_all): + vals = cat[ff]['INDEX'].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the Meisner2023 spectrum and rebin its flux + try: + sp = pysynphot.Icat('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Create the output FITS file + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write the new rebinned file in the Meisner2023_rebin directory + outfile = os.path.join(rebin_path, os.path.basename(file)) + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + except Exception as e: + print(f"Error processing {file}: {e}") + orig_file = os.path.join('Meisner2023', file) + outfile = os.path.join(rebin_path, os.path.basename(file)) + os.system(f'cp {orig_file} {outfile}') + + print('Done {0} of {1}'.format(ff + 1, len(files_all))) + + return + + + def organize_WDKoester_atmospheres(path_to_dir): """ Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) @@ -2576,7 +2897,7 @@ def organize_WDKoester_atmospheres(path_to_dir): for i in files: data = Table.read(i, format='ascii') - + wave = data['col1'] # angstrom flux = data['col2'] # erg/s/cm^2/A @@ -2592,12 +2913,12 @@ def organize_WDKoester_atmospheres(path_to_dir): tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' hdu_new = fits.HDUList([prihdu, tbhdu]) - + # Write new fits table in cdbs directory hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) hdu_new.close() - + # Return to original directory os.chdir(start_dir) return @@ -2614,10 +2935,10 @@ def make_WDKoester_catalog(path_to_dir): """ # Record current working directory for later start_dir = os.getcwd() - + # Enter atmosphere directory os.chdir(path_to_dir) - + # Extract parameters for each atmosphere from the filename, # construct columns for catalog file files = glob.glob("*dk.dat.fits") @@ -2637,10 +2958,10 @@ def make_WDKoester_catalog(path_to_dir): # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits', overwrite=True) - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): @@ -2683,8 +3004,8 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): # Make new output c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) prihdu = fits.PrimaryHDU(header=header0) @@ -2693,8 +3014,8 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): outfile = path + files_all[ff].split('[')[0] finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) + finalhdu.writeto(outfile, overwrite=True) return - - + + diff --git a/spisea/conftest.py b/spisea/conftest.py index 142ae511..fab9a899 100755 --- a/spisea/conftest.py +++ b/spisea/conftest.py @@ -18,10 +18,10 @@ ASTROPY_HEADER = True except ImportError: ASTROPY_HEADER = False - + else: # As of Astropy 5.1, the pytest plugins provided by Astropy have been removed - # and are instead provided by pytest-astropy-header + # and are instead provided by pytest-astropy-header # (https://github.com/astropy/pytest-astropy-header) from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS ASTROPY_HEADER = True diff --git a/spisea/evolution.py b/spisea/evolution.py index b59ca95f..6ffe6e99 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -4,12 +4,14 @@ import numpy as np import os import glob +import pandas as pd import pdb import warnings from astropy.table import Table, vstack, Column from scipy import interpolate import pylab as py from spisea.utils import objects +from scipy.interpolate import RegularGridInterpolator from spisea import exceptions import astropy.units as u import astropy.constants as c @@ -33,7 +35,7 @@ def get_installed_grid_num(input_models_dir): """ # Define the installed model grid number file_name = input_models_dir + '/grid_version.txt' - + # Read in the file. In the case where it doesn't # exist, then grid version is assumed to be 1.0 # (since this didn't always exist) @@ -54,15 +56,15 @@ def check_evo_grid_number(required_num, input_models_dir): grid version number. Installed grid number must be greater than or equal to this number """ - + # Get installed gridnumber grid_num = get_installed_grid_num(input_models_dir) - + # Check: is installed grid number < required_num? # If not, raise mismatch error if grid_num < required_num: raise exceptions.ModelMismatch(required_num, grid_num, 'evolution') - + return grid_num class StellarEvolution(object): @@ -94,9 +96,9 @@ def __init__(self, model_dir, age_list, mass_list, z_list): self.age_list = age_list self.external_evol = False self.model_version_name = "None" - + return - + class Geneva(StellarEvolution): def __init__(self): r""" @@ -105,20 +107,20 @@ def __init__(self): self.model_version_name = "Geneva" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.01, 0.02, 0.03] - + # populate list of isochrone ages (log scale) age_list = [round(5.5 + 0.01*i, 2) for i in range(190)] age_list += [round(7.4 + 0.05*i, 2) for i in range(12)] age_list += [round(math.log10(1.e8*x), 2) for x in range(1, 10)] age_list += [round(math.log10(1.e9*x), 2) for x in range(1, 10)] age_list = age_list - - # specify location of model files - model_dir = models_dir + 'geneva/' + # specify location of model files + self.model_dir = models_dir + 'geneva/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.02 @@ -126,7 +128,7 @@ def __init__(self): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Geneva collection. @@ -135,31 +137,31 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # convert age (in yrs) to log scale and find nearest value in grid log_age = np.log10(age) age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_' + str(self.age_list[age_idx]) + '.fits' - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # return isochrone data return genfromtxt(full_iso_file, comments='#') @@ -170,7 +172,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): class Ekstrom12(StellarEvolution): """ - Evolution models from + Evolution models from `Ekstrom et al. 2012 `_. Downloaded from `website `_. @@ -187,10 +189,10 @@ def __init__(self, rot=True): self.model_version_name = "Ekstrom12-norot" # define metallicity parameters for Ekstrom+12 models self.z_list = [0.014] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.0+0.005, 0.01) - + # Specify location of model files self.model_dir = models_dir+'Ekstrom2012/' @@ -203,7 +205,7 @@ def __init__(self, rot=True): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Ekstrom+12 Geneva collection. @@ -212,34 +214,34 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity z_solar * 10^{metallicity} = {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string - if self.rot: + if self.rot: full_iso_file = self.model_dir + 'iso/' + z_dir + 'rot/' + iso_file else: full_iso_file = self.model_dir + 'iso/' + z_dir + 'norot/' + iso_file - + # Return isochrone data iso = Table.read(full_iso_file, format='fits') iso.rename_column('col4', 'Z') @@ -271,11 +273,11 @@ def format_isochrones(input_iso_dir): Parse iso.fits (filename hardcoded) file downloaded from Ekstrom+12 models, create individual isochrone files for the different ages. - input_iso_directory should lead to - Ekstrom2012/iso/ + input_iso_directory should lead to + Ekstrom2012/iso/ directory, where iso.fits file should be located. - Creates two new directories, rot and norot, which contain their + Creates two new directories, rot and norot, which contain their respective isochrones. """ # Store current directory for later @@ -283,13 +285,13 @@ def format_isochrones(input_iso_dir): # Move into metallicity direcotry, read iso.fits file os.chdir(input_iso_dir) - + print( 'Read Input: this is slow') iso = Table.read('iso.fits') print( 'Done' ) - + ages_all = iso['col1'] - + # Extract the unique ages age_arr = np.unique(ages_all) @@ -303,7 +305,7 @@ def format_isochrones(input_iso_dir): else: os.mkdir('rot') os.mkdir('norot') - + print( 'Making individual isochrone files') for age in age_arr: good = np.where(ages_all == age) @@ -314,7 +316,7 @@ def format_isochrones(input_iso_dir): tmp_r = iso[good][idx_r] tmp_n = iso[good][idx_n] - + # Write tables tmp_r.write('rot/iso_{0:4.2f}.fits'.format(age)) tmp_n.write('norot/iso_{0:4.2f}.fits'.format(age)) @@ -331,14 +333,14 @@ def create_iso(fileList, ageList, rot=True): iso.fits format for parse_iso code. fileList: list of downloaded isochrone files (could be one) - + ageList: list of lists of ages associated with each file in filelist. MUST BE IN SAME ORDER AS ISOCHRONES IN FILE! Also needs to be in logAge - + rot = TRUE: assumes that models are rotating, will add appropriate column - + This code writes the individual files, which is then easiest to combine by hand - in aquamacs + in aquamacs """ # Read each file in fileList individually, add necessary columns for i in range(len(fileList)): @@ -349,14 +351,14 @@ def create_iso(fileList, ageList, rot=True): start = np.where(t['M_ini'] == 0.8) # Now, each identified start is assumed to be associated with the - # corresponding age in ages + # corresponding age in ages if len(start[0]) != len(ages): print( 'Ages mismatched in file! Quitting...') return age_arr = np.zeros(len(t)) - + for j in range(len(start[0])): low_ind = start[0][j] # Deal with case at end of file @@ -375,9 +377,9 @@ def create_iso(fileList, ageList, rot=True): rot_val[:] = 'r' if not rot: rot_val[:] = 'n' - + col_rot = Column(rot_val, name='Rot') - + t.add_column(col_rot, index=0) t.add_column(col_age, index=0) @@ -391,7 +393,7 @@ def create_iso(fileList, ageList, rot=True): class Parsec(StellarEvolution): """ - Evolution models from + Evolution models from `Bressan et al. 2012 `_, version 1.2s. @@ -417,14 +419,14 @@ def __init__(self): # populate list of model masses (in solar masses) self.model_version_name = "Parsec1.2s" #mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Parsec models self.z_list = [0.005, 0.015, 0.04] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.6, 10.12+0.005, 0.01) self.age_list = np.append(6.40, self.age_list) - + # Specify location of model files self.model_dir = models_dir+'ParsecV1.2s/' @@ -433,8 +435,8 @@ def __init__(self): self.z_file_map = {0.005: 'z005/', 0.015: 'z015/', 0.04: 'z04/'} # Define required evo_grid number - self.evo_grid_min = 1.0 - + self.evo_grid_min = 1.0 + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Parsec version 1.2s @@ -444,31 +446,31 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # return isochrone data iso = Table.read(full_iso_file, format='fits') iso.rename_column('col1', 'Z') @@ -484,19 +486,19 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Parsec doesn't identify WR stars, so identify all as "False" isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) return iso - + def format_isochrones(input_iso_dir, metallicity_list): r""" Parse isochrone file downloaded from Parsec version 1.2 for different metallicities, create individual isochrone files for the different ages. - + input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity subdirectories already exist with isochrone files downloaded in them (isochrones files expected to start with "output*") @@ -507,6 +509,151 @@ def format_isochrones(input_iso_dir, metallicity_list): # Store current directory for later start_dir = os.getcwd() + # Move into isochrone directory + os.chdir(input_iso_dir) + + # Work on each metallicity isochrones individually + for metal in metallicity_list: + # More into metallicity directory, read isochrone file + os.chdir(metal) + + isoFile = glob.glob('output*') + print( 'Read Input: this is slow') + iso = Table.read(isoFile[0], format='fits') + print( 'Done') + + ages_all = iso['col2'] + + # Extract the unique ages + age_arr = np.unique(ages_all) + + # For each unique age, extract the proper rows and make corresponding + # table + print( 'Making individual isochrone files') + for age in age_arr: + good = np.where(ages_all == age) + tmp = iso[good] + + #Write table + tmp.write('iso_{0:4.2f}.fits'.format(age)) + + # Move back into iso directory + os.chdir('..') + + # Return to starting directory + os.chdir(start_dir) + return + +class Phillips2020(StellarEvolution): + """ + Evolution models from + `Phillips et al. 2020 `_. + + Downloaded from `here _` + + Notes + ----- + Evolution model parameters used in download: + + * Assume chemical equilibrium + * Solar metallicity + * For young BDs + CHANGE!!! + """ + + def __init__(self): + r""" + Define intrinsic properties for the Phillips brown dwarf stellar models + """ + # specify location of model files + self.model_dir = models_dir + 'Phillips2020/' + + # specifying metallicity + self.z_list = [0.015] + self.z_file_map = {0.015: 'z00'} + self.z_solar = 0.015 + + # populate list of isochrone ages (log scale) + self.age_list = np.array([6.0, 6.102564101374986, 6.205128204112327, 6.307692307498837, + 6.41025641041525, 6.512820513399242, 6.615384615079123, 6.717948717593952, + 6.820512820432055, 6.923076923041517,7.025641027630915, 7.128205127364955, + 7.230769230806862, 7.333333333326906, 7.43589743645667, 7.538461537884313, + 7.64102564151456, 7.743589743588999, 7.846153846357851, 7.948717948583871, + 8.051282050278353, 8.153846153753816, 8.256410257173776, 8.35897435818861, + 8.461538460830697, 8.564102564448598, 8.666666666328634, 8.769230769062688, + 8.87179487160516, 8.974358974304664, 9.076923076299368, 9.179487179310316, + 9.282051282920115, 9.384615385138028, 9.487179487213739, 9.589743589709178, + 9.692307692157154, 9.794871794783068, 9.89743589751841, 10.0]) + + # define required evo_grid number + self.evo_grid_min = 3.0 + + def isochrone(self, age= 1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Phillips2020 collection. + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): + logger.error('Requested age {0} is out of bounds.'.format(log_age)) + + if ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # Specify subdirectory for metallicity + iso_path = os.path.join(self.model_dir, 'iso', z_dir) + + # Find nearest age in grid to input grid by parsing through available files + close_age = self.age_list[np.argmin(abs(self.age_list - log_age))] + close_file = iso_path+f'/iso_{close_age:.15f}.fits' + print(f"Found nearest age {close_age} for requested age of {log_age}") + + # Make sure the closest file exists + if not os.path.exists(close_file): + raise FileNotFoundError(f"Isochrone file not found: {close_file}.") + + # return isochrone data + iso = Table.read(close_file, format='fits') + iso.rename_column('Z', 'Z') + iso.rename_column('Age', 'logAge') + iso.rename_column('Mass', 'mass') + iso.rename_column('Mass_current', 'mass_current') + iso.rename_column('Luminosity', 'logL') + iso.rename_column('Teff', 'logT') + iso.rename_column('Gravity', 'logg') + iso['logT_WR'] = iso['logT'] + + # Phillips doesn't identify WR stars, so identify all as "False" + isWR = Column([False] * len(iso), name='isWR') + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = metallicity + + return iso + + def format_isochrones(input_iso_dir, metallicity_list): + r""" + Change + """ + # Store current directory for later + start_dir = os.getcwd() + # Move into isochrone directory os.chdir(input_iso_dir) @@ -541,6 +688,160 @@ def format_isochrones(input_iso_dir, metallicity_list): # Return to starting directory os.chdir(start_dir) return + + +class Marley2021(StellarEvolution): + """ + Evolution models from + `Marley et al. 2021 `_. + + Downloaded from `here _` + + Notes + ----- + Evolution model parameters used in download: + + * + CHANGE!!! + """ + def __init__(self): + r""" + Define intrinsic properties for the Marley brown dwarf stellar models. + """ + # populate list of model masses (in solar masses) + #mass_list = [(0.1 + i*0.005) for i in range(181)] + + # define metallicity parameters for Parsec models + self.z_solar = 0.0142 + self.z_list = [self.z_solar * (10.**m) for m in [-0.5, 0.0, 0.5]] + + # populate list of isochrone ages (log scale) + self.age_list = [10.0, 7.0, 8.0, 9.0, 6.0, 7.176091259055681, 8.176091259055681, 9.176091259055681, 6.301029995663981, + 7.301029995663981, 8.301029995663981, 9.301029995663981, 6.477121254719663, 7.477121254719663, + 8.477121254719663, 9.477121254719663, 6.6020599913279625, 7.6020599913279625, 8.602059991327963, + 9.602059991327963, 6.778151250383644, 7.778151250383644, 8.778151250383644, 9.778151250383644, + 6.903089986991944, 7.903089986991944, 8.903089986991944, 9.903089986991944] + + # Specify location of model files + self.model_dir = models_dir+'Marley2021/' + + # Specifying metallicity + self.z_file_map = { + self.z_list[0]: 'zm05/', + self.z_list[1]: 'zp00/', + self.z_list[2]: 'zp05/' + } + + # Define required evo_grid number + self.evo_grid_min = 3.0 + + def isochrone(self, age=1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Marley2021 collection. + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): + logger.error('Requested age {0} is out of bounds.'.format(log_age)) + + if ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # Find closest age in grid + age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] + iso_file = f'iso_{self.age_list[age_idx]}.fits' + + # Create path to iso file + full_iso_file = os.path.join(self.model_dir, 'iso', z_dir, iso_file) + + print(f"Found nearest age file as {full_iso_file} for requested age of {log_age}") + + # Make sure the closest file exists + #if not os.path.exists(close_file): + #raise FileNotFoundError(f"Isochrone file not found: {close_file}.") + + # return isochrone data + iso = Table.read(full_iso_file, format='fits') + iso.rename_column('Z', 'Z') + iso.rename_column('Age', 'logAge') + iso.rename_column('Mass', 'mass') + iso.rename_column('Mass_current', 'mass_current') + iso.rename_column('log_L', 'logL') + iso.rename_column('Teff', 'logT') + iso.rename_column('logg', 'logg') + iso.rename_column('Radius', 'radius') + iso['logT_WR'] = iso['logT'] + + # Marley doesn't identify WR stars, so identify all as "False" + isWR = Column([False] * len(iso), name='isWR') + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(z_defined / self.z_solar) + + return iso + + def format_isochrones(input_iso_dir, metallicity_list): + r""" + Parse isochrone files downloaded from Marley 2021 for different + metallicities, create individual isochrone files for the different ages. + + input_iso_dir: points to Marley2021/iso directory. Assumes metallicity + subdirectories already exist with isochrone files downloaded in them + (isochrones files expected to start with "output*") + + """ + # Store current directory for later + start_dir = os.getcwd() + + # Move into isochrone directory + os.chdir(input_iso_dir) + + # Work on each metallicity isochrones individually + for metal in metallicity_list: + # More into metallicity directory, read isochrone file + os.chdir(metal) + + isoFile = glob.glob('output*') + print( 'Read Input: this is slow') + iso = Table.read(isoFile[0], format='fits') + print( 'Done') + + ages_all = iso['Age'] + + # Extract the unique ages + age_arr = np.unique(ages_all) + + # For each unique age, extract the proper rows and make corresponding + # table + print( 'Making individual isochrone files') + for age in age_arr: + good = np.where(ages_all == age) + tmp = iso[good] + + #Write table + tmp.write('iso_{0:4.2f}.fits'.format(age)) + + # Move back into iso directory + os.chdir('..') + + # Return to starting directory + os.chdir(start_dir) + return #---------------------------------------# # Now for the Pisa (Tognelli+11) models @@ -548,9 +849,9 @@ def format_isochrones(input_iso_dir, metallicity_list): class Pisa(StellarEvolution): """ - Evolution models from + Evolution models from `Tognelli et al. 2011 `_. - + Downloaded `online `_ Notes @@ -569,10 +870,10 @@ def __init__(self): self.model_version_name = "Pisa" # define metallicity parameters for Pisa models self.z_list = [0.015] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.01+0.005, 0.01) - + # Specify location of model files self.model_dir = models_dir+'Pisa2011/' @@ -582,7 +883,7 @@ def __init__(self): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Pisa (Tognelli+11) @@ -592,31 +893,31 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + return if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds for evolution model. Available z-vals: {1}.'.format(z_defined, self.z_list)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds for evolution model. Available z-vals: {self.z_list}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # return isochrone data iso = Table.read(full_iso_file, format='fits') iso.rename_column('col1', 'logL') @@ -629,7 +930,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) - # Add columns for current mass and phase. + # Add columns for current mass and phase. iso.add_column( Column(np.zeros(len(iso)), name = 'phase')) iso.add_column( Column(iso['mass'], name = 'mass_current')) @@ -647,7 +948,7 @@ def format_isochrones(input_iso_dir, metallicity_list): input_iso_dir: points to Pisa2011/iso directory. Individual metallicity directories with the downloaded isochrones are expected to already exist there - + metallicity_list is the list of metallicities on which function is to be run. @@ -669,7 +970,7 @@ def format_isochrones(input_iso_dir, metallicity_list): else: # Create a ReadMe with the original file names to preserve the # model details - + cmd = "ls *.FITS > ReadMe" os.system(cmd) @@ -703,14 +1004,14 @@ def make_isochrone_grid(metallicity=0.015): while 0.0150 would not) """ logAge_arr = np.arange(6.0, 8.0+0.005, 0.01) - + count = 0 for logAge in logAge_arr: # Could interpolate using evolutionary tracks, but less accurate. make_isochrone_pisa_interp(logAge, metallicity=metallicity) count += 1 - + print( 'Done {0} of {1} models'.format(count, (len(logAge_arr)))) return @@ -720,7 +1021,7 @@ def make_isochrone_grid(metallicity=0.015): #==============================# class Baraffe15(StellarEvolution): """ - Evolution models published in + Evolution models published in `Baraffe et al. 2015 `_. Downloaded from `BHAC15 site `_. @@ -729,10 +1030,10 @@ def __init__(self): self.model_version_name = "Baraffe15" # define metallicity parameters for Baraffe models self.z_list = [0.015] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.0+0.005, 0.01) - + # Specify location of model files self.model_dir = models_dir+'Baraffe15/' @@ -742,7 +1043,7 @@ def __init__(self): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=5.e7, metallicity=0.0): r""" Extract an individual isochrone from the Baraffe+15 @@ -752,43 +1053,43 @@ def isochrone(self, age=5.e7, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # Read isochrone, get in proper format iso = Table.read(full_iso_file, format='fits') iso.rename_column('Mass', 'mass') iso.rename_column('logG', 'logg') iso['logT'] = np.log10(iso['Teff']) - + # Pisa models are too low for WR phase, add WR column with all False iso['logT_WR'] = iso['logT'] isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) - # Add columns for current mass and phase. + # Add columns for current mass and phase. iso.add_column( Column(np.zeros(len(iso)), name = 'phase')) iso.add_column( Column(iso['mass'], name = 'mass_current')) @@ -802,11 +1103,11 @@ def tracks_to_isochrones(self, tracksFile): r""" Create isochrones at desired age sampling (6.0 < logAge < 8.0, steps of 0.01; hardcoded) from the Baraffe+15 tracks downloaded - online. + online. tracksFile: tracks.dat file downloaded from Baraffe+15, with format modified to be read in python - + Writes isochrones in iso/ subdirectory off of work directory. Will create this subdirectory if it doesn't already exist """ @@ -814,7 +1115,7 @@ def tracks_to_isochrones(self, tracksFile): age_arr = np.arange(6.0, 8.0+0.005, 0.01) #age_arr = [6.28] - + # Loop through the masses, interpolating track over time at each. # Resample track properties at hardcoded ages masses = np.unique(tracks['col1']) @@ -839,13 +1140,13 @@ def tracks_to_isochrones(self, tracksFile): # Interpolate Teff, logL, and logG using linear interpolator tck_Teff = interpolate.interp1d(tmp['col2'], tmp['col3']) tck_logL = interpolate.interp1d(tmp['col2'], tmp['col4']) - tck_logG = interpolate.interp1d(tmp['col2'], tmp['col5']) + tck_logG = interpolate.interp1d(tmp['col2'], tmp['col5']) Teff = tck_Teff(age_arr) logL = tck_logL(age_arr) logG = tck_logG(age_arr) - + # Test interpolation if desired test=False if test: @@ -872,9 +1173,9 @@ def tracks_to_isochrones(self, tracksFile): py.xlabel('logAge') py.ylabel('logG') py.savefig('test_logG.png') - + pdb.set_trace() - + # Build upon arrays of interpolated values mass_interp = np.concatenate((mass_interp, np.ones(len(Teff)) * mass)) age_interp = np.concatenate((age_interp, age_arr)) @@ -909,11 +1210,11 @@ def tracks_to_isochrones(self, tracksFile): def test_age_interp(self, onlineIso, interpIso): r""" Compare one of our interpolated ischrones with one - of the isochrones provided online by Baraffe+15. + of the isochrones provided online by Baraffe+15. """ true_iso = Table.read(onlineIso, format='ascii') our_iso = Table.read(interpIso, format='fits') - + # Compare the two isochrones using plots. Look at mass vs. Teff, # mass vs. logG, mass vs. logL. Ideally these isochrones should # be identical @@ -945,7 +1246,7 @@ def test_age_interp(self, onlineIso, interpIso): Teff_diff = np.mean(abs(true_iso['col2'][7:] - our_iso['Teff'])) logL_diff = np.mean(abs(true_iso['col3'][7:] - our_iso['logL'])) logG_diff = np.mean(abs(true_iso['col4'][7:] - our_iso['logG'])) - + print( 'Average abs difference in Teff: {0}'.format(Teff_diff)) print( 'Average abs difference in logL: {0}'.format(logL_diff)) print( 'Average abs difference in logg: {0}'.format(logG_diff)) @@ -962,7 +1263,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): name = BaraffeIso.split('_') age = name[1][:4] - + # Extract paramters we need b_mass = b['Mass'] b_logT = np.log10(b['Teff']) @@ -976,7 +1277,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): m05_b = np.where( abs(b_mass - 0.5) == min(abs(b_mass - 0.5)) ) m05_p = np.where( abs(p_mass - 0.5) == min(abs(p_mass - 0.5)) ) - + # Comparison plots py.figure(1, figsize=(10,10)) py.clf() @@ -1004,7 +1305,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): #py.axis([4.4, 3.4, -3, 4]) #py.gca().invert_xaxis() py.legend() - py.savefig('BaraffePisa_comp_mass_{0}.png'.format(age)) + py.savefig('BaraffePisa_comp_mass_{0}.png'.format(age)) return @@ -1014,7 +1315,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): class MISTv1(StellarEvolution): """ Define intrinsic properties for the MIST v1 stellar - models. + models. Models originally downloaded from `online server `_. @@ -1025,14 +1326,15 @@ class MISTv1(StellarEvolution): was downloaded from MIST website on 2/2017, while Version 1.2 was downloaded on 8/2018 (solar metallicity) and 4/2019 (other metallicities). Default is 1.2. - - synthpop_extension: boolean (default False) + + synthpop_extension: boolean (default True) If True, the isochrones are extended down to a minimum initial - mass of 0.1Msun using grids interpolated via SynthPop. If False, - the web-downloaded MIST isochrones are used with their varying - lower mass limits. True option is only valid for version=1.2. + mass of 0.1Msun using grids interpolated via `SynthPop + `_. + If False, the web-downloaded MIST isochrones are used with their + varying lower mass limits. True option is only valid for version=1.2. """ - def __init__(self, version=1.2, synthpop_extension=False): + def __init__(self, version=1.2, synthpop_extension=True): # define metallicity parameters for MIST models self.z_list = [0.0000014, # [Fe/H] = -4.00 0.0000045, # [Fe/H] = -3.50 @@ -1049,24 +1351,25 @@ def __init__(self, version=1.2, synthpop_extension=False): 0.014, # [Fe/H] = 0.00 0.025, # [Fe/H] = 0.25 0.045] # [Fe/H] = 0.50 - + # populate list of isochrone ages (log scale) self.age_list = np.arange(5.01, 10.30+0.005, 0.01) # Set version directory self.version = version self.synthpop_extension = synthpop_extension - if (self.version == 1.0) and (not synthpop_extension): + if self.version == 1.0: self.model_version_name = 'MISTv1.0' version_dir = 'v1.0/' - elif (self.version == 1.0) and synthpop_extension: - raise ValueError('Synthpop isochrone extension not supported for MISTv1.0 isochrones') + if synthpop_extension: + warnings.warn('SynthPop isochrone extension not supported for MISTv1.0 isochrones') + self.synthpop_extension = False elif self.version == 1.2: self.model_version_name = 'MISTv1.2' version_dir = 'v1.2/' else: raise ValueError('Version {0} not supported for MIST isochrones'.format(version)) - + # Specify location of model files self.model_dir = models_dir+'MISTv1/' + version_dir if self.synthpop_extension: @@ -1098,7 +1401,7 @@ def __init__(self, version=1.2, synthpop_extension=False): # Define required evo_grid number (now 1.2 for synthpop extension) self.evo_grid_min = 1.2 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the MISTv1 @@ -1108,7 +1411,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar * (10.**metallicity) @@ -1116,25 +1419,25 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - - if ((z_defined < np.min(self.z_list)) or - (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + + if ((z_defined < np.min(self.z_list)-0.1) or + (z_defined > np.max(self.z_list)+0.1)): + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file if self.synthpop_extension: addl_iso_file = self.model_extension_dir + 'iso/' + z_dir + iso_file - + # return isochrone data. Column locations depend on # version iso = Table.read(full_iso_file, format='fits') @@ -1178,7 +1481,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) return iso - + def format_isochrones(self): r""" Parse isochrone file downloaded from MIST web server, @@ -1203,7 +1506,7 @@ def format_isochrones(self): # Move into isochrone directory os.chdir(input_iso_dir) - + # Work on each metallicity isochrones individually for metal in metallicity_list: # More into metallicity directory, read isochrone file @@ -1247,32 +1550,52 @@ def format_isochrones(self): # COSMIC Breivik+ 2020 - not normal evo model #===========================================# class COSMIC(StellarEvolution): - + """ + Evolve objects using COSMIC + `Breivik et al. 2020 `_. + + See code website here: `_. + + Parameters + ---------- + BSEDict: dict or string, optional + Binary Stellar Evolution dictionary for COSMIC evolution. + Default is 'default' which uses the dictionary from COSMIC docs with zsun = 0.02. + + keep_disrupted_companions: bool, optional + When True, if the system is disrupted, the companions are added to the primary table. + When False, the companion is deleted. + Default is True. + + keep_COSMIC_tables : bool, optional + Allows COSMIC tables to be accessible on the SPISEA evolution object. + Default is False. + """ def __init__(self, BSEDict='default', keep_disrupted_companions=True, keep_COSMIC_tables=False): if BSEDict == 'default': self.BSEDict = { - "pts1": 0.001, "pts2": 0.01, "pts3": 0.02, "zsun": 0.02, "windflag": 3, - "eddlimflag": 0, "neta": 0.5, "bwind": 0.0, "hewind": 0.5, "beta": 0.125, - "xi": 0.5, "acc2": 1.5, "LBV_flag": 1, "alpha1": 1.0, "lambdaf": 0.0, - "ceflag": 1, "cekickflag": 2, "cemergeflag": 1, "cehestarflag": 0, - "qcflag": 5, - "qcrit_array": [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], - "kickflag": 5, "sigma": 265.0, "bhflag": 1, "bhsigmafrac": 1.0, - "sigmadiv": -20.0, "ecsn": 2.25, "ecsn_mlow": 1.6, "aic": 1, "ussn": 1, - "polar_kick_angle": 90.0, - "natal_kick_array": [[-100.0, -100.0, -100.0, -100.0, 0.0], [-100.0, -100.0, -100.0, -100.0, 0.0]], - "mm_mu_ns": 400.0, "mm_mu_bh": 200.0, "remnantflag": 4, - "fryer_mass_limit": 0, "mxns": 3.0, "rembar_massloss": 0.5, - "wd_mass_lim": 1, "maltsev_mode": 0, "maltsev_fallback": 0.5, - "maltsev_pf_prob": 0.1, "pisn": -2, "ppi_co_shift": 0.0, - "ppi_extra_ml": 0.0, "bhspinflag": 0, "bhspinmag": 0.0, "grflag": 1, - "eddfac": 10, "gamma": -2, "don_lim": -1, "acc_lim": -1, "tflag": 1, - "ST_tide": 1, - "fprimc_array": [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], - "ifflag": 1, "wdflag": 1, "epsnov": 0.001, "bdecayfac": 1, - "bconst": 3000, "ck": 1000, "rejuv_fac": 1.0, "rejuvflag": 0, - "bhms_coll_flag": 0, "htpmb": 1, "ST_cr": 1, "rtmsflag": 0 - } + "pts1": 0.001, "pts2": 0.01, "pts3": 0.02, "zsun": 0.02, "windflag": 3, + "eddlimflag": 0, "neta": 0.5, "bwind": 0.0, "hewind": 0.5, "beta": 0.125, + "xi": 0.5, "acc2": 1.5, "LBV_flag": 1, "alpha1": [1.0, 1.0], + "lambdaf": 0.0, "ceflag": 1, "cekickflag": 2, "cemergeflag": 1, + "cehestarflag": 0, "qcflag": 5, + "qcrit_array": [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], + "kickflag": 5, "sigma": 265.0, "bhflag": 1, "bhsigmafrac": 1.0, + "sigmadiv": -20.0, "ecsn": 2.25, "ecsn_mlow": 1.6, "aic": 1, "ussn": 1, + "polar_kick_angle": 90.0, + "natal_kick_array": [[-100.0, -100.0, -100.0, -100.0, 0.0], [-100.0, -100.0, -100.0, -100.0, 0.0]], + "mm_mu_ns": 400.0, "mm_mu_bh": 200.0, "remnantflag": 4, + "fryer_mass_limit": 0, "mxns": 3.0, "fryer_fmix": 1.0, + "fryer_mcrit_nsbh": 5.75, "rembar_massloss": 0.5, "wd_mass_lim": 1, + "maltsev_mode": 0, "maltsev_fallback": 0.5, "maltsev_pf_prob": 0.1, + "pisn": -2, "ppi_co_shift": 0.0, "ppi_extra_ml": 0.0, "bhspinflag": 0, + "bhspinmag": 0.0, "grflag": 1, "eddfac": 10, "gamma": -2, "don_lim": -1, + "acc_lim": [-1, -1], "smt_periastron_check": 0, "tflag": 1, "ST_tide": 1, + "fprimc_array": [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], + "ifflag": 1, "wdflag": 1, "epsnov": 0.001, "bdecayfac": 1, + "bconst": 3000, "ck": 1000, "rejuv_fac": 1.0, "rejuvflag": 0, + "bhms_coll_flag": 0, "htpmb": 1, "ST_cr": 1, "rtmsflag": 0 + } else: self.BSEDict = BSEDict @@ -1422,37 +1745,41 @@ def evolve(self, star_systems, companions, logAge, metallicity): star_systems['L'] = final_binaries['lum_1'] star_systems['logg'] = self.calc_logg(final_binaries['mass_1'], final_binaries['rad_1']) - # Takes second row with priamry info for binaries - # This allows for if first object is kicked but not disrupted - multiples_mask = star_systems['isMultiple'] == 1 - star_systems['kick_x'][multiples_mask] = kick_info.groupby(level=0).nth(1).loc[multiples_mask, 'delta_vsysx_1_rot'] - star_systems['kick_y'][multiples_mask] = kick_info.groupby(level=0).nth(1).loc[multiples_mask, 'delta_vsysy_1_rot'] - star_systems['kick_z'][multiples_mask] = kick_info.groupby(level=0).nth(1).loc[multiples_mask, 'delta_vsysz_1_rot'] - # Takes first row with primary info for singles since second row is blank - singles_mask = star_systems['isMultiple'] == 0 - star_systems['kick_x'][singles_mask] = kick_info.groupby(level=0).nth(0).loc[singles_mask, 'delta_vsysx_1_rot'] - star_systems['kick_y'][singles_mask] = kick_info.groupby(level=0).nth(0).loc[singles_mask, 'delta_vsysy_1_rot'] - star_systems['kick_z'][singles_mask] = kick_info.groupby(level=0).nth(0).loc[singles_mask, 'delta_vsysz_1_rot'] + # Takes sum of the delta kicks in case there was a kick, no disruption, then second kick + # Even for isolated stars, take sum since second row is blank + primary_kick_sum = ( + kick_info + .groupby(level=0)[["delta_vsysx_1_rot", "delta_vsysy_1_rot", "delta_vsysz_1_rot"]] + .sum() + ) + star_systems["kick_x"] = primary_kick_sum["delta_vsysx_1_rot"].reindex(star_systems["system_idx"], fill_value=0).to_numpy() + star_systems["kick_y"] = primary_kick_sum["delta_vsysy_1_rot"].reindex(star_systems["system_idx"], fill_value=0).to_numpy() + star_systems["kick_z"] = primary_kick_sum["delta_vsysz_1_rot"].reindex(star_systems["system_idx"], fill_value=0).to_numpy() companions['mass_current'] = final_binaries['mass_2'][companion_system_idxs] companions['Teff'] = final_binaries['teff_2'][companion_system_idxs] companions['L'] = final_binaries['lum_2'][companion_system_idxs] companions['logg'] = self.calc_logg(final_binaries['mass_2'][companion_system_idxs], final_binaries['rad_2'][companion_system_idxs]) - # Takes second row with companion info - companions['kick_x'] = kick_info.groupby(level=0).nth(1)['delta_vsysx_2_rot'][companion_system_idxs] - companions['kick_y'] = kick_info.groupby(level=0).nth(1)['delta_vsysy_2_rot'][companion_system_idxs] - companions['kick_z'] = kick_info.groupby(level=0).nth(1)['delta_vsysz_2_rot'][companion_system_idxs] + # Also take sum of companion kicks + companion_kick_sum = ( + kick_info + .groupby(level=0)[["delta_vsysx_2_rot", "delta_vsysy_2_rot", "delta_vsysz_2_rot"]] + .sum() + ) + companions['kick_x'] = companion_kick_sum["delta_vsysx_2_rot"].reindex(companion_system_idxs, fill_value=0).to_numpy() + companions['kick_y'] = companion_kick_sum["delta_vsysy_2_rot"].reindex(companion_system_idxs, fill_value=0).to_numpy() + companions['kick_z'] = companion_kick_sum["delta_vsysz_2_rot"].reindex(companion_system_idxs, fill_value=0).to_numpy() loga = np.log10(final_binaries['sep'][companion_system_idxs]*u.Rsun.to('AU')) companions['log_a'] = loga - fixed_phases1 = final_binaries['kstar_1'].to_numpy() + fixed_phases1 = final_binaries['kstar_1'].to_numpy(copy=True) fixed_phases1[np.where((final_binaries['kstar_1'] >= 10) & (final_binaries['kstar_1'] <= 12))[0]] = 101 fixed_phases1[np.where(final_binaries['kstar_1'] == 13)[0]] = 102 fixed_phases1[np.where(final_binaries['kstar_1'] == 14)[0]] = 103 star_systems['phase'] = fixed_phases1 - fixed_phases2 = final_binaries['kstar_2'][companion_system_idxs].to_numpy() + fixed_phases2 = final_binaries['kstar_2'][companion_system_idxs].to_numpy(copy=True) fixed_phases2[np.where((final_binaries['kstar_2'][companion_system_idxs] >= 10) & (final_binaries['kstar_2'][companion_system_idxs] <= 12))[0]] = 101 fixed_phases2[np.where(final_binaries['kstar_2'][companion_system_idxs] == 13)[0]] = 102 fixed_phases2[np.where(final_binaries['kstar_2'][companion_system_idxs] == 14)[0]] = 103 @@ -1519,7 +1846,9 @@ def evolve(self, star_systems, companions, logAge, metallicity): companions['system_idx'] = mapping[companions['system_idx']] star_systems.remove_columns(['system_idx', 'system_idx_new']) - #FIXME add assertion about mass_current not being zero + # Preserve a scalar kick magnitude alongside the vector components. + for table in (star_systems, companions): + table['kick'] = np.sqrt(table['kick_x']**2 + table['kick_y']**2 + table['kick_z']**2) # Make sure we didn't break anything by manipulating the number of companions assert star_systems['N_companions'].sum() == len(companions) @@ -1595,10 +1924,11 @@ def get_kick_differential(self, delta_v_sys_x, delta_v_sys_y, delta_v_sys_z, pha #==============================# # Merged model classes #==============================# -class MergedBaraffePisaEkstromParsec(StellarEvolution): +class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): """ This is a combination of several different evolution models: + * Phillips (`Phillips et al. 2020 `_) * Baraffe (`Baraffe et al. 2015 `_) * Pisa (`Tognelli et al. 2011 `_) * Geneva (`Ekstrom et al. 2012 `_) @@ -1610,14 +1940,18 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): For logAge < 7.4: - * Baraffe: 0.08 - 0.4 M_sun + * Phillips: 0.01 - 0.07 M_sun + * Phillips/Baraffe transition: 0.070 - 0.075 M_sun + * Baraffe: 0.075 - 0.4 M_sun * Baraffe/Pisa transition: 0.4 - 0.5 M_sun * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) * Geneva: Highest mass of Pisa models to 120 M_sun For logAge > 7.4: - * Parsec v1.2s: full mass range + * Phillips: 0.01 - 0.075 M_sun + * Phillips/Parsec v1.2s transition: 0.075 - 0.2 M_sun + * Parsec v1.2s: full mass range above 0.2 M_sun Parameters ---------- @@ -1625,21 +1959,17 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): If true, then use rotating Ekstrom models. Default is true. """ def __init__(self, rot=True): - if rot: - self.model_version_name = "MergedBaraffePisaEkstromParsec-rot" - else: - self.model_version_name = "MergedBaraffePisaEkstromParsec-norot" # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun # define metallicity parameters for Geneva models z_list = [0.015] # populate list of isochrone ages (log scale) - age_list = np.arange(6.0, 10.091, 0.01).tolist() + age_list = np.arange(6.0, 10.0, 0.01).tolist() # specify location of model files - model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' + model_dir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/' StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.015 @@ -1650,7 +1980,7 @@ def __init__(self, rot=True): self.z_file_map = {0.015: 'z015_norot/'} # Define required evo_grid number - self.evo_grid_min = 1.0 + self.evo_grid_min = 3.0 def isochrone(self, age=1.e8, metallicity=0.0): @@ -1678,9 +2008,134 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] - iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) + iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx]) + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # generate isochrone file string + full_iso_file = self.model_dir + z_dir + iso_file + + # return isochrone data + iso = Table.read(full_iso_file, format='ascii') + iso.rename_column('col1', 'mass') + iso.rename_column('col2', 'logT') + iso.rename_column('col3', 'logL') + iso.rename_column('col4', 'logg') + iso.rename_column('col5', 'logT_WR') + iso.rename_column('col6', 'mass_current') + iso.rename_column('col7', 'phase') + iso.rename_column('col8', 'model_ref') + + # Define "isWR" column based on phase info + isWR = Column([False] * len(iso), name='isWR') + idx_WR = np.where(iso['logT'] != iso['logT_WR']) + isWR[idx_WR] = True + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + # Assume mass of brown dwarfs does not change over their lifetime + #bd_idx = iso['mass'] < 0.08 + #iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + # Handling NaN effective temperatures + #nan_teff_idx = np.isnan(iso['logT']) + #if np.any(nan_teff_idx): + # iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + + return iso + + +class MergedBaraffePisaEkstromParsec(StellarEvolution): + """ + This is a combination of several different evolution models: + + * Baraffe (`Baraffe et al. 2015 `_) + * Pisa (`Tognelli et al. 2011 `_) + * Geneva (`Ekstrom et al. 2012 `_) + * Parsec (version 1.2s, `Bressan+12 `_) + + The model used depends on the age of the population and what stellar masses + are being modeled: + + + For logAge < 7.4: + + * Baraffe: 0.08 - 0.4 M_sun + * Baraffe/Pisa transition: 0.4 - 0.5 M_sun + * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) + * Geneva: Highest mass of Pisa models to 120 M_sun + + For logAge > 7.4: + + * Parsec v1.2s: full mass range + + Parameters + ---------- + rot: boolean, optional + If true, then use rotating Ekstrom models. Default is true. + """ + def __init__(self, rot=True): + if rot: + self.model_version_name = "MergedBaraffePisaEkstromParsec-rot" + else: + self.model_version_name = "MergedBaraffePisaEkstromParsec-norot" + # populate list of model masses (in solar masses) + mass_list = [(0.1 + i*0.005) for i in range(181)] + + # define metallicity parameters for Geneva models + z_list = [0.015] + + # populate list of isochrone ages (log scale) + age_list = np.arange(6.0, 10.091, 0.01).tolist() + + # specify location of model files + model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.z_solar = 0.015 + + # Switch to specify rotating/non-rotating models + if rot: + self.z_file_map = {0.015: 'z015_rot/'} + else: + self.z_file_map = {0.015: 'z015_norot/'} + + # Define required evo_grid number + self.evo_grid_min = 1.0 + + + def isochrone(self, age=1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec + collection + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + + if ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + + # Find nearest age in grid to input grid + age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] + iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) + + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] @@ -1696,9 +2151,9 @@ def isochrone(self, age=1.e8, metallicity=0.0): # ASCII version of files (newer model evo grids iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx]) full_iso_file = self.model_dir + z_dir + iso_file - + iso = Table.read(full_iso_file, format='ascii') - + iso.rename_column('col1', 'mass') iso.rename_column('col2', 'logT') iso.rename_column('col3', 'logL') @@ -1713,18 +2168,18 @@ def isochrone(self, age=1.e8, metallicity=0.0): idx_WR = np.where(iso['logT'] != iso['logT_WR']) isWR[idx_WR] = True iso.add_column(isWR) - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - + return iso class MergedPisaEkstromParsec(StellarEvolution): """ Same as MergedBaraffePisaEkstromParsec, but without - the Baraffe models. + the Baraffe models. Parameters ---------- @@ -1738,13 +2193,13 @@ def __init__(self, rot=True): self.model_version_name = "MergedPisaEkstromParsec-norot" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.015] - + # populate list of isochrone ages (log scale) age_list = np.arange(6.0, 8.001, 0.01).tolist() - + # specify location of model files model_dir = models_dir + 'merged/pisa_ekstrom_parsec/' StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) @@ -1758,13 +2213,13 @@ def __init__(self, rot=True): # Define required evo_grid number self.evo_grid_min = 1.0 - + # Error check to see if installed evolution model # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - - + + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Pisa-Ekstrom-Parsec collection. @@ -1773,18 +2228,18 @@ def isochrone(self, age=1.e8, metallicity=0.0): z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if not z_defined in self.z_list: - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] @@ -1804,7 +2259,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - + return iso class MergedSiessGenevaPadova(StellarEvolution): @@ -1819,27 +2274,27 @@ class MergedSiessGenevaPadova(StellarEvolution): * Padova (`Marigo et al. 2008 `_) For logAge < 7.4: - + * Siess: 0.1 - 7 M_sun * Siess/Geneva transition: 7 - 9 M_sun * Geneva: > 9 M_sun For logAge > 7.4: - + * Padova: full mass range """ def __init__(self): """ - Define intrinsic properties for merged Siess-meynetMaeder-Padova + Define intrinsic properties for merged Siess-meynetMaeder-Padova stellar models. """ self.model_version_name = "MergedSiessGenevaPadova" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.02] - + # populate list of isochrone ages (log scale) age_list = np.arange(5.5, 7.41, 0.01).tolist() age_list.append(7.48) @@ -1859,24 +2314,24 @@ def __init__(self): age_list.append(9.60) age_list.append(9.70) age_list.append(9.78) - + # specify location of model files model_dir = models_dir + 'merged/siess_meynetMaeder_padova/' StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.02 - + # Metallicity map self.z_file_map = {0.02: 'z02/'} # Define required evo_grid number self.evo_grid_min = 1.0 - + # Error check to see if installed evolution model # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - - + + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Siess-Geneva-Padova collection. @@ -1885,18 +2340,18 @@ def isochrone(self, age=1.e8, metallicity=0.0): z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if not z_defined in self.z_list: - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] @@ -1912,16 +2367,16 @@ def isochrone(self, age=1.e8, metallicity=0.0): iso.rename_column('col4', 'logg') iso.rename_column('col5', 'logT_WR') iso.rename_column('col6', 'model_ref') - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - + return iso #================================================# - -def make_isochrone_pisa_interp(log_age, metallicity=0.015, + +def make_isochrone_pisa_interp(log_age, metallicity=0.015, tracks=None, test=False): """ Read in a set of isochrones and generate an isochrone at log_age @@ -1948,14 +2403,14 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, if os.path.exists(rootDir+'iso_{0:3.2f}.fits'.format(log_age)): print( 'Isochrone at logAge = {0:3.2f} already exists'.format(log_age)) return - + # Name/directory for interpolated isochrone isoFile = rootDir+'iso_%3.2f.fits' % log_age outSuffix = '_%.2f' % (log_age) print( '*** Generating Pisa isochrone for log t = %3.2f and Z = %.3f' % \ (log_age, metallicity)) - + import time print( time.asctime(), 'Getting original Pisa isochrones.') iso = get_orig_pisa_isochrones(metallicity=metallicity) @@ -1969,7 +2424,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, good = np.where(tmp == log_age) young_model_logage = tmp[good[0]-1] old_model_logage = tmp[good[0]+1] - + # Isolate younger/older isochrones young_ind = np.where(iso.log_ages == young_model_logage) old_ind = np.where(iso.log_ages == old_model_logage) @@ -1982,7 +2437,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, if abs(young_model_logage - log_age) <= abs(old_model_logage - log_age): # Use young model mass grid young_iso, old_iso = interpolate_iso_tempgrid(young_iso, old_iso) - + else: # Use old model mass grid old_iso, young_iso = interpolate_iso_tempgrid(old_iso, young_iso) @@ -1990,25 +2445,25 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, # Now, can interpolate in time over the two models. Do this star by star. # Work in linear time here!! numStars = len(young_iso.M) - + interp_iso = Isochrone(log_age) interp_iso.log_Teff = np.zeros(numStars, dtype=float) interp_iso.log_L = np.zeros(numStars, dtype=float) interp_iso.log_g = np.zeros(numStars, dtype=float) interp_iso.M = young_iso.M # Since mass grids should already be matched - + for i in range(numStars): # Do interpolations in linear space model_ages = [10**young_model_logage[0], 10**old_model_logage[0]] target_age = 10**log_age #model_ages = [young_model_logage[0], old_model_logage[0]] #target_age = log_age - + # Build interpolation functions Teff_arr = [10**young_iso.log_Teff[i], 10**old_iso.log_Teff[i]] logL_arr = [10**young_iso.log_L[i], 10**old_iso.log_L[i]] logg_arr = [10**young_iso.log_g[i], 10**old_iso.log_g[i]] - + f_log_Teff = interpolate.interp1d(model_ages, Teff_arr, kind='linear') f_log_L = interpolate.interp1d(model_ages, logL_arr, kind='linear') f_log_g = interpolate.interp1d(model_ages, logg_arr, kind='linear') @@ -2034,15 +2489,15 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, py.legend() py.title('Pisa 2011 Isochrone at log t = %.2f' % log_age) py.savefig(rootDir + 'plots/interp_isochrone_at' + outSuffix + '.png') - + print( time.asctime(), 'Finished.') # Write output to file, MUST BE IN SAME ORDER AS ORIG FILES _out = open(isoFile, 'w') - - _out.write('%10s %10s %10s %10s\n' % + + _out.write('%10s %10s %10s %10s\n' % ('# log L', 'log Teff', 'Mass', 'log g')) - _out.write('%10s %10s %10s %10s\n' % + _out.write('%10s %10s %10s %10s\n' % ('# (Lsun)', '(Kelvin)', '(Msun)', '(cgs)')) for ii in range(len(interp_iso.M)): @@ -2066,7 +2521,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): if not os.path.exists(pms_dir): print( 'Failed to find Siess PMS isochrones for metallicity = ' + metSuffix) return - + # Collect the isochrones files = glob.glob(pms_dir + '*.dat') count = len(files) @@ -2075,7 +2530,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): data.isochrones = [] data.log_ages = [] - + # Extract useful params from isochrones for ff in range(len(files)): d = Table.read(files[ff], format='ascii') @@ -2083,7 +2538,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): # Extract logAge from filename log_age = float(files[ff].split('_')[2][:-4]) - # Create an isochrone object + # Create an isochrone object iso = Isochrone(log_age) iso.M = d['col3'] iso.log_Teff = d['col2'] @@ -2092,13 +2547,13 @@ def get_orig_pisa_isochrones(metallicity=0.015): # If a log g column exist, extract it. Otherwise, calculate # log g from T and L and add column at end if len(d.keys()) == 3: - + # Calculate log g from T and L L_sun = 3.8 * 10**33 #cgs SB_sig = 5.67 * 10**-5 #cgs M_sun = 2. * 10**33 #cgs G_const = 6.67 * 10**-8 #cgs - + radius = np.sqrt( (10**d['col1'] * L_sun) / (4 * np.pi * SB_sig * (10**d['col2'])**4) ) g = (G_const * d['col3'] * M_sun) / radius**2 @@ -2107,7 +2562,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): iso.log_g = np.log10(g.astype(np.float)) else: iso.log_g = d['col4'] - + data.isochrones.append(iso) data.log_ages.append(log_age) diff --git a/spisea/exceptions.py b/spisea/exceptions.py index 5b45baa1..e0587d90 100644 --- a/spisea/exceptions.py +++ b/spisea/exceptions.py @@ -8,12 +8,12 @@ class ModelMismatch(Exception): """ def __init__(self, required_num, grid_num, model_type): assert (model_type == 'evolution') | (model_type == 'atmosphere') - + if model_type == 'evolution': model_file = 'spisea_models.tar.gz' elif model_type == 'atmosphere': model_file = 'spisea_cdbs.tar.gz' - + # Compose error message str1 = 'WARNING: Desired {0} model requires model grid version >= {1},'.format(model_type, required_num) str2 = 'but model grid version {0} is installed.'.format(grid_num) diff --git a/spisea/filters.py b/spisea/filters.py index 23c029b3..f3834167 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -29,7 +29,7 @@ def get_nirc2_filt(name): while len(idx) != 0: wavelength[idx+1] += 1.0e-8 - + diff = np.diff(wavelength) idx = np.where(diff <= 0)[0] #print( 'Duplicate entry loop' ) @@ -68,7 +68,7 @@ def get_2mass_filt(name): name='2MASS_{0}'.format(name)) return spectrum - + def get_vista_filt(name): """ @@ -79,21 +79,21 @@ def get_vista_filt(name): t = Table.read('{0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name)) + raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name)) # Wavelength must be in angstroms, transmission in fraction wave = t['col1'] * 10 trans = t['col2'] * 0.01 - + # Change any negative numbers to 0, as well as anything shortward # of 0.4 microns or longward of 2.9 microns # (no VISTA filter transmissions beyond these boundaries) bad = np.where( (trans < 0) | (wave < 4000) | (wave > 29000) ) trans[bad] = 0 - + # Now we can define the VISTA filter bandpass objects spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='VISTA_{0}'.format(name)) - + return spectrum def get_decam_filt(name): @@ -103,30 +103,26 @@ def get_decam_filt(name): # Read in filter info try: t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii') - t.rename_column('Y', 'y') - - cols = np.array(t.keys()) - idx = np.where(cols == name)[0][0] - trans = t[cols[idx]] + trans = t[name] except: - raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) + if name=='y': + raise ValueError('DECam has a /"Y/" filter, not /"y/". The /"y/" in SPISEA 90) trans[bad] = 0 except: - raise ValueError('Could not find Gaia filter {0}'.format(name)) + raise ValueError('Could not find Gaia filter {0} for version {1}'.format(name, version)) # Convert wavelengths to angstroms (from nm) wave = t['LAMBDA'] * 10 @@ -465,10 +479,147 @@ def get_euclid_filt(name): transmission = t[t.keys()[1]] # Convert wavelength to Angstroms - wavelength = wavelength * 10 + if name.lower() != 'vis': + wavelength = wavelength * 10 # Make spectrum object spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', name='euclid_{0}'.format(name)) return spectrum + +def get_nsfcam_filt(name): + """ + Define irtf nsfcam filters as pysynphot object + """ + try: + t = Table.read('{0}/nsfcam/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find nsfcam filter {0} in {1}/nsfcam'.format(name, filters_dir)) + + # Wavelength already in angstrom and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nsfcam_{0}'.format(name)) + + return spectrum + +def get_tess_filt(name): + """ + Define the TESS filter as pysynphot object + """ + try: + t = Table.read('{0}/tess/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find tess filter {0} in {1}/tess'.format(name, filters_dir)) + + # Wavelength from nanometers to angstroms and and transmission in fraction + wave = t['col1']*10 + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='tess,{0}'.format(name)) + + return spectrum + +def get_washington_filt(name): + """ + Define the Washington filters as pysynphot object + """ + try: + t = Table.read('{0}/washington/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find washington filter {0} in {1}/washington'.format(name, filters_dir)) + + # Wavelength from nanometers to angstroms and and transmission in fraction + wave = t['col1']*10 + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='washington,{0}'.format(name)) + + return spectrum + +def get_hipparcos_filt(name): + """ + Define the Hipparcos filter as pysynphot object + """ + try: + t = Table.read('{0}/hipparcos/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find hipparcos filter {0} in {1}/hipparcos'.format(name, filters_dir)) + + # Wavelength in angstroms and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='hipparcos,{0}'.format(name)) + + return spectrum + +def get_tycho_filt(name): + """ + Define the Tycho filters as pysynphot object + """ + try: + t = Table.read('{0}/tycho/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find tycho filter {0} in {1}/tycho'.format(name, filters_dir)) + + # Wavelength in angstroms and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='tycho,{0}'.format(name)) + + return spectrum + +def get_kepler_filt(name): + """ + Define the Kepler filters as pysynphot object + """ + try: + t = Table.read('{0}/kepler/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find kepler filter {0} in {1}/kepler'.format(name, filters_dir)) + + # Wavelength in angstroms and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='kepler,{0}'.format(name)) + + return spectrum + +def get_ogle_filt(name): + """ + Define the OGLE filters as pysynphot object + """ + try: + t = Table.read('{0}/ogle/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find ogle filter {0} in {1}/ogle'.format(name, filters_dir)) + + # Wavelength in nm->angstroms and and transmission in percent->fraction + wave = np.flip(t['col1'])*10 + trans = np.flip(t['col2'])/100 + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ogle,{0}'.format(name)) + + return spectrum + +def get_subaru_filt(instrument, name): + """ + Define the subaru filters as pysynphot object + """ + try: + t = Table.read('{0}/subaru/{1}/{2}.dat'.format(filters_dir, instrument, name), format='ascii') + except: + raise ValueError('Could not find Subaru filter {0} in {1}/subaru/{2}'.format(name, filters_dir, instrument)) + + # Wavelength in nm->angstroms and and transmission in percent->fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='subaru,{0},{1}'.format(instrument, name)) + + return spectrum diff --git a/spisea/ifmr.py b/spisea/ifmr.py index f88afdbe..ff387e95 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -12,7 +12,7 @@ #https://ui.adsabs.harvard.edu/abs/2014ApJ...783...10S/abstract #BH/NS IFMR based on Sukhbold et al. 2016 for solar-Z models:: #https://ui.adsabs.harvard.edu/abs/2016ApJ...821...38S/abstract -#PPISN based on Woosley 2017: +#PPISN based on Woosley 2017: #https://ui.adsabs.harvard.edu/abs/2017ApJ...836..244W/abstract #PPSIN based on Woosley et al. 2020: #https://ui.adsabs.harvard.edu/abs/2020ApJ...896...56W/abstract @@ -25,8 +25,9 @@ import numpy as np class IFMR(object): - def __init__(self): - pass + def __init__(self, seed=None): + self.seed = seed + self.rng = np.random.default_rng(seed) def get_Z(self, Fe_H): """ @@ -36,7 +37,7 @@ def get_Z(self, Fe_H): """ return 10**(Fe_H - 1.85387) - def Kalirai_mass(self, MZAMS): + def Kalirai_mass(self, MZAMS): # for white dwarfs """ From Kalirai+08 https://ui.adsabs.harvard.edu/abs/2008ApJ...676..594K/abstract 1.16 < MZAMS < 6.5 @@ -47,36 +48,34 @@ def Kalirai_mass(self, MZAMS): result = 0.109*MZAMS + 0.394 final = np.zeros(len(MZAMS)) - - bad_idx = np.where(MZAMS < 0.5) - final[bad_idx] = -99 - - good_idx = np.where(MZAMS >= 0.5) + + good_idx = (MZAMS >= 0.5) & (MZAMS < 9) final[good_idx] = result[good_idx] + final[~good_idx] = -99 return final - - + + class IFMR_Spera15(IFMR): """ The BH/NS IFMR (used for MZAMS>= 7 M_sun) comes from `Spera et. al. (2015) Appendix C `_. - The WD IFMR (used for MZAMS< 7_Msun) comes from + The WD IFMR (used for MZAMS< 7_Msun) comes from `Kalirai et al. (2008) `_. See Rose et al. (submitted) for more details. - + """ - + #The get_Mco functions come from C11 of Spera def get_Mco_low_metal(self, Z, MZAMS): """ C15 of Spera, valid for Z < 1.0e-3 - + """ - + B1 = 67.07 K1 = 46.89 K2 = 1.138e2 @@ -95,7 +94,7 @@ def get_Mco_med_metal(self, Z, MZAMS): C14 of Spera, valid for Z >= 1.0e-3 and Z <= 4.0e-3 """ - + B1 = 40.98 + 3.415e4*Z - 8.064e6*Z**2 K1 = 35.17 + 1.548e4*Z - 3.759e6*Z**2 K2 = 20.36 + 1.162e5*Z - 2.276e7*Z**2 @@ -114,7 +113,7 @@ def get_Mco_high_metal(self, Z, MZAMS): C13 of Spera, valid for Z > 4.0e-3 """ - + B1 = 59.63 - 2.969e3*Z + 4.988e4*Z**2 K1 = 45.04 - 2.176e3*Z + 3.806e4*Z**2 K2 = 1.389e2 - 4.664e3*Z + 5.106e4*Z**2 @@ -124,7 +123,7 @@ def get_Mco_high_metal(self, Z, MZAMS): g1 = 0.5/(1+10**((K1-MZAMS)*(d1))) #C12 of Spera g2 = 0.5/(1+10**((K2-MZAMS)*(d2))) #C12 of Spera - + return -2.0 + (B1 + 2.0)*(g1 + g2) #C11 of Spera @@ -133,8 +132,8 @@ def get_Mco(self, Z, MZAMS): This function uses Spera C11-C15 in order to reurn an array of core masses from an array of metallicities and ZAMS masses. It will be the same length as these two arrays with -99 entries where invalid (ie MZAMS< 7 M_sun) - Parameters: - + Parameters: + Z: an array with metallicities reported as Z where Z is metal_mass/total_mass MZAMS: an array of ZAMS masses in solar masses. The Spera functions are valid for MZAMS> 7 M_sun @@ -159,28 +158,28 @@ def get_Mco(self, Z, MZAMS): core_masses[high_metal_idx] = self.get_Mco_high_metal(Z[high_metal_idx], MZAMS[high_metal_idx]) return core_masses - + def M_rem_very_low_metal_low_mass(self, Z, Mco): """ C1 of Spera, valid for Z <= 5.0e-4 and Mco <= 5.0 - + Parameters: Z: an array of metallicities reported as metal_mass/total_mass Mco: an arrray of core masses in M_sun """ - + p = -2.333 + 0.1559*Mco + 0.2700*Mco**2 #C2 of Spera #need to return p or 1.27, whichever is greater final = np.zeros(len(p)) - + p_max_idx = np.where(p >= 1.27) final[p_max_idx] = p[p_max_idx] - + p_min_idx = np.where(p < 1.27) final[p_min_idx] = 1.27 @@ -218,7 +217,7 @@ def M_rem_very_low_metal_high_mass(self, Z, Mco): m = -6.476e2*Z + 1.911 #C3 of Spera q = 2.300e3*Z + 11.67 #C3 of Spera - + f = m*Mco + q #C2 of Spera #need to return either p or f, whichever is less @@ -229,7 +228,7 @@ def M_rem_very_low_metal_high_mass(self, Z, Mco): f_min_idx = np.where(f < p) final[f_min_idx] = f[f_min_idx] - + return final def M_rem_low_metal_low_mass(self, Z, Mco): @@ -253,15 +252,15 @@ def M_rem_low_metal_low_mass(self, Z, Mco): #need to return h or 1.27, whichever is greater final = np.zeros(len(h)) - + h_max_idx = np.where(h >= 1.27) final[h_max_idx] = h[h_max_idx] - + h_min_idx = np.where(h < 1.27) final[h_min_idx] = 1.27 return final - + def M_rem_low_metal_med_mass(self, Z, Mco): """ @@ -317,9 +316,9 @@ def M_rem_low_metal_high_mass(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final - + def M_rem_med_metal_low_mass(self, Z, Mco): """ @@ -342,10 +341,10 @@ def M_rem_med_metal_low_mass(self, Z, Mco): #need to return h or 1.27, whichever is greater final = np.zeros(len(h)) - + h_max_idx = np.where(h >= 1.27) final[h_max_idx] = h[h_max_idx] - + h_min_idx = np.where(h < 1.27) final[h_min_idx] = 1.27 @@ -396,7 +395,7 @@ def M_rem_med_metal_high_mass_1(self, Z, Mco): q = -1.296e4*Z + 26.98 f = m*Mco + q #C5 of Spera - + #need to return either h or f, whichever is greater final = np.zeros(len(h)) @@ -405,7 +404,7 @@ def M_rem_med_metal_high_mass_1(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final def M_rem_med_metal_high_mass_2(self, Z, Mco): @@ -432,7 +431,7 @@ def M_rem_med_metal_high_mass_2(self, Z, Mco): q = 1.061 f = m*Mco + q #C5 of Spera - + #need to return either h or f, whichever is greater final = np.zeros(len(h)) @@ -441,7 +440,7 @@ def M_rem_med_metal_high_mass_2(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final @@ -466,10 +465,10 @@ def M_rem_high_metal_low_mass(self, Z, Mco): #need to return h or 1.27, whichever is greater final = np.zeros(len(h)) - + h_max_idx = np.where(h >= 1.27) final[h_max_idx] = h[h_max_idx] - + h_min_idx = np.where(h < 1.27) final[h_min_idx] = 1.27 @@ -520,7 +519,7 @@ def M_rem_high_metal_high_mass(self, Z, Mco): q = 1.061 f = m*Mco + q #C5 of Spera - + #need to return either h or f, whichever is greater final = np.zeros(len(h)) @@ -529,15 +528,15 @@ def M_rem_high_metal_high_mass(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final def generate_death_mass(self, mass_array, metallicity_array): """ - The top-level function that assigns the remnant type - and mass based on the stellar initial mass. - + The top-level function that assigns the remnant type + and mass based on the stellar initial mass. + Parameters ---------- mass_array: array of floats @@ -548,20 +547,21 @@ def generate_death_mass(self, mass_array, metallicity_array): Notes ------ The output typecode tells what compact object formed: - + * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - A typecode of value -1 means you're outside the range of + * BD: typecode = 90 + A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: MZAMS > 0.5 + Range of validity: MZAMS > 0.5 and 0.01 < MZAMS < 0.08 Returns ------- output_arr: 2-element array - output_array[0] contains the remnant mass, and + output_array[0] contains the remnant mass, and output_array[1] contains the typecode """ @@ -569,7 +569,7 @@ def generate_death_mass(self, mass_array, metallicity_array): #output_array[1] holds the remnant type output_array = np.zeros((2, len(mass_array))) - codes = {'WD': 101, 'NS': 102, 'BH': 103} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 90} #create array to store the remnant masses generated by Spera equations rem_mass_array = np.zeros(len(mass_array)) @@ -586,10 +586,10 @@ def generate_death_mass(self, mass_array, metallicity_array): Kal_idx = np.where(core_mass < 0) rem_mass_array[Kal_idx] = self.Kalirai_mass(mass_array[Kal_idx]) - + ##### very low metallicity Z < 5.0e-4 - + #remnant masses of stars with Z < 5.0e-4 and Mco < 5.0 very_low_metal_low_mass_idx = np.where((Z_array < 5.0e-4) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[very_low_metal_low_mass_idx] = self.M_rem_very_low_metal_low_mass(Z_array[very_low_metal_low_mass_idx], core_mass[very_low_metal_low_mass_idx]) @@ -603,7 +603,7 @@ def generate_death_mass(self, mass_array, metallicity_array): rem_mass_array[very_low_metal_high_mass_idx] = self.M_rem_very_low_metal_high_mass(Z_array[very_low_metal_high_mass_idx], core_mass[very_low_metal_high_mass_idx]) #### low metallicity 5.0e-4 <= Z < 1.0e-3 - + #remnant masses of stars with 5.0e-4 <= Z < 1.0e-3 and Mco < 5.0 low_metal_low_mass_idx = np.where((Z_array >= 5.0e-4) & (Z_array < 1.0e-3) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[low_metal_low_mass_idx] = self.M_rem_low_metal_low_mass(Z_array[low_metal_low_mass_idx], core_mass[low_metal_low_mass_idx]) @@ -615,9 +615,9 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with 5.0e-4 <= Z < 1.0e-3 and Mco > 10.0 low_metal_high_mass_idx = np.where((Z_array >= 5.0e-4) & (Z_array < 1.0e-3) & (core_mass > 10.0)) rem_mass_array[low_metal_high_mass_idx] = self.M_rem_low_metal_high_mass(Z_array[low_metal_high_mass_idx], core_mass[low_metal_high_mass_idx]) - + #### medium metallicity 1.0e-3 <= Z <= 4.0e-3 - + #remnant masses of stars with 1.0e-3 <= Z <= 4.0e-3 and Mco < 5.0 med_metal_low_mass_idx = np.where((Z_array >= 1.0e-3) & (Z_array <= 4.0e-3) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[med_metal_low_mass_idx] = self.M_rem_med_metal_low_mass(Z_array[med_metal_low_mass_idx],core_mass[med_metal_low_mass_idx]) @@ -633,9 +633,9 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with 2.0e-3 <= Z <= 4.0e-3 and Mco > 10.0 med_metal_high_mass_idx_2 = np.where((Z_array >= 2.0e-3) & (Z_array <= 4.0e-3) & (core_mass > 10.0)) rem_mass_array[med_metal_high_mass_idx_2] = self.M_rem_med_metal_high_mass_2(Z_array[med_metal_high_mass_idx_2], core_mass[med_metal_high_mass_idx_2]) - + #### high metallicity Z > 4.0e-3 - + #remnant masses of stars with Z > 4.0e-3 and Mco < 5.0 high_metal_low_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[high_metal_low_mass_idx] = self.M_rem_high_metal_low_mass(Z_array[high_metal_low_mass_idx], core_mass[high_metal_low_mass_idx]) @@ -647,16 +647,22 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with Z > 4.0e-3 and MZAMS > 10.0 high_metal_high_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass > 10.0)) rem_mass_array[high_metal_high_mass_idx] = self.M_rem_high_metal_high_mass(Z_array[high_metal_high_mass_idx], core_mass[high_metal_high_mass_idx]) - + + #classify brown dwarfs before checking for bad indices (in case they are looped into them) + BD_idx = np.where((mass_array >= 0.01) & (mass_array < 0.08)) + rem_mass_array[BD_idx] = mass_array[BD_idx] + output_array[0][BD_idx] = rem_mass_array[BD_idx] + output_array[1][BD_idx] = codes['BD'] + #assign object types based on remnant mass bad_idx = np.where(rem_mass_array < 0) #outside the range of validity for the ifmr WD_idx = np.where((rem_mass_array <= 1.4) & (rem_mass_array >= 0 )) #based on the Chandresekhar limit - NS_idx = np.where((rem_mass_array > 1.4) & (rem_mass_array <= 3.0)) #based on figures 15-17 of Spera + NS_idx = np.where((rem_mass_array > 1.4) & (rem_mass_array <= 3.0)) #based on figures 15-17 of Spera BH_idx = np.where(rem_mass_array > 3.0) #based on figures 15-17 of Spera output_array[0][bad_idx] = rem_mass_array[bad_idx] output_array[1][bad_idx] = -1 - + output_array[0][WD_idx] = rem_mass_array[WD_idx] output_array[1][WD_idx] = codes['WD'] @@ -667,76 +673,76 @@ def generate_death_mass(self, mass_array, metallicity_array): output_array[1][BH_idx] = codes['BH'] return output_array - + class IFMR_Raithel18(IFMR): """ - The IFMR is a combination of the WD IFMR from + The IFMR is a combination of the WD IFMR from `Kalirai et al. (2008) `_ and the NS/BH IFMR from `Raithel et al. (2018) `_. - Note that the NS masses are NOT assigned based on the above results. + Note that the NS masses are NOT assigned based on the above results. We do take the NS/BH formation ratio and the BH masses. - NS masses are assigned based on random draws from a Gaussian (see NS_mass function). + NS masses are assigned based on random draws from a Gaussian (see NS_mass function). - See + See `Lam et al. (2020) `_ and Rose et al. (submitted) for more details. """ def BH_mass_core_low(self, MZAMS): - """ - Eqn (1) - Paper: 15 < MZAMS < 40 - Us extending: 15 < MZAMS < 42.22 + """ + Eqn (1) + Paper: 15 < MZAMS < 40 + Us extending: 15 < MZAMS < 42.22 """ return -2.024 + 0.4130*MZAMS def BH_mass_all_low(self, MZAMS): - """ - Eqn (2) - Paper: 15 < MZAMS < 40 - Us extending: 15 < MZAMS < 42.22 + """ + Eqn (2) + Paper: 15 < MZAMS < 40 + Us extending: 15 < MZAMS < 42.22 """ return 16.28 + 0.00694 * (MZAMS - 21.872) - 0.05973 * (MZAMS - 21.872)**2 + 0.003112 * (MZAMS - 21.872)**3 def BH_mass_high(self, MZAMS): - """ - Eqn (3) - Paper: 45 < MZAMS < 120 - Us extending: 42.22 < MZAMS < 120 + """ + Eqn (3) + Paper: 45 < MZAMS < 120 + Us extending: 42.22 < MZAMS < 120 """ return 5.795 + 1.007 * 10**9 * MZAMS**-4.926 def BH_mass_low(self, MZAMS, f_ej): - """ - Eqn (4) - Paper: 15 < MZAMS < 40 - Us extending: 15 < MZAMS < 42.22 + """ + Eqn (4) + Paper: 15 < MZAMS < 40 + Us extending: 15 < MZAMS < 42.22 """ return f_ej * self.BH_mass_core_low(MZAMS) + (1 - f_ej) * self.BH_mass_all_low(MZAMS) def NS_mass(self, MZAMS): - """ + """ Drawing the NS mass from a Gaussian distrobuton based on observational data. - Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a + Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of California, Berkeley using a sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), J1757+1854 Cameron et al. (2018), J0030+0451 Riley et al. (2019), J1301+0833 Romani et al. (2016) The Gaussian distribution was fit using this data and a Bayesian MCMC method adapted from Kiziltan et al. (2010). - + """ - return np.random.normal(loc=1.36, scale=0.09, size=len(MZAMS)) + return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS)) def generate_death_mass(self, mass_array): """ - The top-level function that assigns the remnant type - and mass based on the stellar initial mass. - + The top-level function that assigns the remnant type + and mass based on the stellar initial mass. + Parameters ---------- mass_array: array of floats @@ -747,23 +753,24 @@ def generate_death_mass(self, mass_array): Notes ------ The output typecode tells what compact object formed: - + * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 + * BD: typecode = 90 - A typecode of value -1 means you're outside the range of - validity for applying the ifmr formula. + A typecode of value -1 means you're outside the range of + validity for applying the ifmr formula. - A remnant mass of -99 means you're outside the range of + A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: 0.5 < MZAMS < 120 + Range of validity: 0.01 < MZAMS < 0.08 and 0.5 < MZAMS < 120 Returns ------- output_arr: 2-element array - output_array[0] contains the remnant mass, and + output_array[0] contains the remnant mass, and output_array[1] contains the typecode """ @@ -772,25 +779,35 @@ def generate_death_mass(self, mass_array): output_array = np.zeros((2, len(mass_array))) #Random array to get probabilities for what type of object will form - random_array = np.random.randint(1, 1001, size = len(mass_array)) + random_array = self.rng.integers(1, 1001, size = len(mass_array)) - codes = {'WD': 101, 'NS': 102, 'BH': 103} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 90} """ The id_arrays are to separate all the different formation regimes """ - id_array0 = np.where((mass_array < 0.5) | (mass_array >= 120)) - output_array[0][id_array0] = -99 * np.ones(len(id_array0)) - output_array[1][id_array0] = -1 * np.ones(len(id_array0)) + #classifying brown dwarfs + id_array_BD = np.where((mass_array >= 0.01) & (mass_array < 0.08)) + output_array[0][id_array_BD] = mass_array[id_array_BD] + output_array[1][id_array_BD] = codes['BD'] + + #classifying invalid mass ranges + id_array0 = np.where((mass_array < 0.01) | ((mass_array >= 0.08) & (mass_array < 0.5)) | (mass_array >= 120)) + output_array[0][id_array0] = -99 + output_array[1][id_array0] = -1 + + #classifying white dwarfs id_array1 = np.where((mass_array >= 0.5) & (mass_array < 9)) output_array[0][id_array1] = self.Kalirai_mass(mass_array[id_array1]) output_array[1][id_array1]= codes['WD'] + #classifying neutron stars id_array2 = np.where((mass_array >= 9) & (mass_array < 15)) output_array[0][id_array2] = self.NS_mass(mass_array[id_array2]) output_array[1][id_array2] = codes['NS'] + #classifying black holes id_array3_BH = np.where((mass_array >= 15) & (mass_array < 17.8) & (random_array > 679)) output_array[0][id_array3_BH] = self.BH_mass_low(mass_array[id_array3_BH], 0.9) output_array[1][id_array3_BH] = codes['BH'] @@ -802,7 +819,7 @@ def generate_death_mass(self, mass_array): id_array4_BH = np.where((mass_array >= 17.8) & (mass_array < 18.5) & (random_array > 833)) output_array[0][id_array4_BH]= self.BH_mass_low(mass_array[id_array4_BH], 0.9) output_array[1][id_array4_BH] = codes['BH'] - + id_array4_NS = np.where((mass_array >= 17.8) & (mass_array < 18.5) & (random_array <= 833)) output_array[0][id_array4_NS] = self.NS_mass(mass_array[id_array4_NS]) output_array[1][id_array4_NS] = codes['NS'] @@ -810,7 +827,7 @@ def generate_death_mass(self, mass_array): id_array5_BH = np.where((mass_array >= 18.5) & (mass_array < 21.7) & (random_array > 500)) output_array[0][id_array5_BH] = self.BH_mass_low(mass_array[id_array5_BH], 0.9) output_array[1][id_array5_BH] = codes['BH'] - + id_array5_NS = np.where((mass_array >= 18.5) & (mass_array < 21.7) & (random_array <= 500)) output_array[0][id_array5_NS] = self.NS_mass(mass_array[id_array5_NS]) output_array[1][id_array5_NS] = codes['NS'] @@ -822,7 +839,7 @@ def generate_death_mass(self, mass_array): id_array7_BH = np.where((mass_array >= 25.2) & (mass_array < 27.5) & (random_array > 652)) output_array[0][id_array7_BH] = self.BH_mass_low(mass_array[id_array7_BH], 0.9) output_array[1][id_array7_BH] = codes['BH'] - + id_array7_NS = np.where((mass_array >= 25.2) & (mass_array < 27.5) & (random_array <= 652)) output_array[0][id_array7_NS] = self.NS_mass(mass_array[id_array7_NS]) output_array[1][id_array7_NS] = codes['NS'] @@ -838,11 +855,12 @@ def generate_death_mass(self, mass_array): id_array10_BH = np.where((mass_array >= 60) & (mass_array < 120) & (random_array > 400)) output_array[0][id_array10_BH] = self.BH_mass_high(mass_array[id_array10_BH]) output_array[1][id_array10_BH] = codes['BH'] - + id_array10_NS = np.where((mass_array >= 60) & (mass_array < 120) & (random_array <= 400)) output_array[0][id_array10_NS] = self.NS_mass(mass_array[id_array10_NS]) output_array[1][id_array10_NS] = codes['NS'] + return(output_array) class IFMR_N20_Sukhbold(IFMR): @@ -851,11 +869,11 @@ class IFMR_N20_Sukhbold(IFMR): `Sukhbold & Woosley (2014) `_. The BH/NS IFMR for solar metallicity progenitors comes from `Sukhbold et al. (2016) `_. - The PPISN models are from + The PPISN models are from `Woosley (2017) `_ and `Woosley et al. (2020) `_. - The WD IFMR is from + The WD IFMR is from `Kalirai et al. (2008) `_. Note that the NS masses are NOT assigned based on the above results. We do take the NS/BH formation ratio and the BH masses. @@ -870,7 +888,7 @@ def zero_BH_mass(self, MZAMS): #func = np.poly1d(zero_coeff) #result = func(MZAMS) return 0.46522639*MZAMS + -3.29170817 - + def solar_BH_mass(self, MZAMS): #solar_coeff = [-0.27079245, 24.74320755] #func = np.poly1d(solar_coeff) @@ -880,24 +898,24 @@ def solar_BH_mass(self, MZAMS): Zsun = 0.014 def NS_mass(self, MZAMS): - """ + """ Drawing the NS mass from a Gaussian distrobuton based on observational data. Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a - sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), - J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), - J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), + sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), + J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), + J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), J1757+1854 Cameron et al. (2018), J0030+0451 Riley et al. (2019), J1301+0833 Romani et al. (2016) The Gaussian distribution was fit using this data and a Bayesian MCMC method adapted from Kiziltan et al. (2010). - + """ if isinstance(MZAMS, np.ndarray): - return np.random.normal(loc=1.36, scale=0.09, size=len(MZAMS)) + return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS)) else: - return np.random.normal(loc=1.36, scale=0.09, size=1)[0] - - + return self.rng.normal(loc=1.36, scale=0.09, size=1)[0] + + def BH_mass_low(self, MZAMS): """ 9 < MZAMS < 40 Msun @@ -913,9 +931,8 @@ def BH_mass_high(self, MZAMS, Z): """ # Solar metallicity (what Sam is using) Zsun = 0.014 - - zfrac = Z/Zsun + zfrac = np.atleast_1d(Z/Zsun) # super-solar Z gives identical results as solar Z above_idx = np.where(zfrac > 1) if len(above_idx) > 1: @@ -937,15 +954,14 @@ def prob_BH_high(self, Z): """ # Solar metallicity (what Sam is using) Zsun = 0.014 - - zfrac = Z/Zsun - - # super-solar Z gives identical results as solar Z - if zfrac > 1: - zfrac = 1.0 - if zfrac < 0: - raise ValueError('Z must be non-negative') + Z = np.atleast_1d(Z) + # Convert from [Fe/H] to Z + zfrac = Z / Zsun + + # super-solar Z gives identical results as solar Z + zfrac[zfrac > 1] = 1.0 + zfrac[zfrac < 0] = np.nan pBH = 1 - 0.8*zfrac @@ -954,9 +970,9 @@ def prob_BH_high(self, Z): def generate_death_mass(self, mass_array, metallicity_array): """ - The top-level function that assigns the remnant type - and mass based on the stellar initial mass. - + The top-level function that assigns the remnant type + and mass based on the stellar initial mass. + Parameters ---------- mass_array: array of floats @@ -967,93 +983,101 @@ def generate_death_mass(self, mass_array, metallicity_array): Notes ------ The output typecode tells what compact object formed: - + * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - A typecode of value -1 means you're outside the range of - validity for applying the ifmr formula. - A remnant mass of -99 means you're outside the range of + A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. - Range of validity: MZAMS > 0.5 - + A remnant mass of -99 means you're outside the range of + validity for applying the ifmr formula. + Range of validity: MZAMS > 0.5 + Returns ------- output_arr: 2-element array - output_array[0] contains the remnant mass, and + output_array[0] contains the remnant mass, and output_array[1] contains the typecode """ #output_array[0] holds the remnant mass #output_array[1] holds the remnant type + mass_array = np.atleast_1d(mass_array) + metallicity_array = np.atleast_1d(metallicity_array) + output_array = np.zeros((2, len(mass_array))) codes = {'WD': 101, 'NS': 102, 'BH': 103} + # Array to store the remnant masses - rem_mass_array = np.zeros(len(mass_array)) + # rem_mass_array = np.zeros(len(mass_array)) # Convert from [Fe/H] to Z # FIXME: if have Fe/H = nan that makes Z = 0. Is that the behavior we want? Z_array = np.zeros((len(metallicity_array))) - metal_idx = np.where(metallicity_array != np.nan) + metal_idx = ~np.isnan(metallicity_array) Z_array[metal_idx] = self.get_Z(metallicity_array[metal_idx]) # Random array to get probabilities for what type of object will form - random_array = np.random.randint(1, 101, size = len(mass_array)) + random_array = self.rng.integers(1, 101, size=len(mass_array)) - id_array0 = np.where((mass_array < 0.5) | (mass_array >= 120)) - output_array[0][id_array0] = -99 * np.ones(len(id_array0)) - output_array[1][id_array0] = -1 * np.ones(len(id_array0)) + id_array0 = (mass_array < 0.5) | (mass_array >= 120) + output_array[0][id_array0] = -99 + output_array[1][id_array0] = -1 - id_array1 = np.where((mass_array >= 0.5) & (mass_array < 9)) + id_array1 = (mass_array >= 0.5) & (mass_array < 9) output_array[0][id_array1] = self.Kalirai_mass(mass_array[id_array1]) output_array[1][id_array1]= codes['WD'] - id_array2 = np.where((mass_array >= 9) & (mass_array < 15)) + id_array2 = (mass_array >= 9) & (mass_array < 15) output_array[0][id_array2] = self.NS_mass(mass_array[id_array2]) output_array[1][id_array2] = codes['NS'] - id_array3_BH = np.where((mass_array >= 15) & (mass_array < 21.8) & (random_array > 75)) + id_array3_BH = (mass_array >= 15) & (mass_array < 21.8) & (random_array > 75) output_array[0][id_array3_BH] = self.BH_mass_low(mass_array[id_array3_BH]) output_array[1][id_array3_BH] = codes['BH'] - id_array3_NS = np.where((mass_array >= 15) & (mass_array < 21.8) & (random_array <= 75)) + id_array3_NS = (mass_array >= 15) & (mass_array < 21.8) & (random_array <= 75) output_array[0][id_array3_NS] = self.NS_mass(mass_array[id_array3_NS]) output_array[1][id_array3_NS] = codes['NS'] - id_array4 = np.where((mass_array >= 21.8) & (mass_array < 25.2)) + id_array4 = (mass_array >= 21.8) & (mass_array < 25.2) output_array[0][id_array4] = self.BH_mass_low(mass_array[id_array4]) output_array[1][id_array4] = codes['BH'] - id_array5 = np.where((mass_array >= 25.2) & (mass_array < 27.4)) + id_array5 = (mass_array >= 25.2) & (mass_array < 27.4) output_array[0][id_array5] = self.NS_mass(mass_array[id_array5]) output_array[1][id_array5] = codes['NS'] - id_array6 = np.where((mass_array >= 27.4) & (mass_array < 39.6)) + id_array6 = (mass_array >= 27.4) & (mass_array < 39.6) output_array[0][id_array6] = self.BH_mass_low(mass_array[id_array6]) output_array[1][id_array6] = codes['BH'] - id_array7 = np.where((mass_array >= 39.6) & (mass_array < 60)) + id_array7 = (mass_array >= 39.6) & (mass_array < 60) output_array[0][id_array7] = self.BH_mass_high(mass_array[id_array7], Z_array[id_array7]) output_array[1][id_array7] = codes['BH'] - id_array8 = np.where((mass_array >= 60) & (mass_array < 120)) - for i in range(0, len(id_array8[0])): - pBH = self.prob_BH_high(Z_array[id_array8][i]) - if random_array[id_array8][i] > 100*pBH: - output_array[0][id_array8[0][i]] = self.BH_mass_high(mass_array[id_array8][i], - Z_array[id_array8][i]) - output_array[1][id_array8[0][i]] = codes['BH'] - - else: - output_array[0][id_array8[0][i]] = self.NS_mass(mass_array[id_array8][i]) - output_array[1][id_array8[0][i]] = codes['NS'] + BH_or_NS = np.where((mass_array >= 60) & (mass_array < 120))[0] + pBH = self.prob_BH_high(Z_array[BH_or_NS]) + is_BH = random_array[BH_or_NS] > 100 * pBH + + id_array8 = BH_or_NS[is_BH] + id_array9 = BH_or_NS[~is_BH] + + # Assign BH masses and types for BH-forming indices + output_array[0][id_array8] = self.BH_mass_high(mass_array[id_array8], Z_array[id_array8]) + output_array[1][id_array8] = codes['BH'] + + # Assign NS masses and types for NS-forming indices + output_array[0][id_array9] = self.NS_mass(mass_array[id_array9]) + output_array[1][id_array9] = codes['NS'] + #this is where sam's janky fix for unphysical BH massses goes #any BH with mass less then 3 M_sun is reassigned as a NS #and given a mass from the NS mass dist instead - id_array9 = np.where((output_array[1] == codes['BH']) & (output_array[0] < 3.0)) - output_array[0][id_array9] = self.NS_mass(mass_array[id_array9]) - output_array[1][id_array9] = codes['NS'] + id_array10 = (output_array[1] == codes['BH']) & (output_array[0] < 3.0) + output_array[0][id_array10] = self.NS_mass(mass_array[id_array10]) + output_array[1][id_array10] = codes['NS'] - return(output_array) + return output_array diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 14ea7fb3..0595bfbe 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -4,7 +4,7 @@ # Original code was taken from libimf package written by Jan Pflamm-Altenburg # and has been modified only marginally. The libimf code was licensed under # a GNU General Public License. -# +# # When I use this code, I should cite Pflamm-Altenburg & Kroupa 2006 # # Unfortunately, the code was almost completely un-commented, so all @@ -22,47 +22,82 @@ class IMF(object): """ - The IMF base class. The mass sampling and multiplicity - implementation is here. - - Notes - ----- - Code author: J. Lu. - - Original code was taken from libimf package written by Jan Pflamm-Altenburg - (`Pflamm-Altenburg & Kroupa 2006 `_) - and has been modified only marginally, though more convinient and general purpose - functions have been added. The libimf code was licensed under - a GNU General Public License. + The IMF base class. The mass sampling and multiplicity + implementation is here. Parameters ---------- massLimits : 2 element array; optional - Define the minimum and maximum stellar masses in the IMF, in - solar masses. First element is taken as the min, second element + Define the minimum and maximum stellar masses in the IMF, in + solar masses. First element is taken as the min, second element the max (e.g. `massLimits` = [min_mass, max_mass]). multiplicity : Multiplicity object or None - If None, no multiplicity is assumed. Otherwise, use + If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. + + seed : int, optional + Seed for the random generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, by default None. + Behavior: + :: + + imf = IMF(..., seed=42) + result1 = imf.generate_cluster() + result2 = imf.generate_cluster() + imf = IMF(..., seed=42) + result3 = imf.generate_cluster() + result4 = imf.generate_cluster() + + result1==result3, result2==result4, but result1≠result2, result3≠result4. + This is the same behavior as + :: + + rng = np.random.default_rng(seed=42) + result1 = rng.random(1) + result2 = rng.random(1) + rng = np.random.default_rng(seed=42) + result3 = rng.random(1) + result4 = rng.random(1) + + If identical output is desired over each run, the random state can be reset before running the function, e.g. + :: + + imf.rng = np.random.default_rng(seed=42) + result1 = imf.generate_cluster() + imf.rng = np.random.default_rng(seed=42) + result2 = imf.generate_cluster() + + In this case, result1==result2 + + Notes + ----- + Code author: J. Lu. + + Original code was taken from libimf package written by Jan Pflamm-Altenburg + (`Pflamm-Altenburg & Kroupa 2006 `_) + and has been modified only marginally, though more convinient and general purpose + functions have been added. The libimf code was licensed under + a GNU General Public License. + """ - def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None): + def __init__(self, massLimits=np.array([0.01,150]), multiplicity=None, seed=None): self._multi_props = multiplicity - self._mass_limits = massLimits + self._mass_limits = np.atleast_1d(massLimits) + self.rng = np.random.default_rng(seed) - if multiplicity == None: - self.make_multiples = False - else: + if multiplicity: self.make_multiples = True - + else: + self.make_multiples = False + return - - def generate_cluster(self, totalMass, seed=None): + def generate_cluster(self, totalMass): """ Generate a cluster of stellar systems with the specified IMF. - + Randomly sample from an IMF with specified mass limits until the desired total mass is reached. The maximum stellar mass is not allowed to exceed the total cluster mass. @@ -79,23 +114,21 @@ def generate_cluster(self, totalMass, seed=None): totalMass : float The total mass of the cluster (including companions) in solar masses. - seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None - Returns ------- masses : numpy float array - List of primary star masses. + Array of primary star masses. isMultiple : numpy boolean array - List of booleans with True for each primary star that is in a multiple + Array of booleans with True for each primary star that is in a multiple system and False for each single star. - companionMasses : numpy float array - List of - + companionMasses : numpy masked array + Masked array of companion masses. Each row corresponds to a primary star, and each column corresponds to a companion. The mask is True for entries that are not valid companions (e.g. for single stars or for companions that are below the minimum mass limit). + + systemMasses : numpy float array + Array of total system masses (primary + companions) for each primary star. + """ initial_mass_limit = self._mass_limits[-1] @@ -114,7 +147,8 @@ def generate_cluster(self, totalMass, seed=None): # Generate output arrays. masses = np.array([], dtype=float) isMultiple = np.array([], dtype=bool) - compMasses = np.array([], dtype=object) + # compMasses = {} # Hashmap for index -> compMasses for faster lookup + compMasses = [] systemMasses = np.array([], dtype=float) # Loop through and add stars to the cluster until we get to @@ -122,51 +156,45 @@ def generate_cluster(self, totalMass, seed=None): totalMassTally = 0 loopCnt = 0 - # Set the random seed, if desired - if seed: - np.random.seed(seed=seed) - + # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = np.random.rand(int(newStarCount)) - + uniX = self.rng.random(newStarCount.astype(int)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) - + # Testing for Nans produced in masses test = np.isnan(newMasses) if np.sum(test) > 0: raise ValueError('Nan detected in cluster mass') - + # Dealing with multiplicity - if self._multi_props != None: - newCompMasses = np.empty((len(newMasses),), dtype=object) - newCompMasses.fill([]) - + if self._multi_props: + # newCompMasses = np.empty((len(newMasses),), dtype=object) + # newCompMasses.fill([]) # Determine the multiplicity of every star MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - - newIsMultiple = np.random.rand(int(newStarCount)) < MF - # Copy over the primary masses. Eventually add the companions. - newSystemMasses = newMasses.copy() + newIsMultiple = self.rng.random(newStarCount.astype(int)) < MF # Function to calculate multiple systems more efficiently - newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newCompMasses, - newSystemMasses, newIsMultiple, - CSF, MF) - + # start_calc = time.time() + newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF) + # end_calc = time.time() + # print('Time taken for calc_multi: ', end_calc - start_calc) newTotalMassTally = newSystemMasses.sum() isMultiple = np.append(isMultiple, newIsMultiple) systemMasses = np.append(systemMasses, newSystemMasses) - compMasses = np.append(compMasses, newCompMasses) + compMasses.append(newCompMasses) + else: newTotalMassTally = newMasses.sum() - + # end_while = time.time() + # print('Time taken for while loop: ', end_while - start_while) # Append to our primary masses array masses = np.append(masses, newMasses) - + if (loopCnt >= 0): log.info('sample_imf: Loop %d added %.2e Msun to previous total of %.2e Msun' % (loopCnt, newTotalMassTally, totalMassTally)) @@ -174,9 +202,28 @@ def generate_cluster(self, totalMass, seed=None): totalMassTally += newTotalMassTally newStarCount = mean_number * 0.1 # increase by 20% each pass loopCnt += 1 - + # Make a running sum of the system masses if self._multi_props: + # Concatenate the companion masses + if len(compMasses) > 1: + max_cols = max(compMass.shape[1] for compMass in compMasses) + + # Pad each array to have the same number of columns + padded_arrays = [ + np.ma.masked_all((compMass.shape[0], max_cols)) for compMass in compMasses + ] + + for i, compMass in enumerate(compMasses): + padded_arrays[i][:, :compMass.shape[1]] = compMass + + # Vertically stack the padded arrays + compMasses = np.ma.vstack(padded_arrays) + + else: + compMasses = compMasses[0] + + # Make a running sum of the system masses massCumSum = systemMasses.cumsum() else: massCumSum = masses.cumsum() @@ -198,77 +245,81 @@ def generate_cluster(self, totalMass, seed=None): self._mass_limits[-1] = initial_mass_limit return (masses, isMultiple, compMasses, systemMasses) - - def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, MF): + + def calc_multi(self, newMasses, newIsMultiple, CSF, MF): """ Helper function to calculate multiples more efficiently. - We will use array operations as much as possible + We will use array operations as much as possible. + Uses Fontanive+18 parameters for brown dwarf masses + (M <= 0.08 M_sun) while keeping default parameters for + all other stellar primaries. """ - # Identify multiple systems, calculate number of companions for - # each - idx = np.where(newIsMultiple == True)[0] - n_comp_arr = 1 + np.random.poisson((CSF[idx] / MF[idx]) - 1) - if self._multi_props.companion_max == True: - too_many = np.where(n_comp_arr > self._multi_props.CSF_max)[0] - n_comp_arr[too_many] = self._multi_props.CSF_max - primary = newMasses[idx] + # Copy over the primary masses. Eventually add the companions. + newSystemMasses = newMasses.copy() + + # Identify multiple systems, calculate number of companions for each + multiple_idx = np.where(newIsMultiple)[0] + comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) + if self._multi_props.companion_max: + too_many = np.where(comp_nums > self._multi_props.CSF_max)[0] + comp_nums[too_many] = self._multi_props.CSF_max + primary = newMasses[multiple_idx] + + # limit BD primaries to 1 companion (Fontanive+18) + bd_mask = primary <= 0.08 + comp_nums[bd_mask & (comp_nums > 1)] = 1 # We will deal with each number of multiple system independently. This is # so we can put in uniform arrays in _multi_props.random_q. - num = np.unique(n_comp_arr) - for ii in num: - tmp = np.where(n_comp_arr == ii)[0] - - if ii == 1: - # Single companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp))) - - # Calculate mass of companion - m_comp = q_values * primary[tmp] - - # Only keep companions that are more than the minimum mass. Update - # compMasses, newSystemMasses, and newIsMultiple appropriately - good = np.where(m_comp >= self._mass_limits[0])[0] - for jj in good: - compMasses[idx[tmp[jj]]] = np.transpose([m_comp[jj]]) - newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]] - - bad = np.where(m_comp < self._mass_limits[0])[0] - newIsMultiple[idx[tmp[bad]]] = False - else: - # Multple companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp), ii)) + comp_unique = np.unique(comp_nums) + comp_indices = [np.where(comp_nums == i)[0] for i in comp_unique] + if np.any(newIsMultiple): + compMasses = np.zeros((len(newMasses), max(comp_unique))) + else: + compMasses = np.zeros((len(newMasses), 1)) + + for comp_num, comp_index in zip(comp_unique, comp_indices): + prim_subset = primary[comp_index] + bd_sub_mask = prim_subset <= 0.08 + star_sub_mask = ~bd_sub_mask + + q_values = np.empty((len(comp_index), comp_num)) + + # Stellar primaries: use default Duchene & Kraus distribution + if np.any(star_sub_mask): + q_values[star_sub_mask] = self._multi_props.random_q(self.rng.random((star_sub_mask.sum(), comp_num))) - # Calculate masses of companions - m_comp = np.multiply(q_values, np.transpose([primary[tmp]])) + # BD primaries: use Fontanive+18 power-law distribution + if np.any(bd_sub_mask): + b = 1.0 + 6.1 # gamma from Fontanive+18 + rand_vals = self.rng.random((bd_sub_mask.sum(), comp_num)) + q_values[bd_sub_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + + self._multi_props.q_min ** b) ** (1.0 / b) - # Update compMasses, newSystemMasses, and newIsMultiple appropriately - for jj in range(len(tmp)): - m_comp_tmp = m_comp[jj] - compMasses[idx[tmp[jj]]] = m_comp_tmp[m_comp_tmp >= self._mass_limits[0]] - newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]].sum() + m_comp = np.multiply(q_values, np.transpose([prim_subset])) + compMasses[multiple_idx[comp_index], :comp_num] = m_comp - # Double check for the case when we drop all companions. - # This happens a lot near the minimum allowed mass. - if len(compMasses[idx[tmp[jj]]]) == 0: - newIsMultiple[idx[tmp[jj]]] = False + # Mask out companions below the minimum mass + compMasses = np.ma.MaskedArray(compMasses, mask=compMasses < self._mass_limits[0]) + newSystemMasses[multiple_idx] += compMasses[multiple_idx].sum(axis=1) + newIsMultiple = np.any(~compMasses.mask, axis=1) return compMasses, newSystemMasses, newIsMultiple - - + + class IMF_broken_powerlaw(IMF): """ Initialize a multi-part power-law with N parts. Each part of the power-law is described with a probability density function: - P(m) \propto m ** power[n] + P(m) ∠m ** power[n] for mass_limits[n] < m <= mass_limits[n+1]. Parameters ---------- mass_limits : numpy array - Array of length (N + 1) with lower and upper limits of + Array of length (N + 1) with lower and upper limits of the power-law segments. powers : numpy array @@ -276,28 +327,23 @@ class IMF_broken_powerlaw(IMF): power-law segment. multiplicity : Multiplicity object or None - If None, no multiplicity is assumed. Otherwise, use + If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ - def __init__(self, mass_limits, powers, multiplicity=None): + def __init__(self, mass_limits, powers, multiplicity=None, seed=None): + super().__init__(massLimits=mass_limits, multiplicity=multiplicity, seed=seed) + powers = np.atleast_1d(powers) if len(mass_limits) != len(powers) + 1: msg = 'Incorrect specification of multi-part powerlaw.\n' msg += ' len(massLimts) != len(powers)+1\n' - msg += ' len(massLimits) = \n' + len(massLimits) - msg += ' len(powers) = \n' + len(powers) - - raise RuntimeException(msg) + msg += ' len(massLimits) = \n' + str(len(mass_limits)) + msg += ' len(powers) = \n' + str(len(powers)) - self._mass_limits = np.atleast_1d(mass_limits) + raise RuntimeError(msg) + mass_limits = np.atleast_1d(mass_limits) self._m_limits_low = mass_limits[0:-1] self._m_limits_high = mass_limits[1:] - self._powers = powers - self._multi_props = multiplicity - - if multiplicity == None: - self.make_multiples = False - else: - self.make_multiples = True + self._powers = np.atleast_1d(powers) # Calculate the coeffs to make the function continuous nterms = len(self._powers) @@ -326,7 +372,7 @@ def xi(self, m): xi - probability of measuring that mass. """ returnFloat = type(m) == float - + m = np.atleast_1d(m) # Temporary arrays @@ -343,7 +389,7 @@ def xi(self, m): # Maybe we are all done? if len(idx) == 0: break - + m_tmp = m[idx] aux_tmp = aux[idx] @@ -356,7 +402,7 @@ def xi(self, m): z *= delta(m - self._m_limits_high[i]) xi = self.k * z * y - + if returnFloat: return xi[0] else: @@ -384,7 +430,7 @@ def m_xi(self, m): # Maybe we are all done? if len(idx) == 0: break - + m_tmp = m[idx] aux_tmp = aux[idx] @@ -397,7 +443,7 @@ def m_xi(self, m): z *= delta(m - self._m_limits_high[i]) mxi = self.k * z * y - + if returnFloat: return mxi[0] else: @@ -405,23 +451,23 @@ def m_xi(self, m): def getProbabilityBetween(self, massLo, massHi): - """Return the integrated probability between some low and high + """Return the integrated probability between some low and high mass value. """ return self.int_xi(massLo, massHi) def int_xi(self, massLo, massHi): - """Return the integrated probability between some low and high + """Return the integrated probability between some low and high mass value. """ return self.prim_xi(massHi) - self.prim_xi(massLo) - + def getMassBetween(self, massLo, massHi): - """Return the integrated mass between some low and high + """Return the integrated mass between some low and high mass value. """ return self.int_mxi(massLo, massHi) - + def int_mxi(self, massLo, massHi): """Return the integrated total mass between some low and high stellar mass value. Be sure to normalize the IMF instance beforehand. @@ -443,7 +489,7 @@ def prim_xi(self, a): t3 = prim_power(self._m_limits_low, self._powers) y1 = (t1 * (t2 - t3)).sum() - t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) + t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) t1 *= self.coeffs t2 = prim_power(a[i], self._powers) t3 = prim_power(self._m_limits_low, self._powers) @@ -461,7 +507,7 @@ def prim_mxi(self, a): Helper function """ returnFloat = type(a) == float - + a = np.atleast_1d(a) val = np.zeros(len(a), dtype=float) @@ -470,8 +516,8 @@ def prim_mxi(self, a): t2 = prim_power(self._m_limits_high, self._powers+1) t3 = prim_power(self._m_limits_low, self._powers+1) y1 = (t1 * (t2 - t3)).sum() - - t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) + + t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) t1 *= self.coeffs t2 = prim_power(a[i], self._powers+1) t3 = prim_power(self._m_limits_low, self._powers+1) @@ -491,7 +537,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): """ self.k = 1.0 self.Mcl = Mcl - + if Mmax == None: Mmax = self._m_limits_high[-1] @@ -501,7 +547,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): if Mmax > Mcl: Mmax = Mcl - + if Mmax > self._m_limits_high[-1]: Mmax = self._m_limits_high[-1] @@ -510,7 +556,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): self.norm_Mmin = Mmin self.norm_Mmax = Mmax - + self.k = Mcl / self.int_mxi(self.norm_Mmin, self.norm_Mmax) self.lamda = self.int_xi_cl(self._m_limits_low[0], self._mass_limits) @@ -529,7 +575,7 @@ def norm_cl_wk04(self, Mcl, Mmax=None, Mmin=None): if Mmax > Mcl: Mmax = Mcl - + if Mmax > self._m_limits_high[-1]: Mmax = self._m_limits_high[-1] @@ -597,21 +643,21 @@ def dice_star_cl(self, r): returnFloat = type(r) == float r = np.atleast_1d(r) # Make sure it is an array - x = r * self.lamda[-1] - y = np.zeros(len(r), dtype=float) - z = np.ones(len(r), dtype=float) + x = r * self.lamda[-1] + y = np.zeros_like(r) + z = np.ones_like(r) # Loop through the different parts of the power law. for i in range(self.nterms): #-----For i = 0 --> n, where n is the number of intervals aux = x - self.lamda[i] #---Should this be i - 1? - + # Only continue for those entries that are in later segments - idx = np.where(aux >= 0)[0] + idx = aux >= 0 # Maybe we are all done? - if len(idx) == 0: + if sum(idx) == 0: break - + x_tmp = x[idx] aux_tmp = aux[idx] @@ -687,14 +733,31 @@ class Weidner_Kroupa_2004(IMF_broken_powerlaw): Mass range is 0.01 M_sun - inf M_sun. """ def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf]) + massLimits = np.array([0.01, 0.08, 0.5, 1, 120]) powers = np.array([-0.3, -1.3, -2.3, -2.35]) IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) +class Salpeter_Kirkpatrick_2024(IMF_broken_powerlaw): + """ + Define combined IMF from Kirkpatrick (2024) and Salpeter (1955) to allow + inclusion of the brown dwarf mass range. + Mass range: + * 0.01 M_sun - 8 M_sun: Kirkpatrick 2024 + `_. + * 8 M_sun - 120 M_sun: Salpeter 1955 + `_. + """ + def __init__(self, multiplicity=None): + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) + + IMF_broken_powerlaw.__init__(self, massLimits, powers, + multiplicity=multiplicity) + ################################################## -# +# # Generic functions -- see if we can move these up. # ################################################## @@ -714,11 +777,11 @@ def prim_power(m, power): power = np.repeat(power, len(m)) z = 1.0 + power - val = (m**z) / z - - val[power == -1] = np.log(m[power == -1]) + val = np.empty_like(m) + valid_idx = power != -1 + val[valid_idx] = (m[valid_idx]**z[valid_idx]) / z[valid_idx] + val[~valid_idx] = np.log(m[~valid_idx]) - if returnFloat: return val[0] else: @@ -726,7 +789,7 @@ def prim_power(m, power): def inv_prim_power(x, power): """ - returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case + returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case when power == -1. """ returnFloat = (type(x) == float) and (type(power) == float) @@ -740,22 +803,19 @@ def inv_prim_power(x, power): power = np.repeat(power, len(x)) if x.shape != power.shape: - pdb.set_trace() - + raise ValueError('spisea.imf.inv_prim_power: Dimension mismatch, x and power must have the same shape') + z = 1.0 + power - val = (z * x)**(1.0 / z) - - #--------------BUG CHECK---------------------# - # This line doesn't make sense if x is an N-element array and - # power is just a 1-element array, which it appears to be for - # imf.generate_cluster - val[power == -1] = np.exp(x[power == -1]) - #-----------------------------------------------# + val = np.empty_like(x) + valid_idx = power != -1 + val[valid_idx] = (z[valid_idx] * x[valid_idx])**(1.0 / z[valid_idx]) + val[~valid_idx] = np.exp(x[~valid_idx]) + if returnFloat: return val[0] else: return val - + def log_normal(m, mean_logm, sigma_logm): returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ @@ -767,7 +827,7 @@ def log_normal(m, mean_logm, sigma_logm): z = np.log10(m) - mean_logm val = np.exp(-z**2 / (2.0 * sigma_logm**2)) / m - + if returnFloat: return val[0] else: @@ -783,7 +843,7 @@ def prim_log_normal(m, mean_logm, sigma_logm): mu = (np.log10(m) - mean_logm) / (1.4142135623731 * sigma_logm) val = 2.88586244942136 * sigma_logm * error(mu) - + if returnFloat: return val[0] else: @@ -796,10 +856,10 @@ def inv_prim_log_normal(x, mean_logm, sigma_logm): m = np.atleast_1d(m) mean_logm = np.atleat_1d(mean_logm) sigma_logm = np.atleat_1d(sigma_logm) - + mu = inv_error(0.346516861952484 * x / sigma_logm) val = 10.0**(1.4142135623731 * sigma_logm * mu + mean_logm) - + if returnFloat: return val[0] else: @@ -815,7 +875,7 @@ def mlog_normal(x, mean_logm, sigma_logm): z = np.log10(m) - mean_logm val = np.exp(-z**2 / (2.0 * sigma_logm**2)) - + if returnFloat: return val[0] else: @@ -836,12 +896,12 @@ def prim_mlog_normal(x, mean_logm, sigma_logm): val = error(eta) val *= 2.88586244942136 * sigma_logm * np.exp(2.30258509299405 * t1) - + if returnFloat: return val[0] else: return val - + def theta_closed(x): """ @@ -891,7 +951,7 @@ def delta(x): def gamma_closed(m, left, right): """ - + """ return theta_closed(m - left) * theta_closed(right - m) @@ -899,7 +959,7 @@ def gamma_closed(m, left, right): def error(x): x2 = x**2 ax2 = 0.140012288686666 * x2 - + val = np.sqrt(1.0 - np.exp(-x2*(1.27323954473516+ax2)/(1+ax2))) if x >=0: @@ -912,11 +972,10 @@ def inv_error(x): lnx2 = np.log(1.0 - x2) aux = 4.54688497944829 + (lnx2 / 2.0) y = -aux + np.sqrt(aux**2 - (lnx2 / 0.140012288686666)) - + val = np.sqrt(y) if x>=0: return y else: return -y - diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 0d28ced6..4258cb4c 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -1,6 +1,7 @@ import numpy as np import astropy.modeling from random import choice +from scipy.stats import truncnorm defaultMF_amp = 0.44 defaultMF_power = 0.51 @@ -41,6 +42,11 @@ class MultiplicityUnresolved(object): MF(mass) = MF_amp * (mass ** MF_power) + However, in the brown dwarf mass regime, it is currently recognized + that only binaries are possible, and the MF decreases dissimilarly + to higher masses (> 0.08 solar masses). The values for this range + are given by Aberasturi et al. (2014) and Fontanive et al. (2023). + **Companion Star Fraction** -- the expected number of companions in a multiple system. The companion star fraction (CSF) also changes with mass and this dependency can be described as @@ -52,7 +58,10 @@ class MultiplicityUnresolved(object): value, CSF_max. The actual number of companions is drawn from a Poisson distribution with an expectation value of CSF. - **Mass Ratio (Q)** -- The ratio between the companion star + In the brown dwarf regime we impose an assumption that only + binary systems are possible due to current literature trends. + + **Mass Ratio (Q)** -- The ratio between the companion star mass and primary star mass, Q = (m_comp / m_prim ) has a probability density function described by a powerlaw:: @@ -116,6 +125,9 @@ def multiplicity_fraction(self, mass): Given a star's mass, determine the probability that the star is in a multiple system (multiplicity fraction = MF). + Modified to allow binary fraction to decrease in brown dwarf regime. + Supported by Aberasturi et al. (2014) and Fontanive et al. (2018). + Parameters ---------- mass : float or numpy array @@ -133,6 +145,13 @@ def multiplicity_fraction(self, mass): if np.isscalar(mf): if mf > 1: mf = 1 + # physically override mf for brown dwarfs + if (mass <= 0.08) & (mass > 0.06): + mf = 0.16 + if (mass <= 0.06) & (mass > 0.02): + mf = 0.08 + if (mass < 0.02): + mf = 0 else: mf[mf > 1] = 1 @@ -141,7 +160,8 @@ def multiplicity_fraction(self, mass): def companion_star_fraction(self, mass): """ Given a star's mass, determine the average number of - companion stars (companion star fraction = CSF). + companion stars (companion star fraction = CSF). For + brown dwarfs we impose a hard limit of one companion. Parameters ---------- @@ -160,8 +180,12 @@ def companion_star_fraction(self, mass): if np.isscalar(csf): if csf > self.CSF_max: csf = self.CSF_max + if (mass <= 0.08): + csf = self.multiplicity_fraction(mass) else: csf[csf > self.CSF_max] = self.CSF_max + bd = mass <= 0.08 + csf[bd] = self.multiplicity_fraction(mass[bd]) return csf @@ -198,6 +222,10 @@ def random_companion_count(self, x, CSF, MF): """ Helper function: calculate number of companions. """ + # bd stipulation since mf=0 + if MF <= 0: + return 0 + n_comp = 1 + np.random.poisson((CSF / MF) - 1) if self.companion_max == True: @@ -210,7 +238,9 @@ class MultiplicityResolvedDK(MultiplicityUnresolved): """ Sub-class of MultiplicityUnresolved that adds semimajor axis and eccentricity information for multiple objects from distributions described in Duchene and Kraus 2013 - + + For brown dwarf regime, mean separation and std are given by Fontanive et al. (2018). + Parameters -------------- a_amp: float, optional @@ -246,32 +276,58 @@ def log_semimajoraxis(self, mass): Generate the semimajor axis for a given mass. The mean and standard deviation of a given mass are determined by fitting the data from fitting the semimajor axis data as a function of mass in table 1 of Duchene and Kraus 2013. Then a random semimajor axis is drawn from a log normal distribution with that mean and standard deviation. - + + The brown dwarf range is covered by mass-dependent scaling of both the characteristic separation and dispersion + matching trends described in Fontanive et al. (2018). + Parameters ---------- - mass : float - Mass of primary star + mass : array-like + Mass array of primary star Returns ------- - log_semimajoraxis : float + log_semimajoraxis : array-like Log of the semimajor axis/separation between the stars in units of AU """ - a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, alpha_1=self.a_slope1, alpha_2=self.a_slope2) - log_a_mean = np.log10(a_mean_func(mass)) #mean log(a) + mass = np.atleast_1d(mass) + logm = np.log10(mass) + + # Stellar mean and std (Duchene & Kraus 2013) + a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, + alpha_1=self.a_slope1, alpha_2=self.a_slope2) + log_a_mean_star = np.log10(a_mean_func(mass)) # mean log(a) log_a_std_func = astropy.modeling.models.Linear1D(slope=self.a_std_slope, intercept=self.a_std_intercept) - log_a_std = log_a_std_func(np.log10(mass)) #sigma_log(a) - if mass >= 2.9: - log_a_std = log_a_std_func(np.log10(2.9)) #sigma_log(a) - if log_a_std < 0.1: - log_a_std = 0.1 - - log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) - while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: #AU - log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) - + log_a_std_star = log_a_std_func(logm) # sigma_log(a) + log_a_std_star[mass >= 2.9] = log_a_std_func(np.log10(2.9)) # sigma_log(a) + log_a_std_star = np.clip(log_a_std_star, 0.1, None) + + # BD mean and std (Fontanive+18): interpolated over substellar range + log_a_mean_bd = np.interp( + logm, + [np.log10(0.01), np.log10(0.08)], + [np.log10(2.5), np.log10(8.0)] + ) + log_a_std_bd = np.interp( + logm, + [np.log10(0.01), np.log10(0.08)], + [0.25, 0.5] + ) + + # Sigmoid blend: smoothly transitions from BD to stellar regime at 0.08 M_sun + w = 1.0 / (1.0 + np.exp(-(logm - np.log10(0.08)) / 0.15)) + log_a_mean = (1 - w) * log_a_mean_bd + w * log_a_mean_star + log_a_std = (1 - w) * log_a_std_bd + w * log_a_std_star + + # Trunc normal distribution between log10(0.01) AU and log10(2000) AU + log_a_lower = np.log10(0.01) + log_a_upper = np.log10(2000) + a_lower_std = (log_a_lower - log_a_mean) / log_a_std + a_upper_std = (log_a_upper - log_a_mean) / log_a_std + + log_semimajoraxis = truncnorm.rvs(a_lower_std, a_upper_std, loc=log_a_mean, scale=log_a_std) return log_semimajoraxis - + def random_e(self, x): """ Generate random eccentricity from the inverse of the CDF where the PDF is f(e) = 2e from Duchene and Kraus 2013 diff --git a/spisea/imf/tests/__init__.py b/spisea/imf/tests/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/spisea/imf/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/spisea/merge_models.py b/spisea/merge_models.py new file mode 100644 index 00000000..3eded9ea --- /dev/null +++ b/spisea/merge_models.py @@ -0,0 +1,2514 @@ +### Code used to incorporate new brown dwarf evolution/atmosphere models into pre-existing code. +### Messy, ask Caitlin Begbie if there are questions + +import math +import logging +from numpy import genfromtxt +import numpy as np +import os +import glob +import pandas as pd +import pdb +import warnings +from astropy.table import Table, vstack, Column +from scipy import interpolate +import pylab as py +from spisea.utils import objects +from scipy.interpolate import RegularGridInterpolator +from spisea import exceptions +import re +import matplotlib +import matplotlib.pyplot as plt +from spisea import atmospheres + +logger = logging.getLogger('evolution') + +# Fetch root directory of evolution models. +try: + models_dir = os.environ['SPISEA_MODELS'] + models_dir += '/evolution/' +except KeyError: + warnings.warn("SPISEA_MODELS is undefined; functionality " + "will be SEVERELY crippled.") + models_dir = '' + +# Code to deconstruct current Phillips2020 evolution files and reconstruct iso files based on age + +# Define input and output directories +input_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/z00_mass' +output_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/z00_age' +os.makedirs(output_dir, exist_ok=True) + +# Combine data from all files +combined_data = [] + +for filename in os.listdir(input_dir): + if filename.endswith('.txt'): + file_path = os.path.join(input_dir, filename) + table = Table.read(file_path, format='ascii') + combined_data.append(table) + +# Concatenate all data into a single table +all_data = combined_data[0] + +# Add the remaining tables to the main table +for table in combined_data[1:]: + all_data = vstack([all_data, table]) # Use vstack to stack tables + +# Group data by age +ages = np.unique(all_data['Age']) + +# Iterate over each unique age and filter data +for age in ages: + age_group = all_data[all_data['Age'] == age] + output_path = os.path.join(output_dir, f"ATMO_{age}.txt") + + # Save each age group as a .txt file + age_group.write(output_path, format='ascii', overwrite=True) + +print(f"New files created in: {output_dir}") + +# Code to eradicate duplicate lines seen in above code +"""new_input_dir = output_dir + +for filename2 in os.listdir(new_input_dir): + if filename2.endswith('.txt'): + file_path2 = os.path.join(new_input_dir, filename2) + + # Remove duplicates before loading as a table + with open(file_path2, 'r') as f: + lines = f.readlines() + + header = lines[0] + data_lines = [line for line in lines[1:] if not line.startswith(header.split()[0])] + + with open(file_path2, 'w') as f: + f.write(header) + f.writelines(data_lines) + + # Now process with Astropy Table + table2 = Table.read(file_path2, format='ascii') + unique_table2 = unique(table2) + unique_table2.write(file_path2, format='ascii', overwrite=True) + +print('files overwritten successfully!') + + +# Reformatting files to match Parsec +def reformat(): + for file in os.listdir(output_dir): + if file.endswith('.txt'): + new_fp = os.path.join(output_dir, file) + + # Add metallicity column of all 0.0 + r_table = Table.read(new_fp, format='ascii') + r_table.add_column(0.0, name='Z', index=0) + + # Duplicate mass to include current mass column + r_table.add_column(r_table['Mass'], name='Mass_current') + + # Update age column to make it log scale + r_table['Age'] = np.log10(r_table['Age'] * 1e9) #originally in Gyr + + # Update Teff column to make it log scale + r_table['Teff'] = np.log10(r_table['Teff']) + + # Reorder columns to match Parsec + new_order = ['Z', 'Age', 'Mass', 'Mass_current', 'Luminosity', 'Teff', 'Gravity', 'Gaia_Gbp', 'Gaia_G', 'Gaia_Grp'] + t_new = r_table[new_order] + t_new.write(new_fp, format='ascii', overwrite=True) + + print(f"{file} reformatted successfully!") + + return + +# Transform reformatted .txt files to iso fits files +def ATMO_to_iso(): + i_dir = output_dir + o_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso' #output directory SPISEA will pull from + + for file in os.listdir(i_dir): + if file.endswith('.txt'): + fp = os.path.join(i_dir, file) + + try: + age_str = file.split('_')[1].split('.txt')[0] + age = float(age_str) + log_age = np.log10(age * 1e9) + except (IndexError, ValueError): + print(f"{file} cannot be processed.") + continue + + try: + # load in .txt file as ascii table + table = Table.read(fp, format='ascii') + except Exception as e: + print(f"{fp} could not be read: {e}") + continue + + #create output file name + o_file = os.path.join(o_dir, f'iso_{log_age}.fits') + + try: + # write to output directory as fits file + table.write(o_file, format='fits', overwrite=True) + print(f"{o_file} created successfully!") + except Exception as e: + print(f"{o_file} could not be generated: {e}") + continue + + return + + +""" +### CODE TO UNPACK MARLEY FILES ### +def Marley_deconstruct(): + """ + Code to deconstruct big Marley files into individual age files + """ + in_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/Marley_age' + out_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso' + + # Set path to each file + for filename in os.listdir(in_dir): + file_path = os.path.join(in_dir, filename) + + # Extract metallicity from filename + match = re.search(r'nc([+-]\d+\.\d+)_co', filename) + metallicity = match.group(1) if match else "unknown" + + # Deconstruct initial format of single column table + with open(file_path, 'r') as f: + lines = f.readlines() + header = ['Age', 'Mass', 'log_L', 'Teff', 'logg', 'Radius'] + data = [line.strip().split() for line in lines[1:]] + data = [row for row in data if len(row) > 1] + table = Table(rows=data, names=header) + + # Find unique ages to create iso age_based files + ages = np.unique(table['Age']) + + for age in ages: + age_group = table[table['Age'] == age] + out_path = os.path.join(out_dir, f"Marley_{metallicity}_{age}.txt") + + # Save each age group as a .txt file + age_group.write(out_path, format='ascii', overwrite=True) + + print(f"New files created in: {out_dir}") + + return + +# Reformatting files to match Parsec +def reformat_Marley(): + age_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/age_txt' + for file in os.listdir(age_dir): + if file.endswith('.txt'): + new_fp = os.path.join(age_dir, file) + + # Add metallicity column + table = Table.read(new_fp, format='ascii') + + # Extract metallicity from filename + match = re.search(r'Marley_([+-]?\d+\.\d+)_\d+\.\d+.txt', file) + metallicity = float(match.group(1)) if match else np.nan + + # Replace or add metallicity column + if 'Z' in table.colnames: + table.replace_column('Z', [metallicity] * len(table)) + else: + table.add_column([metallicity] * len(table), name='Z', index=0) + + # Duplicate mass to include current mass column + #table.add_column(table['Mass'], name='Mass_current') + + # Update age column to make it log scale + #table['Age'] = np.log10(table['Age'] * 1e9) #originally in Gyr + + # Update Teff column to make it log scale + #table['Teff'] = np.log10(table['Teff']) + + # Reorder columns to match Parsec + new_order = ['Z', 'Age', 'Mass', 'Mass_current', 'log_L', 'Teff', 'logg', 'Radius'] + t_new = table[new_order] + t_new.write(new_fp, format='ascii', overwrite=True) + + print(f"{file} reformatted successfully!") + + return + +def Marley_to_iso(): + in_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/age_txt' + out_dir_1 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zm05' + out_dir_2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zp00' + out_dir_3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zp05' + + for file in os.listdir(in_dir): + if file.endswith('.txt'): + fp = os.path.join(in_dir, file) + + try: + parts = file.split('_') + metal_str = parts[1] # Second part is metallicity + age_str = parts[2].split('.txt')[0] + + age = float(age_str) + metallicity = float(metal_str) + log_age = np.log10(age * 1e9) + except (IndexError, ValueError): + print(f"{file} cannot be processed.") + continue + + try: + # load in .txt file as ascii table + table = Table.read(fp, format='ascii') + except Exception as e: + print(f"{fp} could not be read: {e}") + continue + + # Determine output directory based on metallicity + if metallicity == -0.5: + out_dir = out_dir_1 + elif metallicity == 0.0: + out_dir = out_dir_2 + elif metallicity == 0.5: + out_dir = out_dir_3 + else: + print(f"Skipping {file}: Unexpected metallicity value ({metallicity}).") + continue + + # Create output filename + out_file = os.path.join(out_dir, f'iso_{log_age}.fits') + + try: + # Write to output directory as FITS file + table.write(out_file, format='fits', overwrite=True) + print(f"{out_file} created successfully!") + except Exception as e: + print(f"Error writing {out_file}: {e}") + continue + + return + +### CREATING MERGED EVO MODEL WITH PHILLIPS ### +def get_phillips_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Phillips2020/iso/' + metSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020 metallicities not supported yet') + return + rootDir += metSuffix + + # List available isochrone files + available_ages = [] + for filename in os.listdir(rootDir): + if filename.startswith('iso_') and filename.endswith('.fits'): + age_str = filename.split('_')[1].split('.fits')[0] # Extract the age part from filename + available_ages.append(float(age_str)) + + # Find the closest available age from Phillips + closest_age = min(available_ages, key=lambda x: abs(x - logAge)) + print(closest_age) + + # Load the corresponding isochrone + isoFile = rootDir + f'iso_{closest_age}.fits' + print(f"Loading isochrone for logAge = {closest_age}") + + # Check to see if isochrone exists + if not os.path.exists(isoFile): + print( f'Phillips isochrone for logAge = {closest_age} does\'t exist') + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + print("Available Columns:", data.keys()) + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[5]] # K + logL = data[cols[4]] # L_sun + logg = data[cols[6]] + mass_current = data[cols[3]] # Matches initial mass -- BD masses assumed to not change w current models + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def get_Baraffe15_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Baraffe+15 isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + ALSO interpolates isochrone to a finer mass grid. + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Baraffe15/iso/' + if metallicity != 'solar': + print( 'Non-solar Baraffe+15 metallicities not supported yet') + return + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.fits' % logAge + if not os.path.exists(isoFile): + print( 'Baraffe+15 isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + mass = data['Mass'] #Note: this is initial mass, in M_sun + logT = np.log10(data['Teff']) # K + logL = data['logL'] # L_sun + logg = data['logG'] + + # Interpolate isochrone to finer mass grid. Spacing + # is one model every 0.02 M_sun down to 0.2 M_sun, then + # one model every 0.005 M_sun down to 0.07 M_sun + new_masses0 = np.arange(min(mass), 0.1, 0.005) + new_masses2 = np.arange(0.1, max(mass), 0.02) + + #new_masses = np.concatenate((new_masses0, new_masses1, new_masses2)) + new_masses = np.concatenate((new_masses0, new_masses2)) + + # Build interpolators in linear space + f_logT = interpolate.interp1d(mass, 10**logT, kind='linear') + f_logL = interpolate.interp1d(mass, 10**logL, kind='linear') + f_logg = interpolate.interp1d(mass, 10**logg, kind='linear') + + # Do interpolation, convert back to logspace + logT_interp = np.log10(f_logT(new_masses)) + logL_interp = np.log10(f_logL(new_masses)) + logg_interp = np.log10(f_logg(new_masses)) + + # Hack, add new mass_current and phase columns. + mass_current = np.array(new_masses) + phase = np.ones(len(new_masses), dtype=int) + + # Test the interpolation, if desired + test = False + if test: + py.figure(1, figsize=(10,10)) + py.clf() + py.plot(mass, logT, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logT_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logT') + py.legend() + + py.figure(2, figsize=(10,10)) + py.clf() + py.plot(mass, logL, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logL_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logL') + py.legend() + + py.figure(3, figsize=(10,10)) + py.clf() + py.plot(mass, logg, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logg_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logg') + py.legend() + + pdb.set_trace() + + # Make isochrone + obj = objects.DataHolder() + obj.mass = new_masses + obj.logT = logT_interp + obj.logg = logg_interp + obj.logL = logL_interp + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def merge_isochrone_baraffe_phillips(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Phillips 2020 models. Will take + 100% Phillips2020 between 0.01 - 0.07 M_sun, transition between + 0.07 - 0.075 M_sun, and take 100% Baraffe from 0.075 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Phillips isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPhillips = get_phillips_isochrone(logAge, metallicity=metallicity) + + # Identify M >= 0.075 M_sun in Baraffe and M <= 0.07 M_sun in Phillips + good_b = np.where(isoBaraffe.mass >= 0.075) + good_p = np.where(isoPhillips.mass <= 0.07) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Phillips over this range + mid_mass = np.arange(0.07, 0.075, 0.002) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Phillips to build from. + # Baraffe has identical sampling over this range, and Phillips sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPhillips.mass - mass) == min(abs(isoPhillips.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPhillips.mass[idx_p] - mass) >= 0.002) | ((isoBaraffe.mass[idx_b] - mass) >= 0.002): + print( 'WARNING: Baraffe or Phillips model interpolation between 0.01 - 0.08 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.07 or 0.075 (whichever is appropriate) + diff = 0.075 - 0.07 + weight_p = (0.075 - mass) / diff + weight_b = 1.0 - weight_p + print( 'Baraffe {0} and Phillips {1} at mass {2}'.format(weight_p, weight_b, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoPhillips.logT[idx_p] * weight_p) + \ + (10**isoBaraffe.logT[idx_b] * weight_b) + L = (10**isoPhillips.logL[idx_p] * weight_p) + \ + (10**isoBaraffe.logL[idx_b] * weight_b) + g = (10**isoPhillips.logg[idx_p] * weight_p) + \ + (10**isoBaraffe.logg[idx_b] * weight_b) + mcurr = (isoPhillips.mass_current[idx_p] * weight_p) + \ + (isoBaraffe.mass_current[idx_b] * weight_b) + phase = np.round((isoPhillips.mass_current[idx_p] * weight_p) + \ + (isoBaraffe.mass_current[idx_b] * weight_b)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M>=0.075, + # Phillips at M<=0.075, and the combination inbetween + mass = np.concatenate((isoPhillips.mass[good_p], mid_mass, isoBaraffe.mass[good_b])) + logT = np.concatenate((isoPhillips.logT[good_p], mid_logT, isoBaraffe.logT[good_b])) + logL = np.concatenate((isoPhillips.logL[good_p], mid_logL, isoBaraffe.logL[good_b])) + logG = np.concatenate((isoPhillips.logg[good_p], mid_logG, isoBaraffe.logg[good_b])) + mcurr = np.concatenate((isoPhillips.mass_current[good_p], mid_Mcurr, isoBaraffe.mass_current[good_b])) + phase = np.concatenate((isoPhillips.phase[good_p], mid_phase, isoBaraffe.phase[good_b])) + + # Also add a source flag + source = np.concatenate( (['Phillips']*len(good_p[0]), + ['Baraffe+Phillips']*len(mid_mass), + ['Baraffe']*len(good_b[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +"""def plot_merged_isochrone(iso): + "" + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020 and Baraffe+15 models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + "" + + # Define colors for different sources + color_map = {'Phillips': 'blue', 'Baraffe': 'red', 'Baraffe+Phillips': 'pink'} + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], label='linear representation') + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].set_xlim(0.0, 0.1) + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], label='linear representation') + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].set_xlim(0.0, 0.1) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], label='linear representation') + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].set_xlim(0.0, 0.1) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() +""" + +def plot_merged_isochrone(iso): + """ + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020 and Baraffe+15 models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + """ + + # Define colors for different sources + color_map = {'Phillips': 'blue', 'Baraffe': 'red', 'Baraffe+Phillips': 'pink'} + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], color=color_map[source]) # Fixed plot function + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].set_xlim(0.0, 0.1) + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], color=color_map[source]) # Fixed plot function + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].set_xlim(0.0, 0.1) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], color=color_map[source]) # Fixed plot function + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].set_xlim(0.0, 0.1) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() + +def plot_merged_bpp_isochrone(iso): + """ + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020, Baraffe+15, and Pisa models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + """ + + # Define colors for different sources + color_map = {'Phillips': 'blue', + 'Baraffe': 'red', + 'Pisa':'green', + 'Baraffe+Phillips': 'pink', + 'Baraffe+Pisa': 'cyan' + } + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], color=color_map[source]) # Fixed plot function + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], color=color_map[source]) # Fixed plot function + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], color=color_map[source]) # Fixed plot function + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() + +"""def merge_isochrone_pisa_baraffe_phillips(logAge, metallicity='solar', rotation=True, iso_in=None): #????? + "" + Function to merge Pisa 2011, Baraffe, and Phillips 2020 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Z = 0.015 for Phillips 2020. + + If iso_in = None, will take Pisa models to smallest available mass, + then switch to Baraffe/Phillips. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + "" + # Get individual Ekstrom and Pisa isochrones at desired age + isoBaraffePhillips = merge_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_Pisa = max(isoPisa.mass) + good = np.where(isoEkstrom.mass > max_Pisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPisa.mass, isoEkstrom.mass) + logT = np.append(isoPisa.logT, isoEkstrom.logT) + logg = np.append(isoPisa.logg, isoEkstrom.logg) + logL = np.append(isoPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso +""" + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_baraffe_pisa_phillips(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Merges Phillips, Baraffe, and Pisa isochrones to create a complete evolutionary track. + + Inputs: + logAge - Logarithmic Age + metallicity - Metallicity (default is 'solar') + rotation - Boolean indicating whether to include rotation in the models (default is True) + + Outputs: + iso - An object containing the merged isochrone data. + """ + # Merge Phillips with Baraffe + print(f"Merging Phillips with Baraffe for logAge = {logAge}") + isoBaraffePhillips = merge_isochrone_baraffe_phillips(logAge, metallicity=metallicity) + + # Merge Baraffe+Phillips with Pisa (depending on the logAge) + if logAge <= 7.4: + print(f"Merging Baraffe+Phillips with Pisa for logAge = {logAge}") + iso = merge_isochrone_baraffe_pisa(logAge, metallicity=metallicity, iso_in=isoBaraffePhillips) + else: + # If logAge > 7.4, you may want to merge with different models (Parsec/Ekstrom) + print(f"Merging Baraffe+Phillips with higher mass models for logAge = {logAge}") + iso = merge_isochrone_baraffe_pisa(logAge, metallicity=metallicity, iso_in=isoBaraffePhillips) + + return iso + + +def merge_all_isochrones_phillips_baraffe_pisa_ekstrom_parsec(metallicity='solar', rotation=True, plot=False): + """ + Make evolutionary isochrones containing a continuous distribution of + masses from the PMS to the MS. The models used are the following: + + PMS + Phillips from 0.01 - 0.07 M_sun + Baraffe+15 from 0.07 - 0.4 M_sun + Pisa 2011 from 0.5 - top of grid (~7 M_sun) + + MS (M > Pisa 2011) + Ekstrom+12 for logAge < 7.4 + Parsec V1.2s for logAge > 7.4 + + BD stars + Phillips for 6.0 <= logAge <= 10.0 + + metallicity = 'solar' --> Ekstrom+12 z014, Pisa2011 z015, Parsec z015, Phillips z00 + + if plot = True, will make plots of merged isochrones in 'plots' directory, + which must already exist + + Code is expected to be run in merged model working directory. + """ + # Root data directory for Ekstrom+12 isochrones + rootDirE = models_dir + 'Ekstrom2012/iso/' + metalPart = 'z014/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rotPart = 'rot/' + if not rotation: + rotPart = 'norot/' + rootDirE += metalPart+rotPart + + # Root data directory for the Baraffe isochrones + rootDirBaraffe = models_dir + 'Baraffe15/iso/' + + # Root data directory for Pisa isochrones + rootDirPisa = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPisa += metSuffix + + # Root data directory for Parsec isochrones + rootDirParsec = models_dir + 'ParsecV1.2s/iso/' + metalSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirParsec += metalSuffix + + # Root data directory for Phillips isochrones + rootDirPhillips = models_dir + 'Phillips2020/iso/' + mSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPhillips +=mSuffix + + # Search both directories for iso_*.dat files + isoFilesE = glob.glob(rootDirE + 'iso_*.dat') + isoFilesB = glob.glob(rootDirBaraffe + 'iso_*.fits') + isoFilesPi = glob.glob(rootDirPisa + 'iso_*.dat') + isoFilesPa = glob.glob(rootDirParsec + 'iso_*') + isoFilesPh = glob.glob(rootDirPhillips + 'iso_*.fits') + + # Output of merged isochrones + if rotation == True: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_rot_test/'.format(metSuffix[:-1]) + # Raise error if wanting Phillips data? + else: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_norot/'.format(metSuffix[:-1]) + if not os.path.exists(outDir): + os.mkdir(outDir) + + # Isolate the iso*.dat file names + for ii in range(len(isoFilesE)): + isoFilesE[ii] = isoFilesE[ii].split('/')[-1] + + for ii in range(len(isoFilesB)): + isoFilesB[ii] = isoFilesB[ii].split('/')[-1] + + for ii in range(len(isoFilesPi)): + isoFilesPi[ii] = isoFilesPi[ii].split('/')[-1] + + for ii in range(len(isoFilesPa)): + isoFilesPa[ii] = isoFilesPa[ii].split('/')[-1] + + for ii in range(len(isoFilesPh)): + isoFilesPh[ii] = isoFilesPh[ii].split('/')[-1] + + # Loop through the Pisa isochrones, adding the MS and Baraffe models + # as appropriate + for ii in range(len(isoFilesPi)): + isoFilePi = isoFilesPi[ii] + + logAgeStr = isoFilePi.replace('iso_', '').replace('.dat', '') + logAge = float(logAgeStr) + + #-----PRE-MAIN SEQUENCE----# + # Merge with the Baraffe+15 models from 0.07 - 0.4 M_sun. Includes + # transition region between 0.4 - 0.5 M_sun in which we shift + # from 100% Baraffe to 100% Pisa + + print( 'Merging isochrones Pisa + Baraffe + Phillips from ', isoFilePi) + isoPMS = merge_isochrone_phillips_baraffe_pisa(logAge, metallicity=metallicity) + + #--------MAIN SEQUENCE-------# + # Case where logAge <= 7.4, we merge with Ekstrom. Otherwise, merge + # which parsec + if logAge <= 7.4: + if isoFilePi not in isoFilesE: + print( 'Skipping isochrones from ', isoFilePi) + print( 'PROBLEM WITH PISA OR EKSTROM') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Ekstrom from ', isoFilePi) + iso = merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity=metallicity, + rotation=rotation, iso_in=isoPMS) + else: + if isoFilePi not in isoFilesPa: + print( 'Skipping isochrones from ', isoFilePi) + print( 'PROBLEM WITH PISA OR PARSEC') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Parsec from ', isoFilePi) + iso = merge_isochrone_phillips_pisa_parsec(logAge, metallicity=metallicity, + iso_in=isoPMS) + + # Make test plot, if desired. These are put in plots directory + if plot: + # Make different models different colors + phillips_ind = np.where(iso.source == 'Phillips') + merge_pb = np.where(iso.source == 'Phillips+Baraffe') + baraffe_ind = np.where(iso.source == 'Baraffe') + merge_ind = np.where(iso.source == 'Baraffe+Pisa') + pisa_ind = np.where(iso.source == 'Pisa') + MS_ind = np.where( (iso.source == 'Ekstrom') | (iso.source == 'Parsec')) + #Extract age + logAge = isoFilePi.split('_')[1][:-4] + + # Temporarily turn off interactive plot. Turn back on at end + py.ioff() + + py.figure(1) + py.clf() + py.plot(iso.logT[phillips_ind], iso.logL[phillips_ind], 'c-', label = 'Phillips', + linewidth=2) + py.plot(iso.logT[merge_pb], iso.logL[merge_pb], 'y-', label = 'Phillips+Baraffe', + linewidth=2) + py.plot(iso.logT[baraffe_ind], iso.logL[baraffe_ind], 'k-', label = 'Baraffe', + linewidth=2) + py.plot(iso.logT[merge_ind], iso.logL[merge_ind], 'r-', label = 'Baraffe+Pisa', + linewidth=2) + py.plot(iso.logT[pisa_ind], iso.logL[pisa_ind], 'g-', label = 'Pisa', + linewidth=2) + py.plot(iso.logT[MS_ind], iso.logL[MS_ind], 'b-', + label = 'Ekstrom/Parsec', linewidth=2) + py.xlabel('log Teff') + py.ylabel('log L') + py.title('Log Age = {0:s}'.format(logAge)) + py.legend(loc=3) + py.axis([4.9, 3.2, -3.0, 8]) + py.savefig(outDir+'plots/iso_'+logAge+'.png') + + py.ion() + + + _out = open(outDir + isoFilePi, 'w') + + hdr_fmt = '%12s %10s %10s %10s %10s %12s %5s %-10s\n' + _out.write(hdr_fmt % + ('# M_init', 'log T', 'log L', 'log g', 'logT_WR', 'M_curr', 'phase', 'Source')) + _out.write(hdr_fmt % + ('# (Msun)', '(Kelvin)', '(Lsun)', '(cgs)', '(Kelvin)', '(Msun)', '()', '()')) + + for kk in range(len(iso.mass)): + _out.write('%12.6f %10.4f %10.4f %10.4f %10.4f %12.6f %5d %-10s\n' % + (iso.mass[kk], iso.logT[kk], iso.logL[kk], + iso.logg[kk], iso.logT_WR[kk], + iso.mass_current[kk], iso.phase[kk], iso.source[kk])) + + _out.close() + return + +def get_pisa_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Pisa isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Pisa 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Pisa isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[1]] # K + logL = data[cols[0]] # L_sun + logg = data[cols[3]] + + # Hack: Make mass_current and phase + mass_current = np.array(mass) + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def get_phillips_pisa_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips/Pisa isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'merged/phillips_pisa' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020 & Pisa 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.fits' % logAge + if not os.path.exists(isoFile): + print( 'Phillips/Pisa isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + cols = data.keys() + mass = data[cols[0]] #Note: this is initial mass, in M_sun + logT = data[cols[2]] # K + logL = data[cols[1]] # L_sun + logg = data[cols[3]] + interpolated = data['interpolated'] + + # warning if data point is coming from interpolated region + if warn_interpolated and np.any(interpolated): + print( f"Warning: {np.sum(interpolated)} of {len(interpolated)} data points are interpolated.") + + # Hack: Make mass_current and phase + mass_current = np.array(mass) + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + obj.interpolated = interpolated + + return obj + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_phillips_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Phillips 2020, Baraffe+15, and Pisa 2011 models. + + - Uses Phillips for M <= 0.07 M_sun + - Transitions from Phillips to Baraffe between 0.07 - 0.075 M_sun + - Uses Baraffe for 0.075 - 0.4 M_sun + - Transitions from Baraffe to Pisa between 0.4 - 0.5 M_sun + - Uses Pisa for M >= 0.5 M_sun + + Can only handle ages where models exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print('Non-solar metallicity not supported yet') + return + + # Load individual isochrones + isoPhillips = get_phillips_isochrone(logAge, metallicity=metallicity) + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify mass ranges + good_p = np.where(isoPhillips.mass <= 0.07) + good_b = np.where((isoBaraffe.mass >= 0.075) & (isoBaraffe.mass <= 0.4)) + good_pisa = np.where(isoPisa.mass >= 0.5) + + # Transition Phillips → Baraffe (0.07 - 0.075 M_sun) + mid_mass_phillips_baraffe = np.arange(0.07, 0.075, 0.002) + mid_logT_phillips_baraffe, mid_logL_phillips_baraffe, mid_logG_phillips_baraffe = [], [], [] + mid_Mcurr_phillips_baraffe, mid_phase_phillips_baraffe = [], [] + + for mass in mid_mass_phillips_baraffe: + idx_p = np.argmin(np.abs(isoPhillips.mass - mass)) + idx_b = np.argmin(np.abs(isoBaraffe.mass - mass)) + + weight_p = (0.075 - mass) / 0.005 + weight_b = 1.0 - weight_p + + Teff = (10**isoPhillips.logT[idx_p] * weight_p) + (10**isoBaraffe.logT[idx_b] * weight_b) + L = (10**isoPhillips.logL[idx_p] * weight_p) + (10**isoBaraffe.logL[idx_b] * weight_b) + g = (10**isoPhillips.logg[idx_p] * weight_p) + (10**isoBaraffe.logg[idx_b] * weight_b) + mcurr = (isoPhillips.mass_current[idx_p] * weight_p) + (isoBaraffe.mass_current[idx_b] * weight_b) + phase = np.round((isoPhillips.phase[idx_p] * weight_p) + (isoBaraffe.phase[idx_b] * weight_b)) + + mid_logT_phillips_baraffe.append(np.log10(Teff)) + mid_logL_phillips_baraffe.append(np.log10(L)) + mid_logG_phillips_baraffe.append(np.log10(g)) + mid_Mcurr_phillips_baraffe.append(mcurr) + mid_phase_phillips_baraffe.append(phase) + + # Transition Baraffe → Pisa (0.4 - 0.5 M_sun) + mid_mass_baraffe_pisa = np.arange(0.4, 0.5 + 0.01, 0.02) + mid_logT_baraffe_pisa, mid_logL_baraffe_pisa, mid_logG_baraffe_pisa = [], [], [] + mid_Mcurr_baraffe_pisa, mid_phase_baraffe_pisa = [], [] + + for mass in mid_mass_baraffe_pisa: + idx_b = np.argmin(np.abs(isoBaraffe.mass - mass)) + idx_p = np.argmin(np.abs(isoPisa.mass - mass)) + + weight_b = (0.5 - mass) / 0.1 + weight_p = 1.0 - weight_b + + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.phase[idx_b] * weight_b) + (isoPisa.phase[idx_p] * weight_p)) + + mid_logT_baraffe_pisa.append(np.log10(Teff)) + mid_logL_baraffe_pisa.append(np.log10(L)) + mid_logG_baraffe_pisa.append(np.log10(g)) + mid_Mcurr_baraffe_pisa.append(mcurr) + mid_phase_baraffe_pisa.append(phase) + + # Merge all mass regimes + mass = np.concatenate((isoPhillips.mass[good_p], mid_mass_phillips_baraffe, isoBaraffe.mass[good_b], + mid_mass_baraffe_pisa, isoPisa.mass[good_pisa])) + + logT = np.concatenate((isoPhillips.logT[good_p], mid_logT_phillips_baraffe, isoBaraffe.logT[good_b], + mid_logT_baraffe_pisa, isoPisa.logT[good_pisa])) + + logL = np.concatenate((isoPhillips.logL[good_p], mid_logL_phillips_baraffe, isoBaraffe.logL[good_b], + mid_logL_baraffe_pisa, isoPisa.logL[good_pisa])) + + logG = np.concatenate((isoPhillips.logg[good_p], mid_logG_phillips_baraffe, isoBaraffe.logg[good_b], + mid_logG_baraffe_pisa, isoPisa.logg[good_pisa])) + + mcurr = np.concatenate((isoPhillips.mass_current[good_p], + mid_Mcurr_phillips_baraffe, + isoBaraffe.mass_current[good_b], + mid_Mcurr_baraffe_pisa, + isoPisa.mass_current[good_pisa])) + + phase = np.concatenate((isoPhillips.phase[good_p], mid_phase_phillips_baraffe, isoBaraffe.phase[good_b], + mid_phase_baraffe_pisa, isoPisa.phase[good_pisa])) + + # Source flag + source = np.concatenate((['Phillips']*len(good_p[0]), ['Phillips+Baraffe']*len(mid_mass_phillips_baraffe), + ['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass_baraffe_pisa), + ['Pisa']*len(good_pisa[0]))) + + # Create final merged isochrone object + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_pisa_Ekstrom(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Function to merge Pisa 2011 and Ekstrom 2012 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Z = 0.014 for Ekstrom+12. + + If iso_in = None, will take Pisa models to highest available mass, + then switch to Ekstrom. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + # Get individual Ekstrom and Pisa isochrones at desired age + isoEkstrom = get_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_Pisa = max(isoPisa.mass) + good = np.where(isoEkstrom.mass > max_Pisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPisa.mass, isoEkstrom.mass) + logT = np.append(isoPisa.logT, isoEkstrom.logT) + logg = np.append(isoPisa.logg, isoEkstrom.logg) + logL = np.append(isoPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso + +def merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Function to merge Phillips 2020, Pisa 2011, and Ekstrom 2012 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Phillips 2020 and Z = 0.014 for Ekstrom+12. + + If iso_in = None, will take Pisa/Phillips models to highest available mass, + then switch to Ekstrom. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + # Get individual Ekstrom and Pisa isochrones at desired age + isoEkstrom = get_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPhPisa = get_phillips_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPhPisa.source = np.array(['Phillips/Pisa']*len(isoPhPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPhPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_PhPisa = max(isoPhPisa.mass) + good = np.where(isoEkstrom.mass > max_PhPisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPhPisa.mass, isoEkstrom.mass) + logT = np.append(isoPhPisa.logT, isoEkstrom.logT) + logg = np.append(isoPhPisa.logg, isoEkstrom.logg) + logL = np.append(isoPhPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPhPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPhPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPhPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPhPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + # add interpolation flag + if hasattr(isoPhPisa, 'interpolated'): + interpolated = np.append(isoPhPisa.interpolated, np.full(len(isoEkstrom.mass), False)) + iso.interpolated = interpolated + + # fix phase values in interpolated region (assign same as min-mass Parsec object) + min_ekstrom_mass_idx = np.argmin(isoEkstrom.mass) + min_ekstrom_phase = isoEkstrom.phase[min_ekstrom_mass_idx] + interp_mask = iso.interpolated == True + iso.phase[interp_mask] = min_ekstrom_phase + + return iso + +def get_Ekstrom_isochrone(logAge, metallicity='solar', rotation=True): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Ekstrom isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). Also interpolate model to finer mass grid + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Ekstrom2012/iso/' + metSuffix = 'z014/' + if metallicity != 'solar': + print( 'Non-solar Ekstrom+12 metallicities not supported yet') + return + rotSuffix = 'rot/' + if not rotation: + rotSuffix = 'norot/' + + rootDir += metSuffix + rotSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Ekstrom isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[7]] # K + logL = data[cols[6]] # L_sun + logT_WR = data[cols[8]] # K; if this doesn't equal logT, we have a WR star + mass_curr = data[cols[3]] + phase = np.ones(len(data), dtype=int) + + # Need to calculate log g from mass and R + R_sun = 7.*10**10 #cm + M_sun = 2.*10**33 #g + G_const = 6.67*10**-8 #cgs + + radius = data[cols[19]] #R_sun + logg = np.log10( (G_const * np.array(mass).astype(float) * M_sun) / + (np.array(radius).astype(float) * R_sun)**2 ) + + # Interpolate isochrone to finer mass grid on main-ish sequence + # (1-60 M_sun, or the highest mass in the model); don't want to + # completely redo all sampling, just this region + if max(mass) > 60: + new_masses = np.arange(1, 60+0.1, 0.5) + else: + new_masses = np.arange(1, max(mass), 0.5) + mass_grid = np.append(new_masses, mass) + mass_grid.sort() # Make sure grid is in proper order + + # Build interpolators in linear space + f_logT = interpolate.interp1d(mass, 10**logT, kind='linear') + f_logL = interpolate.interp1d(mass, 10**logL, kind='linear') + f_logT_WR = interpolate.interp1d(mass, 10**logT_WR, kind='linear') + f_logg = interpolate.interp1d(mass, 10**logg, kind='linear') + f_Mcurr = interpolate.interp1d(mass, mass_curr, kind='linear') + f_phase = interpolate.interp1d(mass, phase, kind='linear') + + # Do interpolation, convert back to logspace + logT_interp = np.log10(f_logT(mass_grid)) + logL_interp = np.log10(f_logL(mass_grid)) + logT_WR_interp = np.log10(f_logT_WR(mass_grid)) + logg_interp = np.log10(f_logg(mass_grid)) + Mcurr_interp = f_Mcurr(mass_grid) + phase_interp = f_phase(mass_grid) + + # Make isochrone + obj = objects.DataHolder() + obj.mass = mass_grid + obj.logT = logT_interp + obj.logg = logg_interp + obj.logL = logL_interp + obj.logT_WR = logT_WR_interp + obj.mass_current = Mcurr_interp + obj.phase = phase_interp + + return obj + +def merge_isochrone_pisa_parsec(logAge, metallicity='solar', iso_in=None): + """ + Function to merge Pisa 2011 and ParsecV1.2s models. Solar metallicity is + Z = 0.015. + + If iso_in = None, will take Pisa models to highest available mass, + then switch to Parsec. If iso_in is defined, then will take this + isochrone to highest mass and switch to Parsec + + Can only handle ages at which both sets of models already exist: + logAge = 6.6 - 8.0, delta logAge = 0.01 + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + # Make Parsec source array + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + # Define isoPisa based on iso_in input + if iso_in != None: + isoPisa = iso_in + else: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + + # Use Pisa model as high up as it goes, then switch to Parsec + max_Pisa = max(isoPisa.mass) + good = np.where(isoParsec.mass > max_Pisa) + isoParsec.mass = isoParsec.mass[good] + isoParsec.logT = isoParsec.logT[good] + isoParsec.logg = isoParsec.logg[good] + isoParsec.logL = isoParsec.logL[good] + isoParsec.mass_current = isoParsec.mass_current[good] + isoParsec.phase = isoParsec.phase[good] + isoParsec.source = isoParsec.source[good] + + # Combine the arrays + M = np.append(isoPisa.mass, isoParsec.mass) + logT = np.append(isoPisa.logT, isoParsec.logT) + logg = np.append(isoPisa.logg, isoParsec.logg) + logL = np.append(isoPisa.logL, isoParsec.logL) + mcurr = np.append(isoPisa.mass_current, isoParsec.mass_current) + phase = np.append(isoPisa.phase, isoParsec.phase) + logT_WR = np.append(isoPisa.logT, isoParsec.logT) + source = np.append(isoPisa.source, isoParsec.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso + +def merge_isochrone_phillips_pisa_parsec(logAge, metallicity='solar', iso_in=None): + """ + Function to merge Phillips 2020, Pisa 2011, and ParsecV1.2s models. + Solar metallicity is Z = 0.015. + + If iso_in = None, will take Phillips/Pisa models to highest available mass, + then switch to Parsec. If iso_in is defined, then will take this + isochrone to highest mass and switch to Parsec + + Can only handle ages at which both sets of models already exist: + logAge = 6.6 - 8.0, delta logAge = 0.01 + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + # Make Parsec source array + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + # Define isoPhPisa based on iso_in input + if iso_in != None: + isoPhPisa = iso_in + else: + isoPhPisa = get_phillips_pisa_isochrone(logAge, metallicity=metallicity) + isoPhPisa.source = np.array(['Phillips/Pisa']*len(isoPhPisa.mass)) + + # Use Phillips/Pisa model as high up as it goes, then switch to Parsec + max_PhPisa = max(isoPhPisa.mass) + good = np.where(isoParsec.mass > max_PhPisa) + isoParsec.mass = isoParsec.mass[good] + isoParsec.logT = isoParsec.logT[good] + isoParsec.logg = isoParsec.logg[good] + isoParsec.logL = isoParsec.logL[good] + isoParsec.mass_current = isoParsec.mass_current[good] + isoParsec.phase = isoParsec.phase[good] + isoParsec.source = isoParsec.source[good] + + + # Combine the arrays + M = np.append(isoPhPisa.mass, isoParsec.mass) + logT = np.append(isoPhPisa.logT, isoParsec.logT) + logg = np.append(isoPhPisa.logg, isoParsec.logg) + logL = np.append(isoPhPisa.logL, isoParsec.logL) + mcurr = np.append(isoPhPisa.mass_current, isoParsec.mass_current) + phase = np.append(isoPhPisa.phase, isoParsec.phase) + logT_WR = np.append(isoPhPisa.logT, isoParsec.logT) + source = np.append(isoPhPisa.source, isoParsec.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + # add interpolation flag + if hasattr(isoPhPisa, 'interpolated'): + interpolated = np.append(isoPhPisa.interpolated, np.full(len(isoEkstrom.mass), False)) + iso.interpolated = interpolated + + # fix phase values in interpolated region (assign same as min-mass Parsec object) + min_parsec_mass_idx = np.argmin(isoParsec.mass) + min_parsec_phase = isoParsec.phase[min_parsec_mass_idx] + interp_mask = iso.interpolated == True + iso.phase[interp_mask] = min_parsec_phase + + return iso + +def make_parsec_iso(logAge, metallicity='solar'): + """ + Make parsec isochrone in Popstar iso object + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + iso = objects.DataHolder() + iso.mass = isoParsec.mass + iso.logL = isoParsec.logL + iso.logg = isoParsec.logg + iso.logT = isoParsec.logT + iso.mass_current = isoParsec.mass_current + iso.phase = isoParsec.phase + iso.logT_WR = isoParsec.logT + iso.source = isoParsec.source + + return iso +def get_phillips_parsec_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips/Parsec isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'merged/phillips_parsec/' + print(rootDir) + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020/Parsec 2011 metallicities not supported yet') + return + rootDir += metSuffix + print(rootDir) + + # Check to see if isochrone exists + #isoFile = rootDir + 'iso_%.2f.fits' % logAge + isoFile = os.path.join(rootDir, f'iso_{logAge:.2f}.fits') # not a valid path! + print(isoFile) + print(os.path.exists(isoFile)) + if not os.path.exists(isoFile): + print( 'Phillips/Parsec isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) # formatting in this is mismatched! + print( 'Quitting') #raise IO Error instead and merge with top print statement + return + + data = Table.read(isoFile, format='fits') + cols = data.keys() + mass = data[cols[0]] #Note: this is initial mass, in M_sun + logT = data[cols[2]] # K + logL = data[cols[1]] # L_sun + logg = data[cols[3]] + # current mass + Mcurr = data[cols[3]] #issue! + phase = np.ones(len(data), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = Mcurr + obj.phase = phase + + return obj + +def make_phillips_parsec_iso(logAge, metallicity='solar'): + """ + Make Phillips/Parsec isochrone in Popstar iso object + """ + isoPhParsec = get_phillips_parsec_isochrone(logAge, metallicity=metallicity) + isoPhParsec.source = np.array(['Phillips+Parsec']*len(isoPhParsec.mass)) + + iso = objects.DataHolder() + iso.mass = isoPhParsec.mass + iso.logL = isoPhParsec.logL + iso.logg = isoPhParsec.logg + iso.logT = isoPhParsec.logT + iso.mass_current = isoPhParsec.mass_current + iso.phase = isoPhParsec.phase + iso.logT_WR = isoPhParsec.logT + iso.source = isoPhParsec.source + + return iso + +def get_parsec_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Parsec isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'ParsecV1.2s/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Parsec 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Parsec isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[5]] # K + logL = data[cols[4]] # L_sun + logg = data[cols[6]] + Mcurr = data[cols[3]] + phase = np.ones(len(data), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = Mcurr + obj.phase = phase + + return obj + +#### CREATING FINAL ISOCHRONES #### + +def merge_all_isochrones_phillips_baraffe_pisa_ekstrom_parsec2(logAge_arr=np.arange(6.0, 10.1, 0.01), + metallicity='solar', + rotation=True, plot=False): + """ + Make evolutionary isochrones containing a continuous distribution of + masses from the PMS to the MS. The models used are the following: + + PMS (logAge < 8) + Phillips 2020 from 0.01 to 0.07 M_sun + Baraffe+15 from 0.075 - 0.4 M_sun + Pisa 2011 from 0.5 - top of grid (~7 M_sun) + + MS (M > Pisa 2011) + Ekstrom+12 for logAge < 7.4 + Parsec V1.2s for logAge > 7.4 + + metallicity = 'solar' --> Ekstrom+12 z014, Pisa2011 z015, Parsec z015 + + if plot = True, will make plots of merged isochrones in 'plots' directory, + which must already exist + + Code is expected to be run in merged model working directory. + """ + # Root data directory for Ekstrom+12 isochrones + rootDirE = models_dir + 'Ekstrom2012/iso/' + metalPart = 'z014/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rotPart = 'rot/' + if not rotation: + rotPart = 'norot/' + rootDirE += metalPart+rotPart + + # Root data directory for the Baraffe isochrones + rootDirBaraffe = models_dir + 'Baraffe15/iso/' + + # Root data directory for Pisa isochrones + rootDirPisa = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPisa += metSuffix + + # Root data directory for Parsec isochrones + rootDirParsec = models_dir + 'ParsecV1.2s/iso/' + metalSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirParsec += metalSuffix + + # Root data directory for Phillips isochrones + rootDirPhillips = models_dir + 'Phillips2020/iso/' + metalSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPhillips += metalSuffix + + # Search both directories for iso_*.dat files + isoFilesE = glob.glob(rootDirE + 'iso_*.dat') + isoFilesB = glob.glob(rootDirBaraffe + 'iso_*.fits') + isoFilesPi = glob.glob(rootDirPisa + 'iso_*.dat') + isoFilesPa = glob.glob(rootDirParsec + 'iso_*') + isoFilesPh = glob.glob(rootDirPhillips + 'iso_*.fits') + + # Output of merged isochrones + if rotation == True: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_rot/'.format(metSuffix[:-1]) + #outDir = models_dir + 'merged/test/{0}_rot/'.format(metSuffix[:-1]) + else: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_norot/'.format(metSuffix[:-1]) + #outDir = models_dir + 'merged/test/{0}_norot/'.format(metSuffix[:-1]) + if not os.path.exists(outDir): + os.mkdir(outDir) + + # Isolate the iso*.dat file names + for ii in range(len(isoFilesE)): + isoFilesE[ii] = isoFilesE[ii].split('/')[-1] + + for ii in range(len(isoFilesB)): + isoFilesB[ii] = isoFilesB[ii].split('/')[-1] + + for ii in range(len(isoFilesPi)): + isoFilesPi[ii] = isoFilesPi[ii].split('/')[-1] + + for ii in range(len(isoFilesPa)): + isoFilesPa[ii] = isoFilesPa[ii].split('/')[-1] + + for ii in range(len(isoFilesPh)): + isoFilesPh[ii] = isoFilesPh[ii].split('/')[-1] + + # Loop through the Pisa isochrones, adding the MS and Baraffe/Phillips models + # as appropriate + for ii in range(len(logAge_arr)): + logAge = logAge_arr[ii] + iso_name = 'iso_{0:.2f}.dat'.format(logAge) + + #-----PRE-MAIN SEQUENCE (only for logAge < 8)----# + if logAge <= 8.0: + # Merge with the Phillips 2020 models from 0.01 - 0.07 M_sun and + # Baraffe+15 models from 0.075 - 0.4 M_sun. Includes + # transition region between 0.4 - 0.5 M_sun in which we shift + # from 100% Baraffe to 100% Pisa and 0.07 - 0.075 M_sun in which we + #shift from 100% Phillips to 100% Baraffe + + print( 'Merging isochrones Pisa + Phillips + Baraffe from ', iso_name) + isoPMS = merge_isochrone_phillips_baraffe_pisa(logAge, metallicity=metallicity) + + #--------MAIN SEQUENCE-------# + # Case where logAge <= 7.4, we merge with Ekstrom. Otherwise, merge + # which parsec + if logAge <= 7.4: + if iso_name not in isoFilesE: + print( 'Skipping isochrones from ', iso_name) + print( 'PROBLEM WITH PISA OR EKSTROM') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Ekstrom from ', iso_name) + iso = merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity=metallicity, + rotation=rotation, iso_in=isoPMS) + else: + if iso_name not in isoFilesPa: + print( 'Skipping isochrones from ', iso_name) + print( 'PROBLEM WITH PISA OR PARSEC') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Parsec from ', iso_name) + iso = merge_isochrone_phillips_pisa_parsec(logAge, metallicity=metallicity, + iso_in=isoPMS) + else: + # If logAge > 8.0, just take the Phillips/Parsec model for the entire isochrone + print( 'Making Phillips/Parsec from ', iso_name) + # change to fits format + iso = make_phillips_parsec_iso(logAge, metallicity=metallicity) + + # Make test plot, if desired. These are put in plots directory + if plot: + # Make different models different colors + phillips_ind = np.where(iso.source == 'Phillips') + merge_pb = np.where(iso.source == 'Phillips+Baraffe') + baraffe_ind = np.where(iso.source == 'Baraffe') + merge_ind = np.where(iso.source == 'Baraffe+Pisa') + pisa_ind = np.where(iso.source == 'Pisa') + MS_ind = np.where( (iso.source == 'Ekstrom') | (iso.source == 'Parsec')) + + # Temporarily turn off interactive plot. Turn back on at end + py.ioff() + + py.figure(1) + py.clf() + py.plot(iso.logT[phillips_ind], iso.logL[phillips_ind], 'c-', label = 'Phillips', + linewidth=2) + py.plot(iso.logT[merge_pb], iso.logL[merge_pb], 'y-', label = 'Phillips+Baraffe', + linewidth=2) + py.plot(iso.logT[baraffe_ind], iso.logL[baraffe_ind], 'k-', label = 'Baraffe', + linewidth=2) + py.plot(iso.logT[merge_ind], iso.logL[merge_ind], 'r-', label = 'Baraffe+Pisa', + linewidth=2) + py.plot(iso.logT[pisa_ind], iso.logL[pisa_ind], 'g-', label = 'Pisa', + linewidth=2) + py.plot(iso.logT[MS_ind], iso.logL[MS_ind], 'b-', + label = 'Ekstrom/Parsec', linewidth=2) + py.xlabel('log Teff') + py.ylabel('log L') + py.title('Log Age = {0}'.format(logAge)) + py.legend(loc=3) + py.axis([4.9, 3.2, -3.0, 8]) + py.savefig(outDir+'plots/iso_{0:.2f}.png'.format(logAge)) + + py.ion() + + + _out = open(outDir + iso_name, 'w') + + hdr_fmt = '%12s %10s %10s %10s %10s %12s %5s %-10s\n' + _out.write(hdr_fmt % + ('# M_init', 'log T', 'log L', 'log g', 'logT_WR', 'M_curr', 'phase', 'Source')) + _out.write(hdr_fmt % + ('# (Msun)', '(Kelvin)', '(Lsun)', '(cgs)', '(Kelvin)', '(Msun)', '()', '()')) + + for kk in range(len(iso.mass)): + _out.write('%12.6f %10.4f %10.4f %10.4f %10.4f %12.6f %5d %-10s\n' % + (iso.mass[kk], iso.logT[kk], iso.logL[kk], + iso.logg[kk], iso.logT_WR[kk], + iso.mass_current[kk], iso.phase[kk], iso.source[kk])) + + _out.close() + return + + +##### CREATING MERGED ATMOSPHERE MODEL ##### +def create_merged_models(cdbs_path, plot=False): + """ + for 1200 - 1000 K, merge Meisner and BTSettl! + + From 1000 K - 1200 K, merge the Meisner and BTSettl atmospheres. + More like BTSettl near 1200 K, more like Meisner near 1000 K + + cdbs_path is path to cdbs directory (including cdbs). Will make new directory + in cdbs/grid named "merged_meisner_BTsettl" with with merged models. If plot = True, will plot + the normalized merged model plus the original Meisner and BTSettl models + + Temp 1000 - 1200, steps of 20; logg 2.5 - 5.5, steps of 0.5, metallicity covering + Meisner range (2.5 -- 5.5, in steps of 0.5). (So, this is one model at 5250)? + + Note: metallicity directories created by hand + + Creates new directory "merged_meisner_BTSettl" in cdbs/grid with new spectrum + catalog file. + Also includes the atlas 5500K model and phoenix 5000K model, for interpolation purposes + """ + #Setting logg sampling + logg_arr = np.arange(2.5, 5.5+0.1, 0.5) + + # Setting metallicity sub-directories + atlas_dir = ['ckm25', 'ckm20', 'ckm15', 'ckm10', 'ckm05', 'ckp00', 'ckp02', 'ckp05'] + phoenix_dir = ['phoenixm30', 'phoenixm20', 'phoenixm15', 'phoenixm10', 'phoenixm05', 'phoenixm00', 'phoenixp05', 'phoenixp05'] + output_dir = ['mergedm25', 'mergedm20', 'mergedm15', 'mergedm10', 'mergedm05', 'mergedp00', 'mergedp02', 'mergedp05'] + metal_arr = [-2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.2, 0.5] + + assert len(atlas_dir) == len(phoenix_dir) == len(output_dir) + + # Make new cdbs merged directory, if it doesn't already exist + newPath = '{0}/grid/merged_atlas_phoenix'.format(cdbs_path) + if not os.path.exists(newPath): + os.mkdir(newPath) + + # For each metallicity, create merged model + for ii in range(len(output_dir)): + # Make metallicity dir for merged model + final_dir = '{0}/{1}'.format(newPath, output_dir[ii]) + if not os.path.exists(final_dir): + os.mkdir(final_dir) + + # Extract the relevant ATLAS and PHEONIX models near 5250 K + atlas_path = '{0}/grid/ck04models/{1}/'.format(cdbs_path, atlas_dir[ii]) + atlas_hdu = fits.open('{0}/{1}_5250.fits'.format(atlas_path, atlas_dir[ii])) + atlas_5250 = atlas_hdu[1].data + phoenix_path = '{0}/grid/phoenix_v16_rebin/{1}'.format(cdbs_path, phoenix_dir[ii]) + phoenix_hdu1 = fits.open('{0}/{1}_05200.fits'.format(phoenix_path, phoenix_dir[ii])) + phoenix_hdu2 = fits.open('{0}/{1}_05300.fits'.format(phoenix_path, phoenix_dir[ii])) + phoenix_5200 = phoenix_hdu1[1].data + phoenix_5300 = phoenix_hdu2[1].data + print('Done reading input models') + + # Trim both models to the wavelength region we want: VRIJHKL (0.25 - 4.2 mircons) + good = np.where( (atlas_5250['Wavelength'] > 2500) & + (atlas_5250['Wavelength'] < 52000) ) + atlas_5250 = atlas_5250[good] + good = np.where( (phoenix_5200['Wavelength'] > 2500) & + (phoenix_5200['Wavelength'] < 52000) ) + phoenix_5200 = phoenix_5200[good] + phoenix_5300 = phoenix_5300[good] + + #----------------------------# + # For each logg val, create phoenix spectrum for 5250 K, + # degrade resolution to match atlas, then average with + # 5250 K atlas model to get merged model at 5250 K + #----------------------------# + new_model = [] + phoenix_5250_f = [] + atlas_5250_f = [] + for logg in logg_arr: + # Create phoenix models at 5250 K + low = phoenix_5200['g{0:2.1f}'.format(logg)] + high = phoenix_5300['g{0:2.1f}'.format(logg)] + + arr = np.transpose([low, high]) + phoenix_5250 = np.mean(arr, axis=1) + + phoenix_5250_rebin = rebin_spec(phoenix_5200['Wavelength'], phoenix_5250, + atlas_5250['Wavelength']) + + # Store phoenix rebinned average spectrum + phoenix_5250_f.append(phoenix_5250_rebin) + + # Store atlas spectrum for later + grav = str(logg).split('.') + atlas_5250_f.append(atlas_5250['g'+grav[0]+grav[1]]) + + # Now, final 5250 K model will be average of atlas and phoenix + # models (since exactly inbetween 5000 K and 5500 K) + arr = np.transpose([phoenix_5250_rebin, atlas_5250['g'+grav[0]+grav[1]]]) + final = np.mean(arr, axis=1) + + new_model.append(final) + print('Done with logg {0:2.1f}'.format(logg)) + + # Create fits table with new model. First column is wavelength, followed by + # fluxes for different logg + wave = atlas_5250['Wavelength'] # Still same wavelength array as before + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='g0.0', format='E', array=new_model[0]) + c2 = fits.Column(name='g0.5', format='E', array=new_model[1]) + c3 = fits.Column(name='g1.0', format='E', array=new_model[2]) + c4 = fits.Column(name='g1.5', format='E', array=new_model[3]) + c5 = fits.Column(name='g2.0', format='E', array=new_model[4]) + c6 = fits.Column(name='g2.5', format='E', array=new_model[5]) + c7 = fits.Column(name='g3.0', format='E', array=new_model[6]) + c8 = fits.Column(name='g3.5', format='E', array=new_model[7]) + c9 = fits.Column(name='g4.0', format='E', array=new_model[8]) + c10 = fits.Column(name='g4.5', format='E', array=new_model[9]) + c11 = fits.Column(name='g5.0', format='E', array=new_model[10]) + + cols = fits.ColDefs([c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11]) + tbhdr = phoenix_hdu1[1].header + prihdr = phoenix_hdu1[0].header + prihdu = fits.PrimaryHDU(header=prihdr) + tbhdu = fits.BinTableHDU.from_columns(cols, header=tbhdr) + # Add TUNIT keysto tbhdu + tbhdu = make_merged_header(tbhdu) + + # Make final hdu table, save it + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}_05250.fits'.format(final_dir, output_dir[ii]), clobber=True) + + # Test plot, if desired + if plot == True: + py.figure(1, figsize=(10,10)) + py.clf() + # Plot merged model + py.semilogy(wave, wave * new_model[8], 'r-', label='Merged') + # Plot atlas 5250 K model + py.semilogy(wave, wave * atlas_5250_f[8], 'b-', label = 'Atlas') + # Plot phoenix 5250 K model + py.semilogy(wave, wave * phoenix_5250_f[8], 'g-', label = 'Phoenix') + py.legend() + py.xlabel('Wavelength (Angstrom)') + py.ylabel(r'log ($\lambda$ F$_{\lambda}$)') + py.axis([5000, 40000, 2*10**9, 4*10**10]) + py.title('Merged 5250 K Spectrum') + py.savefig('Merged_atlas_phoenix_{0}.png'.format(output_dir[ii])) + + pdb.set_trace() + py.close('all') + + # Copy the atlas 5500 K and phoenix 5000 K models into the + # merged directory to accompany the merged model. This is for + # interpolation purposes for pysynphot + cmd = 'cp {0}/{1}_5500.fits {2}'.format(atlas_path, atlas_dir[ii], final_dir) + cmd2 = 'cp {0}/{1}_05000.fits {2}'.format(phoenix_path, phoenix_dir[ii], final_dir) + os.system(cmd) + os.system(cmd2) + + cmd = 'mv {0}/{1}_5500.fits {0}/{2}_05500.fits'.format(final_dir, atlas_dir[ii], output_dir[ii]) + cmd2 = 'mv {0}/{1}_05000.fits {0}/{2}_05000.fits'.format(final_dir, phoenix_dir[ii], output_dir[ii]) + os.system(cmd) + os.system(cmd2) + + # Close all open hdu + atlas_hdu.close() + phoenix_hdu1.close() + phoenix_hdu2.close() + + print('Done {0}'.format(output_dir[ii])) + + # Make catalog.fits table for new merged model, plus the atlas and + # phoenix models at the high and low extremes of the temp range. Note temp + # of merged model goes first, for filename purposes + catalog, filenames = make_merged_catalog(output_dir, [5250, 5000, 5500], logg_arr, metal_arr) + catalog.write(cdbs_path+'/grid/merged_atlas_phoenix/catalog.fits', overwrite=True) + + return + + + +def create_merged_meisner_btsettl(cdbs_path, plot=False): + """ + From 1000 K - 1200 K, merge the BTSettl_CIFITS2011_2015 and Meisner 2023 atmospheres. + BTSettl at 1200 K, Meisner near 1000 K + + cdbs_path is path to cdbs directory (including cdbs). Will make new directory + in cdbs/grid named "merged_meisner_btsettl" with with merged models. If plot = True, will plot + the normalized merged model plus the original models + + + (Only 1 truely merged model at 3500 K, with log g from 2.5 - 5.5)? + + Creates new directory "merged_meisner_btsettl" in cdbs/grid with new spectrum + catalog file. + (Also includes the atlas 5500K model and phoenix 5000K model, for interpolation purposes)? + """ + # Set log g sampling + logg_solar_arr = np.arange(3.5, 5.5+0.1, 0.5) + logg_nonsolar_arr = np.array([3.5, 5.0]) + + # Setting metallicity sub-directories + meisner_dir = ['mm05', 'mp00'] + output_dir = ['mergedm05', 'mergedp00'] + metal_arr = [-0.5, 0] + + assert len(meisner_dir) == len(output_dir) + + # Make new cdbs merged directory, if it doesn't already exist + newPath = '{0}/grid/merged_meisner_BTSettl'.format(cdbs_path) + if not os.path.exists(newPath): + os.mkdir(newPath) + + # For each metallicity, create merged model + for ii in range(len(output_dir)): + # Make metallicity dir for merged model + final_dir = '{0}/{1}'.format(newPath, output_dir[ii]) + if not os.path.exists(final_dir): + os.mkdir(final_dir) + + # For each gravity, extract BTSettl and Meisner models at 1100 K and + # average them to get the merged model. Also write BTSettl models at + # 1200 K and Meisner models at 1000 K + if metal_arr[ii] == 0: + logg_tmp = logg_solar_arr + BT_func = atmospheres.get_BTSettl_atmosphere + else: + BT_func = atmospheres.get_BTSettl_atmosphere + logg_tmp = logg_nonsolar_arr + + for jj in logg_tmp: + BT_atmo = BT_func(temperature=1100, gravity=jj, rebin=True) + meisner_atmo = atmospheres.get_Meisner2023_atmosphere(temperature=1100, gravity=jj, rebin=True) + + # Check to make sure wavelengths are the same between atmospheres + print("BT_func assigned to:", BT_func) + print(f"BT_func({1100}, {jj}, rebin=True) -> {BT_atmo}") + diff = BT_atmo.wave - meisner_atmo.wave + if np.sum(diff > 0): + print('Wavelength mismatch problem!') + pdb.set_trace() + + merged_flux = np.mean(np.array([meisner_atmo.flux, BT_atmo.flux]), axis=0) + + # Create fits file with new atmosphere + c0 = fits.Column(name='Wavelength', format='D', array=BT_atmo.wave) + c1 = fits.Column(name='Flux', format='E', array=merged_flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Save fits file to merged Meisner-BTSettl direcotry + hdu_new.writeto('{0}/{1}_03500_{2}.fits'.format(final_dir, output_dir[ii], jj), overwrite=True) + + # Make test plot, if desired + if plot: + py.figure(1, figsize=(10,10)) + py.clf() + py.semilogy(BT_atmo.wave, BT_atmo.wave * merged_flux, 'r-', label='Merged') + py.semilogy(BT_atmo.wave, BT_atmo.wave * BT_atmo.flux, 'b-', label = 'BTSettl') + py.semilogy(meisner_atmo.wave, meisner_atmo.wave * meisner_atmo.flux, 'g-', label = 'Meisner') + py.legend() + py.xlabel('Wavelength (Angstrom)') + py.ylabel(r'log ($\lambda$ F$_{\lambda}$)') + py.xlim(5000, 40000) + py.ylim(10**8, 10**10) + py.title('Merged 1100 K Spectrum, Z = {1}, logg = {0}'.format(jj, metal_arr[ii])) + py.savefig('Merged_Meisner_BTSettl_{1}_{0}.png'.format(jj, metal_arr[ii])) + + # Now to bring over the 1000 K Meisner and 1200 K BTSettl models + BT_atmo = BT_func(temperature=1200, gravity=jj, rebin=True) + meisner_atmo = atmospheres.get_Meisner2023_atmosphere(temperature=1000, gravity=jj, rebin=True) + + # Create new fits files for these as well + c0_b = fits.Column(name='Wavelength', format='D', array=BT_atmo.wave) + c1_b = fits.Column(name='Flux', format='E', array=BT_atmo.flux) + c0_m = fits.Column(name='Wavelength', format='D', array=meisner_atmo.wave) + c1_m = fits.Column(name='Flux', format='E', array=meisner_atmo.flux) + + cols_b = fits.ColDefs([c0_b, c1_b]) + tbhdu_b = fits.BinTableHDU.from_columns(cols_b) + cols_m = fits.ColDefs([c0_m, c1_m]) + tbhdu_m = fits.BinTableHDU.from_columns(cols_m) + + prihdu = fits.PrimaryHDU() + tbhdu_b.header['TUNIT1'] = 'ANGSTROM' + tbhdu_b.header['TUNIT2'] = 'FLAM' + tbhdu_m.header['TUNIT1'] = 'ANGSTROM' + tbhdu_m.header['TUNIT2'] = 'FLAM' + + hdu_newb = fits.HDUList([prihdu, tbhdu_b]) + hdu_newm = fits.HDUList([prihdu, tbhdu_m]) + + # Save fits file to merged Meisner-BTSettl direcotry + hdu_newb.writeto('{0}/{1}_03200_{2}.fits'.format(final_dir, output_dir[ii], jj), clobber=True) + hdu_newp.writeto('{0}/{1}_03800_{2}.fits'.format(final_dir, output_dir[ii], jj), clobber=True) + hdu_new.close() + hdu_newb.close() + hdu_newp.close() + + print('Done {0}'.format(output_dir[ii])) + + return + +def make_catalog_merged_models2(path='/g/lu/models/cdbs/grid/merged_BTSettl_phoenix/'): + """ + Make cdbs catalog.fits file for merged BTSettl/phoenix direcotry. path should + point to this directory. + + Writes catalog.fits file in the cdbs directory + """ + output_dir = ['mergedm25', 'mergedm20', 'mergedm15', 'mergedm10', 'mergedm05', 'mergedp00', 'mergedp02', 'mergedp05'] + metal_arr = [-2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.2, 0.5] + + index_str = [] + name_str = [] + for ii in range(len(output_dir)): + files = glob.glob('{0}/{1}/*.fits'.format(path, output_dir[ii])) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + for name in files: + final = name.split('/')[-1] + tmp = final.split('_') + temp = float(tmp[1]) # In kelvin + logg = float(tmp[2][:-5]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_arr[ii], logg)) + name_str.append('{0}/{1}[Flux]'.format(output_dir[ii], final)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write(path+'catalog.fits', format = 'fits', overwrite=True) + + return \ No newline at end of file diff --git a/spisea/reddening.py b/spisea/reddening.py index 2754719b..b0d23c19 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -24,12 +24,12 @@ def get_red_law(str): ---------- str: str Reddening law name and additional params (comma-separated). - Name must match + Name must match """ # Parse the string, extracting redlaw name and other params tmp = str.split(',') name = tmp[0] - + # How we split this up changes for the broken power law EL # versus the other ELs (since we have arrays for broken power law EL) if name == 'broken_pl': @@ -63,7 +63,8 @@ def get_red_law(str): 'S16': RedLawSchlafly16, 'H18b': RedLawHosek18b, 'NL18': RedLawNoguerasLara18, - 'NL20': RedLawNoguerasLara20 + 'NL20': RedLawNoguerasLara20, + 'SODC': RedLawSODC } # Make reddening law object, including params if necessary. @@ -86,12 +87,12 @@ def get_red_law(str): class RedLawNishiyama09(pysynphot.reddening.CustomRedLaw): """ - The extinction law towards the Galactic Center - from `Nishiyama et al. 2009 + The extinction law towards the Galactic Center + from `Nishiyama et al. 2009 `_, - combined with the Av / AKs value from `Nishiyama et al. 2008 - `_. - This law is defined between 0.5 - 8.0 microns. + combined with the Av / AKs value from `Nishiyama et al. 2008 + `_. + This law is defined between 0.5 - 8.0 microns. This law is constructed in 3 segments: @@ -107,7 +108,7 @@ class RedLawNishiyama09(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 3-8 microns wave = np.arange(0.5, 8.0, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 wave_vals, Alambda_scaled = RedLawNishiyama09._derive_nishiyama09(wave) @@ -115,7 +116,7 @@ def __init__(self): # Convert wavelength to angstrom wave_vals *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals, waveunits='angstrom', Avscaled=Alambda_scaled, name='Nishiyama09', @@ -128,10 +129,10 @@ def __init__(self): # Other info self.scale_lambda = 2.14 self.name = 'N09' - + @staticmethod def _derive_nishiyama09(wavelength): - """ + """ Calculate the N09 extinction law as defined in the paper: a A_lambda/AKs = power law of exponent -2.0 between JHK. Then use a *linear* interpolation in 1/lambda space to go from J to the V-band observation, @@ -148,7 +149,7 @@ def _derive_nishiyama09(wavelength): #-----Define power law extinction law between JHK----# jhk_idx = np.where( (wavelength >= 1.25) & (wavelength <= 2.14) ) #jhk_idx = np.where( (wavelength >= 1.25) & (wavelength <= 2.3) ) - + alpha = 2.0 wave_jhk = wavelength[jhk_idx] idx_scale = np.where(abs(wave_jhk - 2.14) == min(abs(wave_jhk - 2.14)) ) @@ -177,10 +178,10 @@ def _derive_nishiyama09(wavelength): #wave = np.array([0.551, 1.25, 1.63, 2.14, wave_jhk[-1], 3.545, 4.442, 5.675, 7.760]) #A_AKs = np.array([16.13, 3.02, 1.73, 1.00, A_Ks_jhk[-1], 0.500, 0.390, 0.360, 0.430]) #interp_idx = np.where(wave > 2.2) - + spline_interp = interpolate.splrep(wave[interp_idx], A_AKs[interp_idx], k=3, s=0) A_AKs_long = interpolate.splev(wavelength[long_idx], spline_interp) - + # Stitch together sections for the final law wave_vals = np.concatenate((wavelength[jv_idx[0]], wavelength[jhk_idx[0]])) A_AKs_vjhk = np.concatenate((A_Ks_jv, A_Ks_jhk)) @@ -192,8 +193,8 @@ def _derive_nishiyama09(wavelength): return wave_vals, A_AKs_final def Nishiyama09(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -213,7 +214,7 @@ def Nishiyama09(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -253,7 +254,7 @@ def plot_Nishiyama09(self): py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Nishiyama+09 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -261,33 +262,33 @@ def plot_Nishiyama09(self): py.savefig('nishiyama09_el.png') return - + class RedLawCardelli(pysynphot.reddening.CustomRedLaw): - """ - Defines the extinction law from - `Cardelli et al. 1989 `_. + r""" + Defines the extinction law from + `Cardelli et al. 1989 `_. The law is defined from 0.3 - 3 microns, and in terms of :math:`A_{\lambda} / A_{Ks}`, where Ks is 2.174 microns. Parameters ---------- Rv : float - Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. + Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. The standard value for the diffuse ISM is 3.1. """ def __init__(self, Rv): # Fetch the extinction curve, pre-interpolate across 0.3-3 microns wave = np.arange(0.3, 3.0, 0.001) - + # This will eventually be scaled by AKs when you - # call reddening(). Produces A_lambda for AKs = 1, which will be + # call reddening(). Produces A_lambda for AKs = 1, which will be # scaled later. Expects wavelength in microns Alambda_scaled = RedLawCardelli._derive_cardelli(wave, Rv) # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Cardelli89', @@ -298,7 +299,7 @@ def __init__(self, Rv): self.high_lim = max(wave) # other info - self.scale_lambda = 2.174 + self.scale_lambda = 2.174 self.name = 'C89,{0}'.format(Rv) @staticmethod @@ -317,7 +318,7 @@ def _derive_cardelli(wavelength, Rv): if (np.max(x) > 8.0): print( 'wavelength is shorter than applicable range for Cardelli law') return None - + # Set up some arrays for coefficients that we will need a = np.zeros(len(x), dtype=float) b = np.zeros(len(x), dtype=float) @@ -366,14 +367,14 @@ def _derive_cardelli(wavelength, Rv): k_ind = np.where(abs(x-0.46) == min(abs(x-0.46))) Aks_Av = a[k_ind] + b[k_ind]/Rv # Aks / Av Av_Aks = 1.0 / Aks_Av # Av / Aks - + output = extinction * Av_Aks # (A(lamb) / Av) * (Av / Aks) = (A(lamb) / Aks) return output def Cardelli89(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -393,7 +394,7 @@ def Cardelli89(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -409,12 +410,149 @@ def Cardelli89(self, wavelength, AKs): A_at_wave = np.array(A_AKs_at_wave) * AKs return A_at_wave - + +class RedLawSODC(pysynphot.reddening.CustomRedLaw): + r""" + Defines the SODC extinction law from SynthPop, described by + `Klüter & Huston et al. (2025) `_. + It is based on the `Cardelli et al. (1989) `_ + formulation with an updated optical end from `O'Donnell et al (1994) + `_ and infrared side adjusted to match + `Surot et al. (2020) `_. + The law is defined from 0.25 - 3.5 microns, and in terms + of :math:`A_{\lambda} / A_{Ks}`, where Ks is 2.174 microns. + + Parameters + ---------- + Rv : float + Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. + The standard value for the diffuse ISM is 3.1. Toward the Galactic + bulge, 2.5 is more typical. + """ + def __init__(self, Rv): + # Fetch the extinction curve, pre-interpolate across 0.3-3 microns + wave = np.arange(0.25, 3.5, 0.001) + + # This will eventually be scaled by AKs when you + # call reddening(). Produces A_lambda for AKs = 1, which will be + # scaled later. Expects wavelength in microns + Alambda_scaled = RedLawSODC._derive_sodc(wave, Rv) + + # Convert wavelength to angstrom + wave *= 10 ** 4 + + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + waveunits='angstrom', + Avscaled=Alambda_scaled, + name='SODC', + litref='Klüter & Huston + 2025') + + # Set the upper/lower wavelength limits of law (in angstroms) + self.low_lim = min(wave) + self.high_lim = max(wave) + + # other info + self.scale_lambda = 0.549 + self.name = 'SODC,{0}'.format(Rv) + + @staticmethod + def _derive_sodc(wavelength, Rv): + """ + SODC extinction law. This produces extinction values expected + for AKs = 1 + """ + x = 1.0 / np.array(wavelength) + + # check for applicability + if (np.min(wavelength) < 0.25): + print( 'wavelength is shorter than applicable range for SODC law') + return None + + if (np.max(wavelength) > 3.5): + print( 'wavelength is longer than applicable range for SODC law') + return None + + # Set up some arrays for coefficients that we will need + a = np.zeros(len(x), dtype=float) + b = np.zeros(len(x), dtype=float) + + y = x - 1.82 + + # Calculate coefficients for long wavelengths (low wavenumber) + # Wavenumger <= 1.1 + idx = np.where(x <= 1.1)[0] + a[idx] = 0.53974 * x[idx] ** 2.255 + b[idx] = -0.495567 * x[idx] ** 2.255 + + # Calculate coefficients for short wavelengths + # 1.1 < wavenumber + idx = np.where((x > 1.1))[0] + yy = y[idx] + a[idx] = 1 + (0.104 * yy) - (0.609 * yy ** 2) + \ + (0.701 * yy ** 3) + (1.137* yy ** 4) - \ + (1.718 * yy ** 5) - (0.827 * yy ** 6) + \ + (1.647 * yy ** 7) - (0.505 * yy ** 8) + b[idx] = (1.952 * yy) + (2.908 * yy ** 2) - \ + (3.989 * yy ** 3) - (7.985 * yy ** 4) + \ + (11.102 * yy ** 5) + (5.491 * yy ** 6) - \ + (10.805 * yy ** 7) + (3.347 * yy ** 8) + + # A(lam) / A(V), from Eq. 1 + extinction = a + b/Rv + + # Now, want to produce A_lambda / AKs, to match other laws + k_ind = np.argmin(abs(x-0.46)) + Aks_Av = a[k_ind] + b[k_ind]/Rv # Aks / Av + Av_Aks = 1.0 / Aks_Av # Av / Aks + + output = extinction * Av_Aks # (A(lamb) / Av) * (Av / Aks) = (A(lamb) / Aks) + + return output + + def SODC(self, wavelength, AKs): + """ + Return the extinction at a given wavelength assuming the + extinction law and an overall `AKs` value. + + Parameters + ---------- + wavelength : float or array + Wavelength to return extinction for, in microns + AKs : float + Total extinction in AKs, in mags + """ + # If input entry is a single float, turn it into an array + try: + len(wavelength) + except: + wavelength = [wavelength] + + # Return error if any wavelength is beyond interpolation range of + # extinction law + if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + + # Extract wave and A/AKs from law, turning wave into micron units + wave = self.wave * (10**-4) + law = self.obscuration + + # Find the value of the law at the closest points + # to wavelength + A_AKs_at_wave = [] + for ii in wavelength: + idx = np.where( abs(wave - ii) == min(abs(wave - ii)) ) + A_AKs_at_wave.append(law[idx][0]) + + # Now multiply by AKs (since law assumes AKs = 1) + A_at_wave = np.array(A_AKs_at_wave) * AKs + + return A_at_wave + class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw): """ Defines extinction law from `Roman-Zuniga et al. 2007 `_ - for the dense cloud core Barnard 59. The law is a cubic spline fit + for the dense cloud core Barnard 59. The law is a cubic spline fit to the values of A_lambda / A_Ks derived using the color-color diagrams slopes in their Table 1. It is defined between 1.0 - 8.0 microns. @@ -424,7 +562,7 @@ class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 1-8 microns wave = np.arange(1.0, 8.0, 0.01) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawRomanZuniga07._derive_romanzuniga07(wave) @@ -432,7 +570,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10**4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='RomanZuniga07', @@ -452,7 +590,7 @@ def _derive_romanzuniga07(wavelength): wave = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760]) A_AKs = np.array([2.299, 1.550, 1.000, 0.618, 0.525, 0.462, 0.455]) A_AKs_err = np.array([0.530, 0.080, 0.000, 0.077, 0.063, 0.055, 0.059]) - + # Interpolate over the curve spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) @@ -460,8 +598,8 @@ def _derive_romanzuniga07(wavelength): return A_AKs_at_wave def RomanZuniga07(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -481,7 +619,7 @@ def RomanZuniga07(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -519,7 +657,7 @@ def plot_RomanZuniga07(self): py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Roman-Zuniga+07 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -538,7 +676,7 @@ class RedLawRiekeLebofsky(pysynphot.reddening.CustomRedLaw): def __init__(self): # Define the wavelength range of the extinction law wave = np.arange(1.0, 5.0, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawRiekeLebofsky._derive_RiekeLebofsky(wave) @@ -546,7 +684,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='RiekeLebofsky', @@ -569,17 +707,17 @@ def _derive_RiekeLebofsky(wavelength): Data pulled from Rieke+Lebofsky 1985, Table 3. Note that their Table contains values from 0.3 - 13 microns, but only 1 - 5 microns - is measured directly. + is measured directly. Wavelengths for filters I - M are from Rieke+89, table 4. """ # Arrays with the values from the paper - filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', - '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', + filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', + '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]'] - wave = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, + wave = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, 3.50, 4.8, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, - 11.5, 12.0, 12.5, 13.0]) + 11.5, 12.0, 12.5, 13.0]) A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112, 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083, 0.074, 0.060, 0.047, 0.037, 0.030, 0.027]) @@ -597,7 +735,7 @@ def _derive_RiekeLebofsky(wavelength): wave_interp = wave[idx1:idx2+1] A_Ak_interp = A_Ak[idx1:idx2+1] assert len(wave_interp) == 6 - + # Interpolate over the curve over desired wavelength range spline_interp = interpolate.splrep(wave_interp, A_Ak_interp, k=3, s=0) A_Ak_at_wave = interpolate.splev(wavelength, spline_interp) @@ -605,8 +743,8 @@ def _derive_RiekeLebofsky(wavelength): return A_Ak_at_wave def RiekeLebofsky85(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -626,7 +764,7 @@ def RiekeLebofsky85(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -657,12 +795,12 @@ def plot_RiekeLebofsky85(self): # Get the observed values from their Table 3. Note only JHKLM is # measured directly by RL85, other values come from elsewhere. # Wavelengths are from Rieke+89, Table 4 - filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', - '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', + filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', + '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]'] - wave_obs = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, + wave_obs = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, 3.50, 4.8, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, - 11.5, 12.0, 12.5, 13.0]) + 11.5, 12.0, 12.5, 13.0]) A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112, 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083, 0.074, 0.060, 0.047, 0.037, 0.030, 0.027]) @@ -687,7 +825,7 @@ def plot_RiekeLebofsky85(self): py.plot(wave_obs_f, A_Ak_f, 'k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Rieke+Lebofsky+85 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -708,7 +846,7 @@ class RedLawDamineli16(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 0.4-4.8 microns wave = np.arange(0.4, 4.8, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawDamineli16._derive_Damineli16(wave) @@ -716,7 +854,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Damineli16', @@ -730,7 +868,7 @@ def __init__(self): self.name = 'D16' return - + @staticmethod def _derive_Damineli16(wavelength): """ @@ -748,13 +886,13 @@ def _derive_Damineli16(wavelength): log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4. # Now to convert this back to linear space - A_AKs_at_wave = 10**log_A_AKs + A_AKs_at_wave = 10**log_A_AKs return A_AKs_at_wave def Damineli16(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -774,7 +912,7 @@ def Damineli16(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -814,14 +952,14 @@ def plot_Damineli16(self): py.errorbar(wave_obs, A_AKs, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Damineli+16 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') py.legend() py.savefig('damineli16_el.png') return - + class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw): """ Defines extinction law from `De Marchi et al. 2016 @@ -831,7 +969,7 @@ class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 1-8 microns wave = np.arange(0.3, 8.0, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawDeMarchi16._derive_DeMarchi16(wave) @@ -839,7 +977,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='DeMarchi16', @@ -891,8 +1029,8 @@ def _derive_DeMarchi16(wavelength): return A_AK_at_wave def DeMarchi16(self, wavelength, AK): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -912,7 +1050,7 @@ def DeMarchi16(self, wavelength, AK): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -928,32 +1066,32 @@ def DeMarchi16(self, wavelength, AK): A_at_wave = np.array(A_AKs_at_wave) * AK return A_at_wave - + class RedLawFitzpatrick09(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from + Defines the extinction law from `Fitzpatrick et al. 2009 `_. The law is defined between 0.5 -- 3 microns. The extinction law is as defined in their equation 5, and has two free parameters: :math:`\alpha` and R(V). Averaged over 14 sight-lines, - the authors generally find either :math:`alpha` ~ 2.5, R(V) ~ 3, or - :math:`alpha` ~ 1.8, R(V) ~ 5 (their Figure 6). + the authors generally find either :math:`alpha` ~ 2.5, R(V) ~ 3, or + :math:`alpha` ~ 1.8, R(V) ~ 5 (their Figure 6). A_lambda / A_K = 1 at lambda = 2.18 Parameters ---------- alpha : float - alpha parameter for extinction law. + alpha parameter for extinction law. RV : float - R(V) parameter for extinction law. + R(V) parameter for extinction law. """ def __init__(self, alpha, RV): # Fetch the extinction curve, pre-interpolate across 1-8 microns wave = np.arange(0.5, 3.0, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawFitzpatrick09._derive_Fitzpatrick09(wave, alpha, RV) @@ -961,7 +1099,7 @@ def __init__(self, alpha, RV): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Fitzpatrick09', @@ -995,14 +1133,14 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV): """ alpha = float(alpha) RV = float(RV) - + # First we'll calculate k(lambda - V) = E(lambda - V) / E(B - V), # directly from equation 5 k = (0.349 + 2.087*RV) * (1.0 / (1.0 + (wavelength / 0.507)**alpha)) - RV # We'll calculate Alam/Av from K + Rv - Alam_Av = (k / RV) + 1. - + Alam_Av = (k / RV) + 1. + # Finally, to get A_lambda/Aks we need to divide Alam_Av by AKs_Av. # We'll assume a wavelength of 2.18 for Ks, since it is the wavelength # they report for K-band @@ -1013,8 +1151,8 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV): return A_AKs_at_wave def Fitzpatrick09(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1034,7 +1172,7 @@ def Fitzpatrick09(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1053,7 +1191,7 @@ def Fitzpatrick09(self, wavelength, AKs): class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from `Schlafly et al. 2016 + Defines the extinction law from `Schlafly et al. 2016 `_. The law is defined between 0.5 - 4.8 microns. @@ -1069,7 +1207,7 @@ class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw): def __init__(self, AH_AKs, x): # Fetch the extinction curve, pre-interpolate across 0.5-4.8 microns wave = np.arange(0.5, 4.8, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawSchlafly16._derive_Schlafly16(wave, AH_AKs, x) @@ -1077,7 +1215,7 @@ def __init__(self, AH_AKs, x): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Schlafly16', @@ -1091,7 +1229,7 @@ def __init__(self, AH_AKs, x): @staticmethod def _derive_Schlafly16(wavelength, AH_AKs, x): """ - Calculate Schalfly+16 extinction law according to + Calculate Schalfly+16 extinction law according to code provided in appendix of the paper. AH_AKs sets the gray component while x sets the shape of the law in an Rv-like way @@ -1102,20 +1240,20 @@ def _derive_Schlafly16(wavelength, AH_AKs, x): # Evaluate function for desired wavelengths (in angstroms) law = law_func(wavelength*10**4) - + # Now normalize to A_lambda/AKs, rather than A_lambda/A(5420) idx = np.where( abs(wavelength - 2.151) == min(abs(wavelength - 2.151)) ) law_out = law / law[idx] - + return law_out @staticmethod def _Schlafly_appendix(x, rhk): - """ + """ Schlafly+16 extinction law as defined in paper appendix. We've modified - the wrapper slightly so that the user has control of rhk and x. Here is + the wrapper slightly so that the user has control of rhk and x. Here is the comments from that code: - + Returns the extinction curve, A(lambda)/A(5420 A), according to Schlafly+2016, for the parameter "x," which controls the overall shape of the extinction curve in an R(V)-like way. The extinction curve returned @@ -1137,7 +1275,7 @@ def _Schlafly_appendix(x, rhk): lam: anchor wavelengths (angstroms), default to Schlafly+2016 Returns: the extinction curve E, so the extinction alam = A(lam)/A(5420 A) - is given by: + is given by: A = extcurve(x) alam = A(lam) """ @@ -1162,8 +1300,8 @@ def _Schlafly_appendix(x, rhk): return CubicSpline(lam, anchors/cs0(5420.), yp='3d=0') def Schlafly16(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1183,7 +1321,7 @@ def Schlafly16(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1202,7 +1340,7 @@ def Schlafly16(self, wavelength, AKs): class RedLawIndebetouw05(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from `Indebetouw et al. 2005 + Defines the extinction law from `Indebetouw et al. 2005 `_. The law is defined between 1.25 - 8 microns using Equation 4 in their paper. @@ -1217,7 +1355,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Indebetouw05', @@ -1243,9 +1381,9 @@ def _derive_Indebetouw05(wave): Alambda_AK = 10**log_Alambda_AK return Alambda_AK - def Indebetouw05(self, wavelength, AK): - """ - Return the extinction at a given wavelength assuming the + def Indebetouw05(self, wavelength, AKs): + """ + Return the extinction at a given wavelength assuming the extinction law and an overall extinction at AK (2.164 microns) Parameters @@ -1265,7 +1403,7 @@ def Indebetouw05(self, wavelength, AK): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1284,7 +1422,7 @@ def Indebetouw05(self, wavelength, AK): def plot_Indebetouw05(self): """ - Plot Indebetouw+05 extinciton curve versus their + Plot Indebetouw+05 extinciton curve versus their actual measured values (their Table 1). This is similar to their Figure 6. @@ -1296,20 +1434,20 @@ def plot_Indebetouw05(self): # Convert wave to microns for plot wave *= 10**-4 - + # Their average measurements across sight lines # from Table 1 wave_arr = [1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760] law_obs_arr = [2.50, 1.55, 1.0, 0.56, 0.43, 0.43, 0.43] law_obs_err_arr = [0.15, 0.08, 0.0, 0.06, 0.08, 0.10, 0.10] - + # Make plot py.figure(figsize=(10,10)) py.plot(wave, law, 'r-', label='EL Function') py.errorbar(wave_arr, law_obs_arr, yerr=law_obs_err_arr, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Indebetouw+05 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -1317,14 +1455,14 @@ def plot_Indebetouw05(self): py.savefig('indebetouw05_el.png') return - + class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw): - """ - Extinction object that is a power-law extinction law: + r""" + Extinction object that is a power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha}`. - For example, to create an extinction law between - 0.8 and 3 microns where :math:`\alpha = 2.21`, + For example, to create an extinction law between + 0.8 and 3 microns where :math:`\alpha = 2.21`, where :math:`A_{\lambda} / A_{Ks} = 1` at 2.12 microns: >>> from spisea import reddening @@ -1349,7 +1487,7 @@ class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw): def __init__(self, alpha, K_wave, wave_min=0.5, wave_max=5.0): # Fetch the extinction curve, pre-interpolate across wave_min to wave_max wave = np.arange(wave_min, wave_max, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawPowerLaw._derive_powerlaw(wave, alpha, K_wave) @@ -1357,7 +1495,7 @@ def __init__(self, alpha, K_wave, wave_min=0.5, wave_max=5.0): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Power law') @@ -1379,8 +1517,8 @@ def _derive_powerlaw(wavelength, alpha, K_wave): in microns alpha: float - -1.0 * (power law exponent) - + -1.0 * (power law exponent) + K_wave: float Desired K-band wavelength, in microns """ @@ -1394,8 +1532,8 @@ def _derive_powerlaw(wavelength, alpha, K_wave): return A_AKs_at_wave def powerlaw(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1415,7 +1553,7 @@ def powerlaw(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1433,23 +1571,23 @@ def powerlaw(self, wavelength, AKs): return A_at_wave class RedLawBrokenPowerLaw(pysynphot.reddening.CustomRedLaw): - """ - Extinction object that is a broken power-law extinction law: + r""" + Extinction object that is a broken power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha[n]}` - for: + for: :math: `\lambda_{limits}[n] < \lambda <= \lambda_{limits}[n+1]` - Note: lambda_limits must be continuous in wavelength and K_wave must be - within one of the section defined by the lambda_limits array. + Note: lambda_limits must be continuous in wavelength and K_wave must be + within one of the section defined by the lambda_limits array. Extinction law is only defined over lambda_limits - + Units of lambda_limits array is microns. Parameters ---------- lambda_limits : numpy array - Array of length (N + 1) with lower and upper wavelength limits of + Array of length (N + 1) with lower and upper wavelength limits of the power-law segments. Units are microns. alpha_vals : numpy array @@ -1480,7 +1618,7 @@ def __init__(self, lambda_limits, alpha_vals, K_wave): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Broken Power law') @@ -1502,8 +1640,8 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): in microns alpha: float - -1.0 * (power law exponent) - + -1.0 * (power law exponent) + K_wave: float Desired K-band wavelength, in microns """ @@ -1529,14 +1667,14 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): #print('wave_connect = {0}'.format(wave_connect)) #print('alph_num = {0}'.format(alpha_vals[jj])) #print('alpha_den = {0}'.format(alpha_vals[jj+1])) - + coeff *= val - + law[idx] = coeff * (wave[idx]**(-1.0 * alpha)) # Let's make sure we didn't miss updating any parts of the law assert np.sum(np.isnan(law)) == 0 - + # We'll identify K-band as 2.14 microns idx = np.where(abs(wave - K_wave) == min(abs(wave - K_wave))) A_AKs_at_wave = law / law[idx] @@ -1544,8 +1682,8 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): return A_AKs_at_wave def broken_powerlaw(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1565,7 +1703,7 @@ def broken_powerlaw(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1584,7 +1722,7 @@ def broken_powerlaw(self, wavelength, AKs): class RedLawFritz11(pysynphot.reddening.CustomRedLaw): """ - Defines extinction law from `Fritz et al. 2011 + Defines extinction law from `Fritz et al. 2011 `_ for the Galactic Center. The law is defined from 1.0 -- 26 microns. @@ -1613,12 +1751,12 @@ def __init__(self, scale_lambda=2.166): ext_scale = ext / ext[idx] # Make custom reddening law - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=ext_scale, name='Fritz11', litref='Fritz+2011') - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) self.high_lim = max(wave) @@ -1628,13 +1766,13 @@ def __init__(self, scale_lambda=2.166): self.name = 'F11' return - + @staticmethod def _read_Fritz11(): """ - Return the interpolated extinction curve from Fritz+11, - as defined in their Table 8. - + Return the interpolated extinction curve from Fritz+11, + as defined in their Table 8. + Output: ------ wave: array @@ -1649,7 +1787,7 @@ def _read_Fritz11(): # Read in file with Table 8 info (published with Fritz+11 paper) inpath = os.path.dirname(os.path.abspath(__file__)) infile = os.path.join(inpath, 'el_files', 'fritz11_EL_table8.fits') - + t = Table.read(infile, format='fits') wave = t['lambda'] ext = t['A'] @@ -1661,7 +1799,7 @@ def _read_Fritz11(): def _read_Fritz11_obs(): """ Return the Fritz+11 observed values, from their Table 2 - + Output: ------- wave: array @@ -1703,7 +1841,7 @@ def plot_Fritz11(self): # extinction at 2.166 microns. Remember that this produces # throughput = 10^-0.4*Alambda ext_scaled = self.reddening(2.62) - + # Make plot py.figure(figsize=(10,10)) py.plot(wave, ext, 'r-', label='Interpolated EL') @@ -1713,7 +1851,7 @@ def plot_Fritz11(self): label='Measured') py.plot(ext_scaled.wave*10**-4, np.log10(ext_scaled.throughput)/-0.4, 'b-', label='Scaled EL') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Fritz+11 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -1723,8 +1861,8 @@ def plot_Fritz11(self): return def Fritz11(self, wavelength, A_scale_lambda): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and a total extinction at the scale_lambda (the wavelength where the extinction law = 1) @@ -1745,7 +1883,7 @@ def Fritz11(self, wavelength, A_scale_lambda): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/A_scale_lambda from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1768,18 +1906,18 @@ def Fritz11(self, wavelength, A_scale_lambda): #==============================================# #class RedLawHosek18(pysynphot.reddening.CustomRedLaw): # """ -# Defines extinction law from `Hosek et al. 2018 +# Defines extinction law from `Hosek et al. 2018 # `_ -# for the Arches Cluster and Wd1. The law is defined between +# for the Arches Cluster and Wd1. The law is defined between # 0.7 - 3.54 microns. # -# WARNING: DEPRECATED! This law has revised to RedLawHosek18b, which +# WARNING: DEPRECATED! This law has revised to RedLawHosek18b, which # should be used instead # """ # def __init__(self): # # Fetch the extinction curve, pre-interpolate across 3-8 microns # wave = np.arange(0.7, 3.545, 0.001) -# +# # # This will eventually be scaled by AKs when you # # call reddening(). Right now, calc for AKs=1 # Alambda_scaled = RedLawHosek18._derive_Hosek18(wave) @@ -1787,7 +1925,7 @@ def Fritz11(self, wavelength, A_scale_lambda): # # Convert wavelength to angstrom # wave *= 10 ** 4 # -# pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, +# pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, # waveunits='angstrom', # Avscaled=Alambda_scaled, # name='Hosek+18', @@ -1797,12 +1935,12 @@ def Fritz11(self, wavelength, A_scale_lambda): # self.low_lim = min(wave) # self.high_lim = max(wave) # self.name = 'H18' -# +# # @staticmethod # def _derive_Hosek18(wavelength): -# """ -# Derive the Hosek+18 extinction law, using the data from Table 4. -# +# """ +# Derive the Hosek+18 extinction law, using the data from Table 4. +# # Calculate the resulting extinction for an array of wavelengths. # The extinction is normalized with A_Ks. # @@ -1816,19 +1954,19 @@ def Fritz11(self, wavelength, A_scale_lambda): # # Extinction law definition # wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545]) # A_AKs = np.array([9.66, 6.29, 3.56, 2.33, 1.0, 0.50]) -# +# # # # Following Hosek+18, Interpolate over the curve with cubic spline interpolation # spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) # A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) # # # This curve already assumes A_Ks = 1.0, so we can go straight to -# # output +# # output # return A_AKs_at_wave # # def Hosek18(self, wavelength, AKs): -# """ -# Return the extinction at a given wavelength assuming the +# """ +# Return the extinction at a given wavelength assuming the # extinction law and an overall `AKs` value. # # Parameters @@ -1848,7 +1986,7 @@ def Fritz11(self, wavelength, A_scale_lambda): # # extinction law # if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): # return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) -# +# # # Extract wave and A/AKs from law, turning wave into micron units # wave = self.wave * (10**-4) # law = self.obscuration @@ -1868,7 +2006,7 @@ def Fritz11(self, wavelength, A_scale_lambda): class RedLawHosek18b(pysynphot.reddening.CustomRedLaw): """ - Defines extinction law from `Hosek et al. 2019 + Defines extinction law from `Hosek et al. 2019 `_ for the Arches cluster and Wd1. The law is derived between 0.7 - 3.54 microns @@ -1876,7 +2014,7 @@ class RedLawHosek18b(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 3-8 microns wave = np.arange(0.7, 3.545, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawHosek18b._derive_Hosek18b(wave) @@ -1884,7 +2022,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Hosek+18b', @@ -1894,12 +2032,12 @@ def __init__(self): self.low_lim = min(wave) self.high_lim = max(wave) self.name = 'H18b' - + @staticmethod def _derive_Hosek18b(wavelength): - """ - Derive the Hosek+18 extinction law, using the data from Table 4. - + """ + Derive the Hosek+18 extinction law, using the data from Table 4. + Calculate the resulting extinction for an array of wavelengths. The extinction is normalized with A_Ks. @@ -1913,18 +2051,18 @@ def _derive_Hosek18b(wavelength): # Extinction law definition wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545]) A_AKs = np.array([7.943, 5.715, 3.142, 2.04, 1.0, 0.50]) - + # Following Hosek+18, Interpolate over the curve with cubic spline interpolation spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) # This curve already assumes A_Ks = 1.0, so we can go straight to - # output + # output return A_AKs_at_wave def Hosek18b(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1944,7 +2082,7 @@ def Hosek18b(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1967,15 +2105,15 @@ class RedLawSchoedel10(RedLawBrokenPowerLaw): `_ for the Galactic Center. It is defined between 1.5 - 3.8 microns. - Power law indices: + Power law indices: * 1.677 - 2.168 microns ---> alpha = 2.21 +/- 0.24 * 2.168 - 3.636 microns ---> alpha = 1.34 +/- 0.29 - Wavelengths come from effective wavelengths of observations (some buffer + Wavelengths come from effective wavelengths of observations (some buffer is added to either side of these values). - - Reddening law is scaled such that A_lambda / A_Ks = 1 at + + Reddening law is scaled such that A_lambda / A_Ks = 1 at lambda = 2.168 microns. """ def __init__(self): @@ -1983,7 +2121,7 @@ def __init__(self): alpha_vals = [1.34, 2.21] K_wave = 2.168 RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave) - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = np.min(lambda_limits)*10**4 self.high_lim = np.max(lambda_limits)*10**4 @@ -1995,8 +2133,8 @@ def __init__(self): return def Schoedel10(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and a total extinction at scale_lambda (the wavelength where the extinction law = 1) @@ -2016,7 +2154,7 @@ def Schoedel10(self, wavelength, AKs): # Return error if any wavelength is beyond interpolation range of # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): - return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) @@ -2032,21 +2170,21 @@ def Schoedel10(self, wavelength, AKs): # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_at_wave + - class RedLawNoguerasLara18(RedLawPowerLaw): """ - Defines extinction law from `Nogueras-Lara et al. 2018 + Defines extinction law from `Nogueras-Lara et al. 2018 `_ for the Galactic Center. It is defined between 1.0 - 3.0 microns. - Measurements were made in JHK, with effective wavelengths + Measurements were made in JHK, with effective wavelengths of 1.2685, 1.6506, and 2.1629 microns, respectively. - This extinction law is a single power law with exponent + This extinction law is a single power law with exponent of alpha = 2.3 +/- 0.08. - Reddening law is scaled such that A_lambda / A_Ks = 1 at + Reddening law is scaled such that A_lambda / A_Ks = 1 at lambda = 2.163 microns (the observed K-band) """ def __init__(self): @@ -2054,7 +2192,7 @@ def __init__(self): wave_max = 3.0 K_wave = 2.163 RedLawPowerLaw.__init__(self, 2.30, K_wave, wave_min=wave_min, wave_max=wave_max) - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = wave_min*10**4 self.high_lim = wave_max*10**4 @@ -2063,8 +2201,8 @@ def __init__(self): self.name = 'NL18' def NoguerasLara18(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -2083,7 +2221,7 @@ def NoguerasLara18(self, wavelength, AKs): # Return error if any wavelength is beyond interpolation range of # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): - return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) @@ -2106,18 +2244,18 @@ class RedLawNoguerasLara20(RedLawBrokenPowerLaw): Defines extinction law from `Nogueras-Lara et al. 2020 `_ for the Galactic Center. It is defined between 1.0 -- 3 microns. - Measurements were made in JHK, with effective wavelengths + Measurements were made in JHK, with effective wavelengths of 1.2685, 1.6506, and 2.1629 microns, respectively - Measured power law indices: - + Measured power law indices: + * 1.2685 - 1.6505 microns ---> alpha = 2.44 +/- 0.05 * 1.6505 - 2.1629 microns ---> alpha = 2.23 +/- 0.05 - Wavelengths come from effective wavelengths of observations (some buffer + Wavelengths come from effective wavelengths of observations (some buffer is added to either side of these values). - - Reddening law is scaled such that A_lambda / A_Ks = 1 at + + Reddening law is scaled such that A_lambda / A_Ks = 1 at lambda = 2.163 microns (the observed K-band) """ def __init__(self): @@ -2125,7 +2263,7 @@ def __init__(self): alpha_vals = [2.44, 2.23] K_wave = 2.163 RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave) - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = np.min(lambda_limits)*10**4 self.high_lim = np.max(lambda_limits)*10**4 @@ -2137,8 +2275,8 @@ def __init__(self): return def NoguerasLara20(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and a total extinction at scale_lambda (the wavelength where the extinction law = 1) @@ -2158,7 +2296,7 @@ def NoguerasLara20(self, wavelength, AKs): # Return error if any wavelength is beyond interpolation range of # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): - return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) @@ -2174,7 +2312,7 @@ def NoguerasLara20(self, wavelength, AKs): # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_at_wave #---------------------------# # Cubic spline function from Schalfly+16 appendix @@ -2224,4 +2362,4 @@ def __init__(self, x, y, yp=None): y2 = solve_banded((1,1), mat, bb) self.x, self.y, self.y2 = (x, y, y2) def __call__(self, x): - return splint(self, x) + return splint(self, x) \ No newline at end of file diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 20e6bcd7..9553a87f 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1,32 +1,24 @@ +import os +import time +import math +import datetime +import scipy +import scipy.interpolate +import inspect +import warnings import numpy as np import pylab as plt -from spisea import reddening -from spisea import evolution +import matplotlib.pyplot as plt +from spisea import reddening, evolution, filters from spisea import atmospheres as atm -from spisea import filters -from spisea.imf import imf, multiplicity -from scipy import interpolate -from scipy import stats -from scipy.special import erf +from scipy.spatial import cKDTree as KDTree +from scipy.stats import truncnorm +from spisea.imf import multiplicity from pysynphot import spectrum from pysynphot import ObsBandpass from pysynphot import observation as obs -import pysynphot from astropy import constants, units -from astropy.table import Table, Column, MaskedColumn -import pickle -import time, datetime -import math -import os, glob -import tempfile -import scipy -import matplotlib -import matplotlib.pyplot as plt -import time -import warnings -import pdb -from scipy.spatial import cKDTree as KDTree -import inspect +from astropy.table import Table, Column import astropy.modeling import sys @@ -39,7 +31,7 @@ def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 # These parameters are defined in Girardi+02 - vega = atm.get_kurucz_atmosphere(temperature=9550, + vega = atm.get_kurucz_atmosphere(temperature=9550, gravity=3.95, metallicity=-0.5) @@ -49,22 +41,39 @@ def Vega(): # This is (R/d)**2 as reported by Girardi et al. 2002, page 198, col 1. # and is used to convert to flux observed at Earth. - vega *= 6.247e-17 - + vega *= 6.247e-17 + return vega vega = Vega() +class Interpolator(object): + def __init__(self, xp, yp): + """Wrapper for np.interp to allow for pickling in multiprocessing. + + Parameters + ---------- + xp: array-like + x data points for interpolation + yp: array-like + y data points for interpolation + """ + self.xp = xp + self.yp = yp + + def __call__(self, x): + return np.interp(x, self.xp, self.yp, left=np.nan, right=np.nan) + class Cluster(object): """ Base class to create a cluster with user-specified isochrone, - imf, ifmr, and total mass. + imf, ifmr, and total mass. Parameters ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -77,9 +86,9 @@ class Cluster(object): no compact remnants are produced. seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None + Seed for the random number generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, + unless a different generator is passed in as an argument to the function, by default None. vebose: boolean True for verbose output. @@ -92,17 +101,18 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, self.ifmr = ifmr self.cluster_mass = cluster_mass self.seed = seed - + self.rng = np.random.default_rng(self.seed) + return - + class ResolvedCluster(Cluster): """ Cluster sub-class that produces a *resolved* stellar cluster. - A table is output with the synthetic photometry and intrinsic - properties of the individual stars (or stellar systems, if + A table is output with the synthetic photometry and intrinsic + properties of the individual stars (or stellar systems, if mutliplicity is used in the IMF object). - If multiplicity is used, than a second table is produced that + If multiplicity is used, than a second table is produced that contains the properties of the companion stars independent of their primary stars. @@ -110,31 +120,63 @@ class ResolvedCluster(Cluster): ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object cluster_mass: float Total initial mass of the cluster, in M_sun - ifmr: ifmr object or None + ifmr: ifmr object, optional If ifmr object is defined, will create compact remnants produced by the cluster at the given isochrone age. Otherwise, no compact remnants are produced. + By default None. - keep_low_mass_stars: boolean (default False) - If True, the cluster will not cut out stars below the isochrone grid - on initial mass. They are assigned a current mass equal to their initial + keep_low_mass_stars: boolean, optional + If True, the cluster will not cut out stars below the isochrone grid + on initial mass. They are assigned a current mass equal to their initial mass, a phase of 98, and no other evolutionary properties or photometry. If False, stars below the isochrone initial mass limit are cut out. + By default False. - seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None + seed: int, optional + Seed for the random number generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, + unless a different generator is passed in as an argument to the function, by default None. - vebose: boolean + vebose: boolean, optional True for verbose output. + + Attributes + ---------- + star_systems: astropy.table.Table + Table containing the properties of the primary stars (or stellar systems, if multiplicity is used). The columns include: + mass: primary mass + isMultiple: boolean for whether the star is in a multiple system + systemMass: total initial mass of the stellar system (primary + companions) + Teff: effective temperature of the star + L: luminosity of the star in L_sun + logg: surface gravity of the star in cgs + isWR: boolean for whether the star is a Wolf-Rayet star + mass_current: current mass of the star + phase: evolutionary phase of the star, as defined by the isochrone model + metallicity: metallicity of the star + filter columns: magnitude of the star in each filter defined by the isochrone model + + companions: astropy.table.Table (only if multiplicity is used in the IMF object) + Table containing the properties of the companion stars. The columns include: + system_idx: index of the stellar system this companion belongs to, which can be used to match to the star_systems table + mass: initial mass of the companion star + Teff: effective temperature of the companion star + L: luminosity of the companion star in L_sun + logg: surface gravity of the companion star in cgs + isWR: boolean for whether the companion star is a Wolf-Rayet star + mass_current: current mass of the companion star + phase: evolutionary phase of the companion star, as defined by the isochrone model + metallicity: metallicity of the companion star + filter columns: magnitude of the companion star in each filter defined by the isochrone model + If multiplicity properties are defined in the IMF object, additional columns for those properties (e.g., log_a, e, i, Omega, omega) are included. """ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, seed=None, keep_low_mass_stars=False): @@ -146,13 +188,15 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, # Provide a user warning is random seed is set if seed is not None and verbose: print('WARNING: random seed set to %i' % seed) + imf.rng = self.rng - t1 = time.time() - ##### + ##### # Sample the IMF to build up our cluster mass. ##### - mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass, - seed=seed) + # start0 = time.time() + mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass) + # end0 = time.time() + # print('IMF sampling took {0:f} s.'.format(end0 - start0)) # Figure out the filters we will make. try: @@ -162,11 +206,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, self.cluster_mass = cluster_mass #FIXME? # Check if using an external evolution model (i.e. COSMIC) - if hasattr(iso, 'external_evol'): - if iso.external_evol == True: - self.external_evol = True - else: - self.external_evol = False + self.external_evol = getattr(iso, 'external_evol', False) ##### # Make isochrone interpolators @@ -175,8 +215,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, interp_keys = ['Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase'] + self.filt_names self.iso_interps = {} for ikey in interp_keys: - self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey], - kind='linear', bounds_error=False, fill_value=np.nan) + self.iso_interps[ikey] = Interpolator(self.iso.points['mass'], self.iso.points[ikey]) else: from scipy.interpolate import LinearNDInterpolator self.iso.points.sort(['Teff', 'logg', 'metallicity']) @@ -190,75 +229,52 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, ##### # Make a table to contain all the information about each stellar system. ##### - star_systems = self._make_star_systems_table(mass, isMulti, sysMass) - - # Trim out bad systems; specifically, stars with masses outside those provided - # by the model isochrone (except for compact objects). - # Assumes external evolution software (i.e. COSMIC) will handle systems that fall outside of range if self.external_evol == False: + # start1 = time.time() + star_systems = self._make_star_systems_table(mass, isMulti, sysMass) + # end1 = time.time() + # print('Star systems table took {0:f} s.'.format(end1 - start1)) + + # Trim out bad systems; specifically, stars with masses outside those provided + # by the model isochrone (except for compact objects). + # Assumes external evolution software (i.e. COSMIC) will handle systems that fall outside of range star_systems, compMass = self._remove_bad_systems(star_systems, compMass, keep_low_mass_stars) + else: + # Makes initial table to be evolved externally + star_systems = self._make_star_systems_table_initial(mass, isMulti, sysMass) - ##### + ##### # Make a table to contain all the information about companions. ##### if self.imf.make_multiples: - companions = self._make_companions_table(star_systems, compMass) + # start3 = time.time() + if self.external_evol == False: + star_systems, companions = self._make_companions_table(star_systems, compMass) + else: + # Makes initial table to be evolved externally + star_systems, companions = self._make_companions_table_initial(star_systems, compMass) + # end3 = time.time() + # print('Companion table new took {0:f} s.'.format(end3 - start3)) + + ##### + # Do external evolution if chosen and assign photometry + ##### # Assigns atmospheres based on grid for external evolution software # Must be done here instead of in Isochrone() since the systems are evolved after generation above if self.external_evol: star_systems, companions = iso.evo_model.evolve(star_systems, companions, iso.logAge, iso.metallicity) - - for filt in self.filt_names: - filt_name = filt.split('_') - filt_val = get_filter_info(get_obs_str(filt), rebin=False, vega=vega) - - # Rescale magnitudes to correct radius - # Since original grid was done assuming 1 Rsun - star_systems[filt] = self.iso_interps[filt](star_systems['Teff'], star_systems['logg'], iso.metallicity) - flux_val = filt_val.flux0*(10**(-(star_systems[filt] - filt_val.mag0)/2.5)) - R_vals = np.sqrt((star_systems['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(star_systems['Teff']*units.K)**4)).to('pc^2')).value - flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) - m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 - star_systems[filt] = m_rescaled - - companions[filt] = self.iso_interps[filt](companions['Teff'], companions['logg'], iso.metallicity) - flux_val = filt_val.flux0*(10**(-(companions[filt] - filt_val.mag0)/2.5)) - R_vals = np.sqrt((companions['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(companions['Teff']*units.K)**4)).to('pc^2')).value - flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) - m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 - companions[filt] = m_rescaled - - # Add companions masses to primaries - N_comp_max = np.max(star_systems['N_companions']) - comp_index = np.zeros((len(star_systems), N_comp_max), dtype=int) - kk = 0 - for ii in range(len(star_systems)): - for cc in range(star_systems['N_companions'][ii]): - comp_index[ii][cc] = kk - kk += 1 - - # Find all the systems with at least one companion... add the flux - # of that companion to the primary. Repeat for 2 companions, - # 3 companions, etc. - for cc in range(1, N_comp_max+1): - # All systems with at least cc companions. - idx = np.where(star_systems['N_companions'] >= cc)[0] - - # Get the location in the companions array for each system and - # the cc'th companion. - cdx = comp_index[idx, cc-1] - star_systems = self._calc_system_mag(star_systems, companions, idx, cdx, filt) - - #star_systems, companions = self._remove_bad_systems_and_companions(star_systems, companions) - + + star_systems, companions = self._external_evol_add_photometry(star_systems, companions, iso) + self.companions = companions + + if self.imf.make_multiples and not hasattr(self, 'companions'): + self.companions = companions + ##### # Save our arrays to the object ##### self.star_systems = star_systems - - if self.imf.make_multiples: - self.companions = companions return @@ -267,78 +283,74 @@ def set_filter_names(self): Set filter column names """ filt_names = [] - + for col_name in self.iso.points.colnames: if 'm_' in col_name: filt_names.append(col_name) return filt_names - + def _make_star_systems_table(self, mass, isMulti, sysMass): """ Make a star_systems table and get synthetic photometry for each primary star. """ - star_systems = Table([mass, isMulti, sysMass], - names=['mass', 'isMultiple', 'systemMass']) + star_systems = self._make_star_systems_table_initial(mass, isMulti, sysMass) N_systems = len(star_systems) - # Add columns for the Teff, L, logg, isWR, mass_current, phase for the primary stars. - star_systems.add_column( Column(np.zeros(N_systems, dtype=float), name='Teff') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='L') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='logg') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='isWR') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='mass_current') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='phase') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='metallicity') ) + # Use our pre-built interpolators to fetch values from the isochrone for each star. + star_systems['Teff'] = self.iso_interps['Teff'](star_systems['mass']) + star_systems['L'] = self.iso_interps['L'](star_systems['mass']) + star_systems['logg'] = self.iso_interps['logg'](star_systems['mass']) + star_systems['isWR'] = ~(self.iso_interps['isWR'](star_systems['mass']) < 0.5) #round to 0 or 1 for speed + star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass']) + star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass'])) + star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity + + # For a very small fraction of stars, the star phase falls on integers in-between + # the ones we have definition for, as a result of the interpolation. For these + # stars, round phase down to nearest defined phase (e.g., if phase is 71, + # then round it down to 5, rather than up to 101). + # Note: this only becomes relevant when the cluster is > 10**6 M-sun, this + # effect is so small + # Convert nan_to_num to avoid errors on greater than, less than comparisons - # Add the filter columns to the table. They are empty so far. - # Keep track of the filter names in : filt_names - for filt in self.filt_names: - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name=filt) ) + # Define brown dwarf mass range + bd_mask = (star_systems['mass'] >= 0.01) & (star_systems['mass'] <= 0.08) + # hard code BDs as 90 and invariant masses + star_systems['phase'][bd_mask] = 90 + star_systems['mass_current'][bd_mask] = star_systems['mass'][bd_mask] - # Use our pre-built interpolators to fetch values from the isochrone for each star. - if self.external_evol == False: - star_systems['Teff'] = self.iso_interps['Teff'](star_systems['mass']) - star_systems['L'] = self.iso_interps['L'](star_systems['mass']) - star_systems['logg'] = self.iso_interps['logg'](star_systems['mass']) - star_systems['isWR'] = np.round(self.iso_interps['isWR'](star_systems['mass'])) - star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass']) - star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass'])) - star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity - - # For a very small fraction of stars, the star phase falls on integers in-between - # the ones we have definition for, as a result of the interpolation. For these - # stars, round phase down to nearest defined phase (e.g., if phase is 71, - # then round it down to 5, rather than up to 101). - # Note: this only becomes relevant when the cluster is > 10**6 M-sun, this - # effect is so small - # Convert nan_to_num to avoid errors on greater than, less than comparisons - star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) - # Print warning, if desired - verbose=False - if verbose: - for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) - star_systems['phase'][bad] = 5 + # Identify bad phases (non-brown-dwarfs only) + star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) + bad = np.where( + (star_systems_phase_non_nan > 5) & + (star_systems_phase_non_nan < 101) & + (star_systems_phase_non_nan != 9) & + (star_systems_phase_non_nan != 90) & + (star_systems_phase_non_nan != -99) + ) + # Print warning, if desired + verbose=False + if verbose: + for ii in range(len(bad[0])): + print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) + star_systems['phase'][bad] = 5 - if self.external_evol == False: - for filt in self.filt_names: - star_systems[filt] = self.iso_interps[filt](star_systems['mass']) - + for filt in self.filt_names: + star_systems[filt] = self.iso_interps[filt](star_systems['mass']) ##### # Make Remnants # Note: Some models already have WDs in them. If they do, then they shouldn't # be handled by this code here (because their Teff > 0). - # + # # Remnants have flux = 0 in all bands if they are generated here. - ##### - if self.ifmr != None and self.external_evol == False: - # Identify compact objects as those with Teff = 0 or with phase > 100. + ##### + if self.ifmr != None: + # Identify compact objects as those with Teff = 0 or with phase > 100 or BDs highest_mass_iso = self.iso.points['mass'].max() idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0] - + # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args: @@ -349,7 +361,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) - good = np.where(r_id_tmp > 0) + good = r_id_tmp > 0 idx_rem_good = idx_rem[good] star_systems['mass_current'][idx_rem_good] = r_mass_tmp[good] @@ -360,157 +372,233 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) return star_systems - - def _make_companions_table(self, star_systems, compMass): + def _make_star_systems_table_initial(self, mass, isMulti, sysMass): + """ + Make intial star_systems table and add columns to be filled in. + """ + star_systems = Table([mass, isMulti, sysMass], + names=['mass', 'isMultiple', 'systemMass']) N_systems = len(star_systems) - - ##### - # MULTIPLICITY - # Make a second table containing all the companion-star masses. - # This table will be much longer... here are the arrays: - # sysIndex - the index of the system this star belongs too - # mass - the mass of this individual star. - N_companions = np.array([len(star_masses) for star_masses in compMass]) - star_systems.add_column( Column(N_companions, name='N_companions') ) - - N_comp_tot = N_companions.sum() - system_index = np.repeat(np.arange(N_systems), N_companions) - companions = Table([system_index], names=['system_idx']) + # Add columns for the Teff, L, logg, isWR, mass_current, phase, and filters. + for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: + star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=key)) + star_systems.add_column(Column(np.zeros(N_systems, dtype=bool), name='isWR')) # for models with no WR designation, this remains 0 + star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity - # Add columns for the Teff, L, logg, isWR mass_current, phase, and filters for the companion stars. - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='mass') ) - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='Teff') ) - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='L') ) - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='logg') ) - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='isWR') ) - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='mass_current') ) - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='phase') ) - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='metallicity') ) + # Add the filter columns to the table. They are empty so far. + # Keep track of the filter names in : filt_names for filt in self.filt_names: - companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name=filt) ) - - if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='log_a') ) - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='e') ) - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='i', description = 'degrees') ) - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='Omega') ) - companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='omega') ) - - for ii in range(len(companions)): - companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) - - companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) - companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot)) + star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=filt)) + + return star_systems - # Make an array that maps system index (ii), companion index (cc) to - # the place in the 1D companions array. - N_comp_max = N_companions.max() + def _make_companions_table(self, star_systems, compMass): + """Make companions table for resolved clusters with multiplicity. + + Parameters + ---------- + star_systems : astropy.table.Table + Table containing the properties of the primary stars. + compMass : numpy.ma.MaskedArray + Masked array containing the masses of the companions. + + Returns + ------- + star_systems : astropy.table.Table - comp_index = np.zeros((N_systems, N_comp_max), dtype=int) - kk = 0 - for ii in range(N_systems): - for cc in range(N_companions[ii]): - comp_index[ii][cc] = kk - kk += 1 - - # Find all the systems with at least one companion... add the flux - # of that companion to the primary. Repeat for 2 companions, - # 3 companions, etc. - for cc in range(1, N_comp_max+1): - # All systems with at least cc companions. - idx = np.where(N_companions >= cc)[0] - - # Get the location in the companions array for each system and - # the cc'th companion. - cdx = comp_index[idx, cc-1] - - companions['mass'][cdx] = [compMass[ii][cc-1] for ii in idx] - comp_mass = companions['mass'][cdx] - - if len(idx) > 0 and self.external_evol == False: - companions['Teff'][cdx] = self.iso_interps['Teff'](comp_mass) - companions['L'][cdx] = self.iso_interps['L'](comp_mass) - companions['logg'][cdx] = self.iso_interps['logg'](comp_mass) - companions['isWR'][cdx] = np.round(self.iso_interps['isWR'](comp_mass)) - companions['mass_current'] = self.iso_interps['mass_current'](companions['mass']) - companions['phase'] = np.round(self.iso_interps['phase'](companions['mass'])) - companions['metallicity'] = np.ones(N_comp_tot)*self.iso.metallicity - - # For a very small fraction of stars, the star phase falls on integers in-between - # the ones we have definition for, as a result of the interpolation. For these - # stars, round phase down to nearest defined phase (e.g., if phase is 71, - # then round it down to 5, rather than up to 101). - # Convert nan_to_num to avoid errors on greater than, less than comparisons - companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) - bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 101) & - (companions_phase_non_nan != 9) & - (companions_phase_non_nan != -99)) - # Print warning, if desired - verbose=False - if verbose: - for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(companions['phase'][bad[0][ii]])) - companions['phase'][bad] = 5 - - for filt in self.filt_names: - # Magnitude of companion - companions[filt][cdx] = self.iso_interps[filt](comp_mass) - star_systems = self._calc_system_mag(star_systems, companions, idx, cdx, filt) + companions : astropy.table.Table + """ + star_systems, companions = self._make_companions_table_initial(star_systems, compMass) + N_systems = len(star_systems) + N_companions = np.sum(~compMass.mask, axis=1) + N_comp_tot = np.sum(N_companions) + + for key in ['Teff', 'L', 'logg', 'mass_current']: + companions[key] = self.iso_interps[key](companions['mass']) + + companions['isWR'] = ~(self.iso_interps['isWR'](companions['mass']) < 0.5) #round to 0 or 1 for speed + companions['phase'] = np.round(self.iso_interps['phase'](companions['mass'])) + + bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] <= 0.08) + companions['phase'][bd_mask] = 90 + companions['mass_current'][bd_mask] = companions['mass'][bd_mask] + + # For a very small fraction of stars, the star phase falls on integers in-between + # the ones we have definition for, as a result of the interpolation. For these + # stars, round phase down to nearest defined phase (e.g., if phase is 71, + # then round it down to 5, rather than up to 101). + # Convert nan_to_num to avoid errors on greater than, less than comparisons + companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) + companions['phase'][ + (companions_phase_non_nan > 5) & + (companions_phase_non_nan < 101) & + (companions_phase_non_nan != 9) & + (companions_phase_non_nan != 90) & + (companions_phase_non_nan != -99) + ] = 5 + + # Update primary fluxes to include the flux of companions. + for filt in self.filt_names: + companions[filt] = self.iso_interps[filt](companions['mass']) + primary_flux = 10**(-star_systems[filt] / 2.5) + # Sum the flux of all companions in each system + companions_flux = np.bincount(companions['system_idx'], weights=10**(-companions[filt] / 2.5), minlength=N_systems) + combined_flux = np.nansum(np.vstack((primary_flux, companions_flux)), axis=0) + combined_flux[combined_flux == 0] = np.nan + star_systems[filt] = -2.5 * np.log10(combined_flux) ##### # Make Remnants with flux = 0 in all bands. - ##### - if self.ifmr != None and self.external_evol == False: + ##### + if self.ifmr: # Identify compact objects as those with Teff = 0 or with masses above the max iso mass highest_mass_iso = self.iso.points['mass'].max() - cdx_rem = np.where(np.isnan(companions['Teff']) & - (companions['mass'] > highest_mass_iso))[0] - - # Calculate remnant mass and ID for compact objects; update remnant_id and - # remnant_mass arrays accordingly + remnant_idx = np.where(np.isnan(companions['Teff']) & (companions['mass'] > highest_mass_iso))[0] + self.remnant_idx_new = remnant_idx + # Calculate remnant mass and ID for compact objects; update remnant_id and remnant_mass arrays accordingly if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args: - r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem], - metallicity_array=companions['metallicity'][cdx_rem]) + remnant_mass, remnant_code = self.ifmr.generate_death_mass(mass_array=companions['mass'][remnant_idx], metallicity_array=companions['metallicity'][remnant_idx]) else: - r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) + remnant_mass, remnant_code = self.ifmr.generate_death_mass(mass_array=companions['mass'][remnant_idx]) + + # Drop remnants where it is not relevant (e.g. not a compact object or outside mass range IFMR is defined for) + remnant_valid = remnant_code > 0 + remnant_valid_idx = remnant_idx[remnant_valid] + self.remnant_mass_new = remnant_mass + self.remnant_valid_idx_new = remnant_valid_idx + companions['mass_current'][remnant_valid_idx] = remnant_mass[remnant_valid] + companions['phase'][remnant_valid_idx] = remnant_code[remnant_valid] + # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. + for filt in self.filt_names: + companions[filt][remnant_valid_idx] = np.full(len(remnant_idx[remnant_valid]), np.nan) + + companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) + if self.verbose and sum(companions_teff_non_nan > 0) != N_comp_tot: + print(f'Found {N_comp_tot - sum(companions_teff_non_nan > 0):d} companions out of stellar mass range') + # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 + low_mass_idxs = (companions['mass'] 0) - cdx_rem_good = cdx_rem[good] + if len(companions['mass'][companions_teff_non_nan > 0])>0: + assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" - companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] - companions['phase'][cdx_rem_good] = r_id_tmp[good] - - # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. - for filt in self.filt_names: - companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) + return star_systems, companions + def _make_companions_table_initial(self, star_systems, compMass): + """Make initial companions table for resolved clusters with multiplicity. - # Notify if we have a lot of bad ones. - # Convert nan_to_num to avoid errors on greater than, less than comparisons - if self.external_evol == False: - companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) - idx = np.where(companions_teff_non_nan > 0)[0] - if len(idx) != N_comp_tot and self.verbose: - print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) + Parameters + ---------- + star_systems : astropy.table.Table + Table containing the properties of the primary stars. + compMass : numpy.ma.MaskedArray + Masked array containing the masses of the companions. + + Returns + ------- + star_systems : astropy.table.Table + Table containing the properties of the primary stars. + companions : astropy.table.Table + Table containing the properties of the companion stars. + """ + N_systems = len(star_systems) + N_companions = np.sum(~compMass.mask, axis=1) + N_comp_tot = np.sum(N_companions) + star_systems.add_column(Column(N_companions, name='N_companions')) + system_index = np.repeat(np.arange(N_systems), N_companions) + companions = Table([system_index], names=['system_idx']) + companions.add_column(np.zeros(N_comp_tot, dtype=float), name='mass') + + if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): + companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a')) + companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e')) + companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot) + ) + + companions['mass'] = compMass.compressed() + for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: + companions[key] = np.empty(N_comp_tot, dtype=float) + companions['isWR'] = np.zeros(N_comp_tot, dtype=bool) + companions['metallicity'] = np.ones(N_comp_tot) * self.iso.metallicity + for filt in self.filt_names: + companions[filt] = np.empty(N_comp_tot, dtype=float) + + return star_systems, companions - # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 - low_mass_idxs = (companions['mass']0: - assert companions['mass'][idx].min() > 0 + def _external_evol_add_photometry(self, star_systems, companions, iso): + """ + Function to calculate the photometry for systems that were + evolved using an external software (i.e. COSMIC) + + Parameters + ---------- + star_systems : astropy.table.Table + Table containing the properties of the primary objects. + companions : astropy.table.Table + Table containing the properties of the companions. + iso : Ioschrone object + + Returns + ------- + star_systems : astropy.table.Table + Table containing the properties of the primary objects. + companions : astropy.table.Table + Table containing the properties of the companion companions. + """ + c = constants + for filt in self.filt_names: + filt_name = filt.split('_') + filt_val = get_filter_info(get_obs_str(filt), rebin=False, vega=vega) + + # Rescale magnitudes to correct radius + # Since original grid was done assuming 1 Rsun + star_systems[filt] = self.iso_interps[filt](star_systems['Teff'], star_systems['logg'], iso.metallicity) + flux_val = filt_val.flux0*(10**(-(star_systems[filt] - filt_val.mag0)/2.5)) + R_vals = np.sqrt((star_systems['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(star_systems['Teff']*units.K)**4)).to('pc^2')).value + flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) + m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 + star_systems[filt] = m_rescaled + + companions[filt] = self.iso_interps[filt](companions['Teff'], companions['logg'], iso.metallicity) + flux_val = filt_val.flux0*(10**(-(companions[filt] - filt_val.mag0)/2.5)) + R_vals = np.sqrt((companions['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(companions['Teff']*units.K)**4)).to('pc^2')).value + flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) + m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 + companions[filt] = m_rescaled + + # Add companions masses to primaries + N_comp_max = np.max(star_systems['N_companions']) + comp_index = np.zeros((len(star_systems), N_comp_max), dtype=int) + kk = 0 + for ii in range(len(star_systems)): + for cc in range(star_systems['N_companions'][ii]): + comp_index[ii][cc] = kk + kk += 1 + + # Find all the systems with at least one companion... add the flux + # of that companion to the primary. Repeat for 2 companions, + # 3 companions, etc. + for cc in range(1, N_comp_max+1): + # All systems with at least cc companions. + idx = np.where(star_systems['N_companions'] >= cc)[0] + + # Get the location in the companions array for each system and + # the cc'th companion. + cdx = comp_index[idx, cc-1] + star_systems = self._calc_system_mag(star_systems, companions, idx, cdx, filt) + + return star_systems, companions + - return companions def _calc_system_mag(self, star_systems, companions, idx, cdx, filt): """ @@ -552,24 +640,25 @@ def _calc_system_mag(self, star_systems, companions, idx, cdx, filt): # If *both* objects are dark, then keep the magnitude # as np.nan. Otherwise, add fluxes together - good = np.where( (f1 != 0) | (f2 != 0) ) - bad = np.where( (f1 == 0) & (f2 == 0) ) + good = np.where( (f1 != 0) | (f2 != 0) )[0] + bad = np.where( (f1 == 0) & (f2 == 0) )[0] star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan return star_systems - + def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): """ Helper function to remove stars with masses outside the isochrone - mass range from the cluster. These stars are identified by having + mass range from the cluster. These stars are identified by having a Teff = 0, as set up by _make_star_systems_table_interp. - If self.ifmr == None, then both high and low-mass bad systems are - removed. If self.ifmr != None, then we will save the high mass systems + If self.ifmr == None, then both high and low-mass bad systems are + removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ + N_systems = len(star_systems) # Get rid of the bad ones @@ -578,27 +667,42 @@ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) if (self.ifmr == None) and (not keep_low_mass_stars): print('Remove low mass stars below grid and compact objects') - # Keep only those stars with Teff assigned. - idx = np.where(star_systems_teff_non_nan > 0)[0] + # Keep only those stars with Teff assigned and masses in grid + min_iso = np.min(self.iso.points['mass']) + max_iso = np.max(self.iso.points['mass']) + mass = star_systems['mass'] + on_grid = (mass >= min_iso) & (mass <= max_iso) & (star_systems_teff_non_nan > 0) + idx = on_grid + elif not keep_low_mass_stars: print('Remove low mass stars, keep compact objects') - # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0] + # Keep stars (with Teff) and any other compact objects (with phase info). + min_iso = np.min(self.iso.points['mass']) + max_iso = np.max(self.iso.points['mass']) + mass = star_systems['mass'] + on_grid = (mass >= min_iso) & (mass <= max_iso) & (star_systems_teff_non_nan > 0) + above_grid = (mass > max_iso) & ( + (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 101) + ) + idx = on_grid | above_grid + elif self.ifmr == None: print('Remove compact objects, keep low mass stars below grid') # Keep stars (with Teff) and objects below mass grid - idx = np.where( (star_systems_teff_non_nan > 0) | ((star_systems['mass'] 0) | (star_systems['mass'] < np.min(self.iso.points['mass'])) else: print('Keep low mass stars below grid and compact objects') # Keep all - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | - ((star_systems['mass'] 0) | \ + (star_systems_phase_non_nan >= 0) | \ + (star_systems['mass'] < np.min(self.iso.points['mass'])) - if len(idx) != N_systems and self.verbose: - print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) + n_out_of_range = N_systems - sum(idx) + if self.verbose and n_out_of_range > 0: + print( 'Found {0:d} stars out of mass range'.format(n_out_of_range)) if keep_low_mass_stars: - lm_idx = np.where(star_systems['mass'] 0) evol = evol[idx] @@ -1762,8 +1955,8 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, evol = evol[idx] if max_mass != None: idx = np.where(evol['mass'] <= max_mass) - evol = evol[idx] - + evol = evol[idx] + # Trim down the table by selecting every Nth point where # N = mass sampling factor. evol = evol[::mass_sampling] @@ -1798,18 +1991,18 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, # Get the atmosphere model now. Wavelength is in Angstroms # This is the time-intensive call... everything else is negligable. star = atm_func(temperature=T, gravity=gravity) - + # Trim wavelength range down to JHKL range (0.5 - 5.2 microns) star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) # Convert into flux observed at Earth (unreddened) star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1 - - # Save the final spectrum to our spec_list for later use. + + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) # Append all the meta data to the summary table. - + tab.meta['ATMFUNC'] = atm_func.__name__ tab.meta['EVOMODEL'] = type(evo_model).__name__ tab.meta['EVOMODELVERSION'] = evo_model.model_version_name @@ -1819,10 +2012,10 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, tab.meta['WAVEMAX'] = wave_range[1] self.points = tab - + t2 = time.time() print('Isochrone generation took {0:f} s.'.format(t2-t1)) - + return def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=None): @@ -1834,7 +2027,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= ---------- AKs: float Total extinction in AKs - + extinction_law: SPISEA extinction object Extinction law to be used on the spectra @@ -1844,13 +2037,13 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= dAKs_max: float or None If not none, defines the maximum |dAKs| a star can - have in gaussian distribution case + have in gaussian distribution case dist: string, 'uniform' or 'gaussian' Distribution to draw differential reddening from. If uniform, dAKs will cut off at Aks +/- dAKs. Otherwise, will draw from Gaussian of width AKs +/- dAks - + """ self.AKs = np.ones(len(self.spec_list)) # Apply reddening to each object in the spec list @@ -1861,25 +2054,25 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= # extinction law if dAKs != 0: if dist == 'gaussian': - AKs_act = np.random.normal(loc=AKs, scale=dAKs) + AKs_act = self.rng.normal(loc=AKs, scale=dAKs) # Apply dAKs_max if desired. Redo if diff > dAKs_max if dAKs_max != None: diff = abs(AKs_act - AKs) while diff > dAKs_max: print('While loop active') - AKs_act = np.random.normal(loc=AKs, scale=dAKs) + AKs_act = self.rng.normal(loc=AKs, scale=dAKs) diff = abs(AKs_act - AKs) elif dist == 'uniform': low = AKs - dAKs high = AKs + dAKs - AKs_act = np.random.uniform(low=low, high=high) + AKs_act = self.rng.uniform(low=low, high=high) else: print('dist {0} undefined'.format(dist)) return else: AKs_act = AKs - red = extinction_law.reddening(AKs_act).resample(star.wave) + red = extinction_law.reddening(AKs_act).resample(star.wave) star *= red # Update the spectrum in spec list @@ -1892,7 +2085,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= return def make_photometry(self, filters, rebin=True): - """ + """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the photometry. @@ -1902,11 +2095,11 @@ def make_photometry(self, filters, rebin=True): filters : dictionary A dictionary containing the filter name (for the output columns) and the filter specification string that can be processed by pysynphot. - + rebin: boolean - True to rebin filter function (only used if non-zero transmission points are + True to rebin filter function (only used if non-zero transmission points are larger than 1500 points) - + """ npoints = len(self.points) @@ -1923,35 +2116,78 @@ def make_photometry(self, filters, rebin=True): col_name = 'mag_' + filt_name mag_col = Column(np.zeros(npoints, dtype=float), name=col_name) self.points.add_column(mag_col) - + # Loop through each star in the isochrone and do the filter integration for ss in range(npoints): star = self.spec_list[ss] # These are already extincted, observed spectra. star_mag = mag_in_filter(star, filt) - + self.points[col_name][ss] = star_mag - - + + endTime = time.time() print( ' Time taken: {0:.2f} seconds'.format(endTime - ts)) return +def check_save_file(save_file_path, evo_model, atm_func, red_law, verbose=False): + """ + Check to see if save_file exists, as saved by the save_file + and save_file_legacy objects. If the filename exists, check the + meta-data as well. + + returns a boolean: True is file exists, false otherwise + """ + out_bool = False + + if not os.path.exists(save_file_path): + if verbose: print(f'Isochrone file {save_file_path} does not exist.') + return out_bool + + tmp = Table.read(save_file_path) + + # See if the meta-data matches: evo model, atm_func, redlaw + if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) & + (tmp.meta['ATMFUNC'] == atm_func.__name__) & + (tmp.meta['REDLAW'] == red_law.name) ): + out_bool = True + else: + # If out_bool is false, print out what doesn't match + if verbose: + print(f'Isochrone file {save_file_path} exists, but meta-data does not match.') + if tmp.meta['EVOMODEL'] != type(evo_model).__name__: + print(f' EVOMODEL: {tmp.meta["EVOMODEL"]} != {type(evo_model).__name__}') + if tmp.meta['ATMFUNC'] != atm_func.__name__: + print(f' ATMFUNC: {tmp.meta["ATMFUNC"]} != {atm_func.__name__}') + if tmp.meta['REDLAW'] != red_law.name: + print(f' REDLAW: {tmp.meta["REDLAW"]} != {red_law.name}') + + # Check model version if it was logged + if 'EVOMODELVERSION' in tmp.meta: + if tmp.meta['EVOMODELVERSION'] != evo_model.model_version_name: + out_bool=False + if verbose: + print(f'EVOMODELVERSION does not match: The recorded {tmp.meta["EVOMODELVERSION"]} does not matched the existing version {evo_model.model_version_name}') + + + return out_bool + + def get_filter_info(name, vega=vega, rebin=True): - """ + """ Define filter functions, setting ZP according to Vega spectrum. Input name is the SPISEA obs_string """ tmp = name.split(',') filterName = tmp[-1] - + if name.startswith('nirc2'): filt = filters.get_nirc2_filt(filterName) elif name.startswith('2mass'): filt = filters.get_2mass_filt(filterName) - + elif name.startswith('vista'): filt = filters.get_vista_filt(filterName) @@ -1966,10 +2202,10 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('jg'): filt = filters.get_Johnson_Glass_filt(filterName) - + elif name.startswith('nirc1'): filt = filters.get_nirc1_filt(filterName) - + elif name.startswith('ctio_osiris'): filt = filters.get_ctio_osiris_filt(filterName) @@ -1981,7 +2217,7 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('ukirt'): filt = filters.get_ukirt_filt(filterName) - + elif name.startswith('keck_osiris'): filt = filters.get_keck_osiris_filt(filterName) @@ -1989,25 +2225,53 @@ def get_filter_info(name, vega=vega, rebin=True): filt = filters.get_ztf_filt(filterName) elif name.startswith('gaia'): - version = tmp[1] + version = tmp[1] if len(tmp)==3 else 'edr3' filt = filters.get_gaia_filt(version, filterName) elif name.startswith('hawki'): filt = filters.get_hawki_filt(filterName) - + elif name.startswith('rubin'): filt = filters.get_rubin_filt(filterName) elif name.startswith('euclid'): filt = filters.get_euclid_filt(filterName) - + + elif name.startswith('nsfcam'): + filt = filters.get_nsfcam_filt(filterName) + + elif name.startswith('tess'): + filt = filters.get_tess_filt(filterName) + + elif name.startswith('washington'): + filt = filters.get_washington_filt(filterName) + + elif name.startswith('hipparcos'): + filt = filters.get_hipparcos_filt(filterName) + + elif name.startswith('tycho'): + filt = filters.get_tycho_filt(filterName) + + elif name.startswith('kepler'): + filt = filters.get_kepler_filt(filterName) + + elif name.startswith('ogle'): + filt = filters.get_ogle_filt(filterName) + + elif name.startswith('subaru'): + inst = tmp[1] + filt = filters.get_subaru_filt(inst, filterName) + + elif name.startswith('bessell'): + filt = filters.get_bessell_filt(filterName) + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: filt = ObsBandpass(name) except: raise Exception('Filter {0} not understood. Check spelling and make sure cdbs/mtab and cdbs/comp files are up to date'.format(name)) - + # Convert to ArraySpectralElement for resampling. filt = spectrum.ArraySpectralElement(filt.wave, filt.throughput, waveunits=filt.waveunits, @@ -2025,14 +2289,14 @@ def get_filter_info(name, vega=vega, rebin=True): # Otherwise, throw an error idx = np.where(filt.throughput > 0.001)[0] if (min(filt.wave[idx]) < min(vega.wave)) | (max(filt.wave[idx]) > max(vega.wave)): - raise ValueError('Vega spectrum doesnt cover filter wavelength range!') + raise ValueError('Vega spectrum doesnt cover filter wavelength range!') vega_obs = obs.Observation(vega, filt, binset=filt.wave, force='taper') #vega_flux = vega_obs.binflux.sum() diff = np.diff(vega_obs.binwave) diff = np.append(diff, diff[-1]) vega_flux = np.sum(vega_obs.binflux * diff) - + vega_mag = 0.03 filt.flux0 = vega_flux @@ -2043,7 +2307,7 @@ def get_filter_info(name, vega=vega, rebin=True): def get_filter_col_name(obs_str): """ - Get standard column name for synthetic photometry based on + Get standard column name for synthetic photometry based on the input string. The input string is expected to be an appropriate SPISEA obs_string """ @@ -2054,15 +2318,23 @@ def get_filter_col_name(obs_str): if len(tmp) == 3: # Catch Gaia filter cases. Otherwise, it is HST filter - if 'dr2_rev' in tmp: + if 'dr1' in tmp: + filt_name = 'gaiaDR1_{0}'.format(tmp[-1]) + elif 'dr2' in tmp: + filt_name = 'gaiaDR2old_{0}'.format(tmp[-1]) + elif 'dr2_rev' in tmp: filt_name = 'gaiaDR2_{0}'.format(tmp[-1]) + elif 'edr3' in tmp: + filt_name = 'gaiaEDR3_{0}'.format(tmp[-1]) elif 'roman' in tmp: filt_name = 'roman_{0}'.format(tmp[-1]) + elif tmp[0] == 'subaru': + filt_name = '_'.join(tmp) else: filt_name = 'hst_{0}'.format(tmp[-1]) else: filt_name = '{0}_{1}'.format(tmp[0], tmp[1]) - + return filt_name def get_obs_str(col): @@ -2072,84 +2344,29 @@ def get_obs_str(col): """ # Remove the trailing m_ name = col[2:] - - # Define dictionary for filters - filt_list = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', - 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w', - 'decam_y': 'decam,y', 'decam_i': 'decam,i', 'decam_z': 'decam,z', - 'decam_u':'decam,u', 'decam_g':'decam,g', 'decam_r':'decam,r', - 'vista_Y':'vista,Y', 'vista_Z':'vista,Z', 'vista_J': 'vista,J', - 'vista_H': 'vista,H', 'vista_Ks': 'vista,Ks', - 'ps1_z':'ps1,z', 'ps1_g':'ps1,g', 'ps1_r': 'ps1,r', - 'ps1_i': 'ps1,i', 'ps1_y':'ps1,y', - 'jwst_F090W': 'jwst,F090W', 'jwst_F164N': 'jwst,F164N', 'jwst_F212N': 'jwst,F212N', - 'jwst_F323N':'jwst,F323N', 'jwst_F466N': 'jwst,F466N', - 'jwst_F070W': 'jwst,F070W', - 'jwst_F115W': 'jwst,F115W', - 'jwst_F140M': 'jwst,F140M', - 'jwst_F150W': 'jwst,F150W', - 'jwst_F150W2': 'jwst,F150W2', - 'jwst_F162M': 'jwst,F162M', - 'jwst_F182M': 'jwst,F182M', - 'jwst_F187N': 'jwst,F187N', - 'jwst_F200W': 'jwst,F200W', - 'jwst_F210M': 'jwst,F210M', - 'jwst_F250M': 'jwst,F250M', - 'jwst_F277W': 'jwst,F277W', - 'jwst_F300M': 'jwst,F300M', - 'jwst_F322W2': 'jwst,F322W2', - 'jwst_F335M': 'jwst,F335M', - 'jwst_F356W': 'jwst,F356W', - 'jwst_F360M': 'jwst,F360M', - 'jwst_F405N': 'jwst,F405N', - 'jwst_F410M': 'jwst,F410M', - 'jwst_F430M': 'jwst,F430M', - 'jwst_F444W': 'jwst,F444W', - 'jwst_F440W': 'jwst,F440W', - 'jwst_F460M': 'jwst,F460M', - 'jwst_F470N': 'jwst,F470N', - 'jwst_F480M': 'jwst,F480M', - 'nirc2_J': 'nirc2,J', 'nirc2_H': 'nirc2,H', 'nirc2_Kp': 'nirc2,Kp', 'nirc2_K': 'nirc2,K', - 'nirc2_Lp': 'nirc2,Lp', 'nirc2_Ms': 'nirc2,Ms', 'nirc2_Hcont': 'nirc2,Hcont', - 'nirc2_FeII': 'nirc2,FeII', 'nirc2_Brgamma': 'nirc2,Brgamma', - '2mass_J': '2mass,J', '2mass_H': '2mass,H', '2mass_Ks': '2mass,Ks', - 'ubv_U':'ubv,U', 'ubv_B':'ubv,B', 'ubv_V':'ubv,V', 'ubv_R':'ubv,R', - 'ubv_I':'ubv,I', - 'jg_J': 'jg,J', 'jg_H': 'jg,H', 'jg_K': 'jg,K', - 'nirc1_K':'nirc1,K', 'nirc1_H':'nirc1,H', - 'naco_J':'naco,J', 'naco_H':'naco,H', 'naco_Ks':'naco,Ks', - 'naco_IB_2.00': 'naco,IB_2.00', 'naco_IB_2.03':'naco,IB_2.03', 'naco_IB_2.06':'naco,IB_2.06', - 'naco_IB_2.24':'naco,IB_2.24', 'naco_IB_2.27':'naco,IB_2.27', - 'naco_IB_2.30':'naco,IB_2.30', 'naco_IB_2.33':'naco,IB_2.33', - 'naco_IB_2.36':'naco,IB_2.36', - 'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', - 'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K', - 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', - 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', - 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', - 'hawki_J': 'hawki,J', - 'hawki_H': 'hawki,H', - 'hawki_Ks': 'hawki,Ks', - 'roman_f062': 'roman,wfi,f062', - 'roman_f087': 'roman,wfi,f087', - 'roman_f106': 'roman,wfi,f106', - 'roman_f129': 'roman,wfi,f129', - 'roman_f158': 'roman,wfi,f158', - 'roman_f146': 'roman,wfi,f146', - 'roman_f213': 'roman,wfi,f213', - 'roman_f184': 'roman,wfi,f184', - 'rubin_g':'rubin,g', - 'rubin_i':'rubin,i', - 'rubin_r':'rubin,r', - 'rubin_u':'rubin,u', - 'rubin_z':'rubin,z', - 'rubin_y':'rubin,y', - 'euclid_Y':'euclid,Y', - 'euclid_J':'euclid,J', - 'euclid_H':'euclid,H'} - - obs_str = filt_list[name] - + + # This mostly follows a standard form, but we'll account for some special cases + if name[:4]=='hst_': + hst_filts = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', + 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w'} + obs_str = hst_filts[name] + elif name[:6]=='roman_': + tmp = name.split('_') + obs_str = 'roman,wfi,'+tmp[1] + elif name[:4]=='gaia': + gaia_filts = {'gaiaDR1_G': 'gaia,dr1,G', 'gaiaDR1_Gbp':'gaia,dr1,Gbp', 'gaiaDR1_Grp':'gaia,dr1,Grp', + 'gaiaDR2old_G': 'gaia,dr2,G', 'gaiaDR2old_Gbp':'gaia,dr2,Gbp', 'gaiaDR2old_Grp':'gaia,dr2,Grp', + 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', + 'gaiaEDR3_G': 'gaia,edr3,G', 'gaiaEDR3_Gbp':'gaia,edr3,Gbp', 'gaiaEDR3_Grp':'gaia,edr3,Grp'} + obs_str = gaia_filts[name] + elif name[:8]=='naco_IB_': + obs_str = name.replace('_', ',', 1) + elif name[:12]=='ctio_osiris_': + tmp = name.split('_') + obs_str = tmp[0]+'_'+tmp[1]+','+tmp[2] + else: + obs_str = ','.join(name.split('_')) + return obs_str def rebin_spec(wave, specin, wavnew): @@ -2162,7 +2379,7 @@ def rebin_spec(wave, specin, wavnew): f = np.ones(len(wave)) filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') obs_f = obs.Observation(spec, filt, binset=wavnew, force='taper') - + return obs_f.binflux def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, @@ -2173,7 +2390,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, 'wfc3,ir,f153m']): """ Wrapper routine to generate a grid of isochrones of different ages, - extinctions, and distances. + extinctions, and distances. Parameters: ---------- @@ -2185,7 +2402,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, dist_arr: array Array of distances to loop over (pc) - + evo_models: SPISEA evolution object Which evolution models to use @@ -2202,7 +2419,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, Mass sampling of isochrone, relative to original mass sampling filters: dictionary - Which filters to do the synthetic photometry on + Which filters to do the synthetic photometry on """ print( '**************************************') print( 'Start generating isochrones') @@ -2247,7 +2464,7 @@ def mag_in_filter(star, filt): diff = np.diff(star_in_filter.binwave) diff = np.append(diff, diff[-1]) star_flux = np.sum(star_in_filter.binflux * diff) - + star_mag = -2.5 * math.log10(star_flux / filt.flux0) + filt.mag0 return star_mag @@ -2270,10 +2487,10 @@ def match_model_masses(isoMasses, starMasses): idx = np.where(dm_frac > 0.1)[0] indices[idx] = -1 - + return indices - + def get_evo_model_by_string(evo_model_string): return getattr(evolution, evo_model_string) @@ -2284,13 +2501,13 @@ def calc_ab_vega_filter_conversion(filt_str): AB and Vega magnitudes for a given filter: m_AB - m_vega - Note: this conversion is just the vega magnitude in + Note: this conversion is just the vega magnitude in AB system - Parameters: - ----------- + Parameters + ---------- filt_str: string - Filter identification string + SPISEA filter identification string (see Photometric Filters doc page) """ # Get filter info filt = get_filter_info(filt_str) @@ -2304,14 +2521,14 @@ def calc_ab_vega_filter_conversion(filt_str): filt_wave = filt.wave filt_mu = c / filt_wave s_filt = filt.throughput - + # Interpolate the filter function, determine what the # filter function is at the exact sampling of the # vega spectrum (in freq space) filt_interp = scipy.interpolate.interp1d(filt_mu, s_filt, kind='linear', bounds_error=False, fill_value=0) s_interp = filt_interp(vega_mu) - + # Now for the m_ab calculation mu_diff = np.diff(vega_mu) numerator = np.sum(vega_flux_mu[:-1] * s_interp[:-1] * mu_diff) @@ -2329,10 +2546,10 @@ def calc_ab_vega_filter_conversion(filt_str): # fill_value=0) #s_interp = filt_interp(vega.wave) - # Calculate the numerator + # Calculate the numerator #diff = np.diff(vega.wave) #numerator2 = np.sum((vega.wave[:-1]**2. / c) * vega.flux[:-1] * s_interp[:-1] * diff) - + # Now we need to intergrate the filter response for the denominator #denominator2 = np.sum(s_interp[:-1] * diff) @@ -2341,3 +2558,42 @@ def calc_ab_vega_filter_conversion(filt_str): return vega_mag_ab +def calc_st_vega_filter_conversion(filt_str): + """ + Function to calculate the conversion between + ST and Vega magnitudes for a given filter: + m_ST - m_vega + + Note: this conversion is just the vega magnitude in + ST system + + Parameters + ---------- + filt_str: string + SPISEA filter identification string (see Photometric Filters doc page) + """ + # Get filter info + filt = get_filter_info(filt_str) + + # Interpolate the filter function to be the exact same sampling as the + # vega spectrum + c = 2.997*10**18 # A / s + filt_interp = scipy.interpolate.interp1d(filt.wave, filt.throughput, kind='linear', bounds_error=False, + fill_value=0) + s_interp = filt_interp(vega.wave) + + # Calculate the numerator + diff = np.diff(vega.wave) + numerator = np.sum(vega.flux[:-1] * s_interp[:-1] * diff) + + # Now we need to intergrate the filter response for the denominator + denominator = np.sum(s_interp[:-1] * diff) + # Fλ must be in erg cm–2 sec–1 Å–1 + + # Calculate vega AB magnitude. This is the conversion + vega_mag_st = -2.5 * np.log10(numerator / denominator) - 21.1 + + print('For {0}, m_st - m_vega = {1}'.format(filt_str, vega_mag_st)) + + return vega_mag_st + diff --git a/spisea/tests/coveragerc b/spisea/tests/coveragerc deleted file mode 100755 index bec7c291..00000000 --- a/spisea/tests/coveragerc +++ /dev/null @@ -1,31 +0,0 @@ -[run] -source = {packagename} -omit = - {packagename}/_astropy_init* - {packagename}/conftest* - {packagename}/cython_version* - {packagename}/setup_package* - {packagename}/*/setup_package* - {packagename}/*/*/setup_package* - {packagename}/tests/* - {packagename}/*/tests/* - {packagename}/*/*/tests/* - {packagename}/version* - -[report] -exclude_lines = - # Have to re-enable the standard pragma - pragma: no cover - - # Don't complain about packages we have installed - except ImportError - - # Don't complain if tests don't hit assertions - raise AssertionError - raise NotImplementedError - - # Don't complain about script hooks - def main\(.*\): - - # Ignore branches that don't pertain to this version of Python - pragma: py{ignore_python_version} \ No newline at end of file diff --git a/spisea/tests/setup_package.py b/spisea/tests/setup_package.py deleted file mode 100755 index f2fd9ed4..00000000 --- a/spisea/tests/setup_package.py +++ /dev/null @@ -1,3 +0,0 @@ -def get_package_data(): - return { - _ASTROPY_PACKAGE_NAME_ + '.tests': ['coveragerc']} diff --git a/spisea/tests/test_atmospheres_grid_timing.py b/spisea/tests/test_atmospheres_grid_timing.py new file mode 100644 index 00000000..afbecd66 --- /dev/null +++ b/spisea/tests/test_atmospheres_grid_timing.py @@ -0,0 +1,62 @@ +""" +Timing benchmark for Kurucz ``k93models`` grid extraction (stsynphot / pysynphot). + +Kept in a separate module (no ``spisea`` imports) so the pysynphot-only branch can +run when ``stsynphot`` is not imported elsewhere. +""" +import importlib.util +import os +import time + +import numpy as np +import pytest + + +def test_pysynphot_vs_stsynphot_timing(): + """ + Time Kurucz ``k93models`` spectrum extraction via stsynphot or pysynphot. + """ + temperature = 20000 + metallicity = 0.0 + gravity = 4.0 + + has_stsyn = importlib.util.find_spec("stsynphot") is not None + has_pysyn = importlib.util.find_spec("pysynphot") is not None + + if has_stsyn: + if not os.environ.get("PYSYN_CDBS"): + pytest.skip("PYSYN_CDBS not set; grid_to_spec needs CDBS tree") + + from stsynphot.catalog import grid_to_spec + + t0 = time.perf_counter() + try: + sp = grid_to_spec("k93models", temperature, metallicity, gravity) + w = sp.waveset + _ = sp(w) + except Exception as exc: + pytest.skip(f"stsynphot grid_to_spec failed: {exc}") + elapsed = time.perf_counter() - t0 + + assert elapsed >= 0 + assert np.isfinite(elapsed) + assert w.size > 0 + + elif has_pysyn: + import pysynphot + + t0 = time.perf_counter() + try: + sp = pysynphot.Icat("k93models", temperature, metallicity, gravity) + wave = sp.GetWaveSet() + _ = sp.sample(wave) + except Exception as exc: + pytest.skip(f"pysynphot Icat failed: {exc}") + elapsed = time.perf_counter() - t0 + + assert elapsed >= 0 + assert np.isfinite(elapsed) + assert np.asarray(wave).size > 0 + + else: + pytest.skip("Neither stsynphot nor pysynphot is installed") diff --git a/spisea/tests/test_data/companions.fits b/spisea/tests/test_data/companions.fits new file mode 100644 index 00000000..c964bcf4 Binary files /dev/null and b/spisea/tests/test_data/companions.fits differ diff --git a/spisea/tests/test_data/star_systems.fits b/spisea/tests/test_data/star_systems.fits new file mode 100644 index 00000000..2c5f355a Binary files /dev/null and b/spisea/tests/test_data/star_systems.fits differ diff --git a/spisea/tests/test_exceptions.py b/spisea/tests/test_exceptions.py index 4996bb3c..b20a132d 100644 --- a/spisea/tests/test_exceptions.py +++ b/spisea/tests/test_exceptions.py @@ -32,12 +32,6 @@ def test_grid_number_exception(): # Case 3: installed model grid is higher than required grid (no error) required_grid = installed_grid - 1.0 - evolution.check_evo_grid_number(required_grid, models_dir) - - return - - - - + evolution.check_evo_grid_number(required_grid, models_dir) - + return \ No newline at end of file diff --git a/spisea/imf/tests/test_imf.py b/spisea/tests/test_imf.py similarity index 87% rename from spisea/imf/tests/test_imf.py rename to spisea/tests/test_imf.py index 571f6edc..943dc0a1 100755 --- a/spisea/imf/tests/test_imf.py +++ b/spisea/tests/test_imf.py @@ -1,17 +1,16 @@ import numpy as np import time import pdb +import cProfile +from spisea.imf import imf, multiplicity def test_generate_cluster(): - from .. import imf - from .. import multiplicity - # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Make IMF object; we'll use a broken power law with the parameters from Kroupa+01 - massLimits = np.array([0.08, 0.5, 1, 120]) # Define boundaries of each mass segement - powers = np.array([-1.3, -2.3, -2.3]) # Power law slope associated with each mass segment + # Make IMF object; we'll use a broken power law with the parameters from Salpeter_Kirkpatrick+24 (updated with brown dwarf addition + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) # Define total cluster mass @@ -20,8 +19,8 @@ def test_generate_cluster(): mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 + # range of the requested mass (2%). + assert np.abs(M_cl - sysMass.sum()) < M_cl*0.02 # Check that enough companions were generated. # Should be greater than 25% of the stars with companions. @@ -32,8 +31,6 @@ def test_generate_cluster(): return def test_prim_power(): - from .. import imf - #mass_limits = np.array([0.1, 1.0, 100.0]) #powers = np.array([-2.0, -1.8]) mass_limits = np.array([1.0, 100.0]) @@ -60,9 +57,6 @@ def test_prim_power(): return def test_xi(): - from .. import imf - - import cProfile, pstats, io #from pstats import SortKey pr = cProfile.Profile() @@ -72,14 +66,14 @@ def test_xi(): ########## # - # Test validity of returned values. - # + # Test validity of returned values. + # ########## N_size = 10 m = np.linspace(0.2, 20, N_size) val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704, 0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142]) - + val_test = np.zeros(len(m), dtype=float) for ii in range(len(m)): val_test[ii] = imf_tmp.xi(m[ii]) @@ -90,26 +84,26 @@ def test_xi(): ########## # # Performance testing - # + # ########## t1 = time.time() # pr.enable() - + # Run a time test N_size = int(1e4) m = np.random.uniform(1.1, 99.0, size=N_size) foo1 = imf_tmp.xi(m) - + # pr.disable() t2 = time.time() print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - + # s = io.StringIO() # sortby = SortKey.CUMULATIVE # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) # ps.print_stats() - # print(s.getvalue()) + # print(s.getvalue()) return @@ -118,9 +112,6 @@ def test_xi2(): Test that xi() produces the correct probability for a given slope. """ - from .. import imf - - import cProfile, pstats, io #from pstats import SortKey pr = cProfile.Profile() @@ -169,17 +160,13 @@ def test_xi2(): plt.clf() plt.loglog(masses[sdx], pdf_sorted, 'k.') plt.axis('equal') - # pdb.set_trace() return - -def test_mxi(): - from .. import imf - import cProfile, pstats, io +def test_mxi(): #from pstats import SortKey pr = cProfile.Profile() @@ -189,8 +176,8 @@ def test_mxi(): ########## # - # Test validity of returned values. - # + # Test validity of returned values. + # ########## N_size = 10 m = np.linspace(0.2, 20, N_size) @@ -207,11 +194,11 @@ def test_mxi(): ########## # # Performance testing - # + # ########## t1 = time.time() # pr.enable() - + # Run a time test N_size = int(1e4) m = np.random.uniform(1.1, 99.0, size=N_size) @@ -222,29 +209,26 @@ def test_mxi(): t2 = time.time() print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - + # s = io.StringIO() # sortby = SortKey.CUMULATIVE # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) # ps.print_stats() - # print(s.getvalue()) + # print(s.getvalue()) return def test_theta_closed(): - from .. import imf - - import cProfile, pstats, io #from pstats import SortKey - + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) powers = np.array([-0.3, -1.5, -2.3]) imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) ########## # - # Test validity of returned values. - # + # Test validity of returned values. + # ########## N_size = 10 m = np.linspace(0.2, 20, N_size) @@ -265,22 +249,22 @@ def test_theta_closed(): np.testing.assert_equal(val_test, val_good) - + ########## # # Speed tests and performance profiling. - # + # ########## N_size = 10000 m = np.linspace(1.1, 99, N_size) - + tmp = np.zeros((len(m), len(powers)), dtype=float) t1 = time.time() # pr = cProfile.Profile() # pr.enable() - + for ii in range(len(m)): tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) @@ -288,7 +272,7 @@ def test_theta_closed(): t2 = time.time() print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - + # s = io.StringIO() # sortby = SortKey.CUMULATIVE # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) @@ -297,4 +281,4 @@ def test_theta_closed(): return - + diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 78d8e9e1..831a02a0 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -1,21 +1,19 @@ # Test functions for the different stellar evolution and atmosphere models -from spisea import evolution +from spisea import evolution, atmospheres, synthetic import numpy as np import pdb def test_evo_model_grid_num(): """ - Make sure evolution models have both evo_grid_num + Make sure evolution models have both evo_grid_num and evo_grid_min (e.g., make sure these functions are working). Try it on one evolution model here; we'll test on all evo models in another function. """ - from spisea import evolution - # Make MIST evolution model, check evo grid variables evo = evolution.MISTv1() assert isinstance(evo.evo_grid_min, float) - + return def test_evolution_models(): @@ -26,21 +24,25 @@ def test_evolution_models(): age_young_arr = [6.7, 7.9] age_all_arr = [6.7, 8.0, 9.7] age_all_MIST_arr = [5.2, 6.7, 9.7, 10.13] + bd_test = [6.0, 6.5, 7.4, 8.4, 10.0] # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] metal_solar = [0] + metal_Marley = [-0.5, 0.0, 0.5] # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), - evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa()] + evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), + evolution.Phillips2020(), evolution.Marley2021(), + evolution.MergedPhillipsBaraffePisaEkstromParsec()] + - # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_test] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley, metal_solar] assert len(evo_models) == len(age_vals) == len(metal_vals) @@ -68,12 +70,12 @@ def test_evolution_models(): raise Exception('EVO TEST FAILED: {0}, age = {1}, metal = {2}'.format(evo, kk, jj)) print('Done {0}'.format(evo)) - + return def test_synthpop_MIST_extension(): """ - Testing the synthpop MIST extension to consistently lower masses + Testing the synthpop MIST extension to consistently lower masses """ evo1_grid = evolution.MISTv1(version=1.2, synthpop_extension=False) evo2_grid = evolution.MISTv1(version=1.2, synthpop_extension=True) @@ -89,20 +91,106 @@ def test_synthpop_MIST_extension(): return +def test_COSMIC_init(): + """ + Test the COSMIC external evolution model constructor: default flags, + default BSEDict, and that user-supplied options are stored. + """ + # Default construction + evo = evolution.COSMIC() + assert evo.external_evol is True + assert evo.z_solar == 0.02 + assert evo.model_version_name == 'COSMIC' + assert evo.keep_disrupted_companions is True + assert evo.keep_COSMIC_tables is False + + # Default BSEDict should be a populated dictionary of BSE parameters + assert isinstance(evo.BSEDict, dict) + assert len(evo.BSEDict) > 0 + + # User-supplied options should be stored + custom_dict = {'windflag': 3, 'neta': 0.5} + evo2 = evolution.COSMIC(BSEDict=custom_dict, keep_disrupted_companions=False, + keep_COSMIC_tables=True) + assert evo2.BSEDict == custom_dict + assert evo2.keep_disrupted_companions is False + assert evo2.keep_COSMIC_tables is True + + return + +def test_COSMIC_calc_logg(): + """ + Test the COSMIC.calc_logg helper. For the Sun (M=1 Msun, R=1 Rsun) + the surface gravity should be logg ~ 4.438 (cgs). + """ + evo = evolution.COSMIC() + + # Scalar solar value + assert np.isclose(evo.calc_logg(1.0, 1.0), 4.438, atol=0.01) + + # Array input should be handled element-wise + masses = np.array([1.0, 2.0]) + radii = np.array([1.0, 2.0]) + logg = evo.calc_logg(masses, radii) + assert np.isclose(logg[0], 4.438, atol=0.01) + # logg scales as log10(M/R^2); doubling both M and R lowers logg by log10(2) + assert np.isclose(logg[0] - logg[1], np.log10(2.0), atol=0.01) + + return + +def test_COSMIC_get_kick_differential(): + """ + Test the COSMIC.get_kick_differential helper. The transformation is a + pure rotation (Rz(theta) * Rx(phi)) of the kick vector, so it must + preserve the vector magnitude. A zero kick must map to a zero kick. + """ + evo = evolution.COSMIC() + + # Zero kick in -> zero kick out + zeros = np.zeros(3) + phase = np.array([0.3, 1.1, 2.0]) + incl = np.array([0.5, 1.5, 2.5]) + kd_zero = evo.get_kick_differential(zeros, zeros, zeros, phase=phase, inclination=incl) + assert np.allclose(kd_zero.d_x.value, 0.0) + assert np.allclose(kd_zero.d_y.value, 0.0) + assert np.allclose(kd_zero.d_z.value, 0.0) + + # Magnitude is preserved under the rotation + vx = np.array([10.0, -5.0, 3.0]) + vy = np.array([2.0, 7.0, -1.0]) + vz = np.array([-4.0, 1.0, 8.0]) + kd = evo.get_kick_differential(vx, vy, vz, phase=phase, inclination=incl) + + mag_in = np.sqrt(vx**2 + vy**2 + vz**2) + mag_out = np.sqrt(kd.d_x.value**2 + kd.d_y.value**2 + kd.d_z.value**2) + np.testing.assert_allclose(mag_out, mag_in, rtol=1e-10) + + return + def test_atmosphere_models(): """ Test the rebinned atmosphere models used for synthetic photometry """ - from spisea import atmospheres as atm - # Array of atmospheres - atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere, - atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere] + atm_arr = [ + atmospheres.get_merged_atmosphere, + atmospheres.get_castelli_atmosphere, + atmospheres.get_phoenixv16_atmosphere, + atmospheres.get_BTSettl_2015_atmosphere, + atmospheres.get_BTSettl_atmosphere, + atmospheres.get_kurucz_atmosphere, + atmospheres.get_phoenix_atmosphere, + atmospheres.get_wdKoester_atmosphere, + atmospheres.get_Phillips2020_atmosphere, + atmospheres.get_Meisner2023_atmosphere + ] # Array of metallicities metals_range = [-2.0, 0, 0.15] + bd_metals_range = [-1.0, -0.5, 0, 0.3] metals_solar = [0] - metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, metals_solar] + metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, + metals_solar, metals_solar, bd_metals_range] assert len(atm_arr) == len(metals_arr) @@ -116,13 +204,13 @@ def test_atmosphere_models(): test = atm_func(metallicity=jj) except: raise Exception('ATM TEST FAILED: {0}, metal = {1}'.format(atm_func, jj)) - + print('Done {0}'.format(atm_func)) - + # Test get_merged_atmospheres at different temps - temp_range = [2000, 3500, 4000, 5250, 6000, 12000] - atm_func = atm.get_merged_atmosphere - for ii in metals_range: + temp_range = [250, 1000, 2000, 3500, 4000, 5250, 6000, 12000] + atm_func = atmospheres.get_merged_atmosphere + for ii in bd_metals_range: for jj in temp_range: try: test = atm_func(metallicity=ii, temperature=jj, verbose=True) @@ -131,39 +219,52 @@ def test_atmosphere_models(): print('get_merged_atmosphere: all temps/metallicities passed') - + # Test get_bb_atmosphere at different temps # This func only requests temp - temp_range = [2000, 3500, 4000, 5250, 6000, 12000] - atm_func = atm.get_bb_atmosphere + temp_range = [1000, 2000, 3500, 4000, 5250, 6000, 12000] + atm_func = atmospheres.get_bb_atmosphere for jj in temp_range: try: test = atm_func(temperature=jj, verbose=True) except: raise Exception('ATM TEST FAILED: {0}, temp = {2}'.format(atm_func, jj)) - + print('get_bb_atmosphere: all temps passed') - + + # Test get_bd_atmosphere at different temps + # This func only requests temp + temp_range = [250, 400, 500, 750, 950, 1200] + atm_func = atmospheres.get_bd_atmosphere + for jj in temp_range: + try: + test = atm_func(temperature=jj, verbose=True) + except: + raise Exception('ATM TEST FAILED: {0}, temp = {1}'.format(atm_func, jj)) + + print('get_bd_atmosphere: all temps passed') + return def test_filters(): """ Test to make sure all of the filters work as expected """ - from spisea import synthetic - # Define vega spectrum vega = synthetic.Vega() - + # Filter list to test filt_list = ['wfc3,ir,f127m','acs,wfc1,f814w', '2mass,J', '2mass,H','2mass,Ks', 'ctio_osiris,K', 'ctio_osiris,H', 'ubv,U', 'ubv,B', 'ubv,V', 'ubv,R', 'ubv,I', 'jg,J', 'jg,H', 'jg,K', - 'decam,y', 'decam,i', 'decam,z', + 'decam,Y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', + 'gaia,dr1,G', 'gaia,dr1,Gbp', 'gaia,dr1,Grp', + 'gaia,dr2,G', 'gaia,dr2,Gbp', 'gaia,dr2,Grp', 'gaia,dr2_rev,G', 'gaia,dr2_rev,Gbp', 'gaia,dr2_rev,Grp', + 'gaia,edr3,G', 'gaia,edr3,Gbp', 'gaia,edr3,Grp', 'jwst,F070W', 'jwst,F090W', 'jwst,F115W', 'jwst,F140M', 'jwst,F150W', 'jwst,F150W2', 'jwst,F162M', 'jwst,F164N', 'jwst,F182M', 'jwst,F187N', 'jwst,F200W', 'jwst,F212N', @@ -177,8 +278,8 @@ def test_filters(): 'nirc1,K', 'nirc1,H', 'nirc2,J', 'nirc2,H', 'nirc2,Kp', 'nirc2,K', 'nirc2,Lp', 'nirc2,Hcont', 'nirc2,FeII', 'nirc2,Brgamma', 'ps1,z', - 'ps1,g', 'ps1,r','ps1,i', 'ps1,y', - 'ukirt,J', 'ukirt,H', 'ukirt,K', + 'ps1,g', 'ps1,r','ps1,i', 'ps1,y', 'ps1,w', + 'ukirt,Z','ukirt,Y','ukirt,J', 'ukirt,H', 'ukirt,K', 'vista,Y', 'vista,Z', 'vista,J', 'vista,H', 'vista,Ks', 'ztf,g', 'ztf,r', 'ztf,i', 'hawki,J', 'hawki,H', 'hawki,Ks', 'roman,wfi,f062', @@ -186,28 +287,31 @@ def test_filters(): 'roman,wfi,f158', 'roman,wfi,f146', 'roman,wfi,f213', 'roman,wfi,f184', 'rubin,g', 'rubin,i', 'rubin,r', 'rubin,u', 'rubin,z', 'rubin,y', - 'euclid,Y', 'euclid,J', 'euclid,H'] + 'euclid,VIS', 'euclid,Y', 'euclid,J', 'euclid,H', + 'nsfcam,L', 'tess,tess', + 'washington,C', 'washington,M', 'washington,T1', 'washington,T2', + 'hipparcos,Hp', 'tycho,B', 'tycho,V', + 'kepler,Kp', 'ogle,Rw', + 'subaru,hsc,g','subaru,hsc,r','subaru,hsc,i','subaru,hsc,z','subaru,hsc,Y', + 'subaru,hsc,nb387', 'subaru,hsc,nb468', 'subaru,hsc,nb515', 'subaru,hsc,nb527', + 'subaru,hsc,nb656', 'subaru,hsc,nb718', 'subaru,hsc,nb816', 'subaru,hsc,nb921', + 'subaru,hsc,nb926', 'subaru,hsc,nb973', + 'bessell,U', 'bessell,B', 'bessell,V', 'bessell,R', 'bessell,I'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: - try: - filt = synthetic.get_filter_info(ii, rebin=True, vega=vega) - except: - raise Exception('get_filter_info TEST FAILED for {0}'.format(ii)) + filt = synthetic.get_filter_info(ii, rebin=True, vega=vega) print('get_filter_info pass') - + # Loop through filters to test that they work: get_obs_str for ii in filt_list: - try: - # Test going from col_name to obs_str - col_name = synthetic.get_filter_col_name(ii) - obs_str = synthetic.get_obs_str('m_{0}'.format(col_name)) - # Does the obs_str work? - filt_info = synthetic.get_filter_info(obs_str) - except: - raise Exception('get_obs_str TEST FAILED for {0}'.format(ii)) - + # Test going from col_name to obs_str + col_name = synthetic.get_filter_col_name(ii) + obs_str = synthetic.get_obs_str('m_{0}'.format(col_name)) + # Does the obs_str work? + filt_info = synthetic.get_filter_info(obs_str) + print('get_obs_str pass') print('Filters done') diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/tests/test_multiplicity.py similarity index 62% rename from spisea/imf/tests/test_multiplicity.py rename to spisea/tests/test_multiplicity.py index 3a9dc9b7..53226651 100755 --- a/spisea/imf/tests/test_multiplicity.py +++ b/spisea/tests/test_multiplicity.py @@ -1,12 +1,12 @@ import numpy as np import time - +import spisea +from spisea.imf import imf, multiplicity + def test_create_MultiplicityUnresolved(): """ Tests creating and accessing a MultiplicityUnresolved object. """ - from .. import multiplicity - # All default parameters -- check their values mu1 = multiplicity.MultiplicityUnresolved() assert mu1.MF_amp == 0.44 @@ -18,28 +18,27 @@ def test_create_MultiplicityUnresolved(): assert mu1.q_min == 0.01 # Test setting different parameters - mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4, + mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4, MF_power=0.4, - CSF_amp=0.4, - CSF_power=0.4, + CSF_amp=0.4, + CSF_power=0.4, CSF_max=4, - q_power=0.4, + q_power=0.4, q_min=0.04) - assert mu2.MF_amp == 0.4 + assert mu2.MF_amp == 0.4 assert mu2.MF_pow == 0.4 - assert mu2.CSF_amp == 0.4 - assert mu2.CSF_pow == 0.4 + assert mu2.CSF_amp == 0.4 + assert mu2.CSF_pow == 0.4 assert mu2.CSF_max == 4 - assert mu2.q_pow == 0.4 + assert mu2.q_pow == 0.4 assert mu2.q_min == 0.04 + def test_multiplicity_fraction(): """ Test creating a MultiplicityUnresolved object and getting the multiplicity fraction out. - """ - from spisea.imf import multiplicity - + """ # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() @@ -57,14 +56,22 @@ def test_multiplicity_fraction(): CSF_amp=0.4, CSF_power=0.4, CSF_max=4, q_power=0.4, q_min=0.04) - mf2_1 = mu1.multiplicity_fraction(1.0) - np.testing.assert_almost_equal(mf2_1, 0.44, decimal=2) + mf2_1 = mu2.multiplicity_fraction(1.0) + np.testing.assert_almost_equal(mf2_1, 0.4, decimal=2) - mf2_2 = mu1.multiplicity_fraction(10.0) + mf2_2 = mu2.multiplicity_fraction(10.0) np.testing.assert_almost_equal(mf2_2, 1.0, decimal=2) - mf2_3 = mu1.multiplicity_fraction(0.1) - np.testing.assert_almost_equal(mf2_3, 0.136, decimal=2) + mf2_3 = mu2.multiplicity_fraction(0.1) + np.testing.assert_almost_equal(mf2_3, 0.159, decimal=2) + + # Test brown dwarf mass fractions + mf_bd1 = mu1.multiplicity_fraction(0.07) # near upper BD limit + mf_bd2 = mu1.multiplicity_fraction(0.04) # mid BD + mf_bd3 = mu1.multiplicity_fraction(0.01) # lower BD limit + assert np.isclose(mf_bd1, 0.16, atol=0.01) + assert np.isclose(mf_bd2, 0.08, atol=0.01) + assert np.isclose(mf_bd3, 0.0, atol=1e-6) def test_multiplicity_fraction_array(): @@ -72,25 +79,32 @@ def test_multiplicity_fraction_array(): Test multiplicity_fraction() on the MultiplicityUnresolved object where the inputs and outputs are in array form. """ - from spisea.imf import multiplicity - # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() - mass_array = np.array([1.0, 10.0, 0.1]) + mass_array = np.array([1.0, 10.0, 0.1, 0.07, 0.04, 0.01]) mf_array = mu1.multiplicity_fraction(mass_array) + # Stellar regime checks np.testing.assert_almost_equal(mf_array[0], 0.44, decimal=2) np.testing.assert_almost_equal(mf_array[1], 1.0, decimal=2) np.testing.assert_almost_equal(mf_array[2], 0.136, decimal=2) - + + # BD regime checks + # interpolation between values implies lower masses --> lower mf + assert mf_array[3] < mf_array[2] + assert mf_array[4] <= mf_array[3] + assert mf_array[5] <= mf_array[4] + + # Ensure mf stars within reasonable bound (upper limit is 0.2) + assert np.all(mf_array[3:] >= 0.0) + assert np.all(mf_array[3:] <= 0.2) + def test_companion_star_fraction(): """ Test the companion_star fraction on the MultiplicityUnresolved object. """ - from spisea.imf import multiplicity - # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() @@ -117,74 +131,115 @@ def test_companion_star_fraction(): # csf2_3 = mu1.companion_star_fraction(0.1) # np.testing.assert_almost_equal(csf2_3, 0.159, decimal=2) + # Test brown dwarf csf + csf_bd1 = mu1.companion_star_fraction(0.07) + csf_bd2 = mu1.companion_star_fraction(0.04) + csf_bd3 = mu1.companion_star_fraction(0.01) + assert np.isclose(csf_bd1, 0.16, atol=0.01) + assert np.isclose(csf_bd2, 0.08, atol=0.01) + assert np.isclose(csf_bd3, 0.0, atol=1e-6) + def test_resolvedmult(): """ - Test creating a MultiplicityResolvedDK object + Test creating a MultiplicityResolvedDK object and that the parameters it's populated with are correct. + Updated to test for specific brown dwarf characteristics. """ from spisea import synthetic, evolution, atmospheres, reddening, ifmr - from spisea.imf import imf, multiplicity - # Fetch isochrone logAge = 6.70 # Age in log(years) AKs = 1.0 # Ks filter extinction in mags dist = 4000 # distance in parsecs metallicity = 0 # metallicity in [M/H] atm_func = atmospheres.get_merged_atmosphere - evo_merged = evolution.MISTv1() + evo_merged = evolution.MergedPhillipsBaraffePisaEkstromParsec() redlaw = reddening.RedLawCardelli(3.1) # Rv = 3.1 filt_list = ['nirc2,J', 'nirc2,Kp'] - + startTime = time.time() iso_merged = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=metallicity, evo_model=evo_merged, atm_func=atm_func, filters=filt_list, red_law=redlaw, - mass_sampling=3) + mass_sampling=3, iso_dir=f'{spisea.__path__[0]}/tests/isochrones') print('Constructed isochrone: %d seconds' % (time.time() - startTime)) - - # Now we can make the cluster. + + # Now we can make the cluster. clust_mtot = 10**4. clust_multiplicity = multiplicity.MultiplicityResolvedDK() # Multiplicity is defined in the IMF object - clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity) + clust_imf_Mult = imf.Salpeter_Kirkpatrick_2024(multiplicity=clust_multiplicity) # Make clusters clust_Mult = synthetic.ResolvedCluster(iso_merged, clust_imf_Mult, clust_mtot) clust_Mult_ss = clust_Mult.star_systems - + print('Constructed cluster: %d seconds' % (time.time() - startTime)) - + #check if columns were created assert 'log_a' in clust_Mult.companions.colnames assert 'e' in clust_Mult.companions.colnames assert 'i' in clust_Mult.companions.colnames assert 'Omega' in clust_Mult.companions.colnames assert 'omega' in clust_Mult.companions.colnames - + #check values are in correct range assert all(10**i<= 2000 and 10**i>= 0 for i in clust_Mult.companions['log_a']) #max separation is 2000 AU assert all(i<= 1 and i>= 0 for i in clust_Mult.companions['e']) assert all(i<= 180 and i>= 0 for i in clust_Mult.companions['i']) assert all(i<= 360 and i>= 0 for i in clust_Mult.companions['omega']) assert all(i<= 360 and i>= 0 for i in clust_Mult.companions['Omega']) - + #checks sign for inclination is being randomly genarated assert any(i > 90 for i in clust_Mult.companions['i']) and any(i < 90 for i in clust_Mult.companions['i']) - + #checks eccentricity follows f(e) = 2e pdf n, bins = np.histogram(clust_Mult.companions['e'], density = True) bin_centers = 0.5*(bins[1:] + bins[:-1]) assert all(np.abs(i) < 0.3 for i in 2*bin_centers - n) - + #checks shape of inclination histogram is sin(i) n, bins = np.histogram(clust_Mult.companions['i']) bin_centers = 0.5*(bins[1:] + bins[:-1]) assert all(np.abs(i) < 0.15 for i in n/max(n) - np.sin(np.pi*bin_centers/180)) - - return - + #checks for brown dwarf specific features + bd_idx = np.where(clust_Mult.star_systems['mass'] < 0.08)[0] + + #check there is only one possible companion per BD + assert all(clust_Mult.star_systems['N_companions'][bd_idx] <= 1), \ + "Brown dwarf primaries have >1 companion." + + comp_rows = [] + start = 0 + for ii, N in enumerate(clust_Mult.star_systems['N_companions']): + if ii in bd_idx and N > 0: + comp_rows.extend(range(start, start+N)) + start += N + + bd_companions = clust_Mult.companions[comp_rows] + + if len(bd_companions) > 30: # only test if enough BD binaries + mean_log_a = np.mean(bd_companions['log_a']) + std_log_a = np.std(bd_companions['log_a']) + + bd_masses = clust_Mult.star_systems['mass'][bd_idx] + expected_sigma = np.mean( + np.interp( + np.log10(bd_masses), + [np.log10(0.01), np.log10(0.08)], + [0.25, 0.5] + ) + ) + + #expect lognormal centered near log10(2.9 AU), width ~0.21 + assert abs(mean_log_a - np.log10(2.9)) < 0.25, \ + f"BD mean log(a) off: {mean_log_a:.2f}" + + assert abs(std_log_a - expected_sigma) < 0.15, \ + f"BD sigma log(a) off: {std_log_a:.2f}" + + return diff --git a/spisea/tests/test_reddening.py b/spisea/tests/test_reddening.py index d4b99d88..67d4842a 100644 --- a/spisea/tests/test_reddening.py +++ b/spisea/tests/test_reddening.py @@ -39,7 +39,7 @@ def test_RedLawBrokenPowerLaw(plots=False): # Compare law_test and the output from the redlaw object law_output = red_law.broken_powerlaw(wave_test, 1) - + assert len(law_test) == len(law_output) assert np.sum(np.isnan(law_output)) == 0 @@ -62,7 +62,7 @@ def test_RedLawBrokenPowerLaw(plots=False): idx2 = np.where(abs(wave_test-2.1) == np.min(abs(wave_test-2.1))) slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2]) assert abs(slope - (-1.0 * alpha1)) < 10**-4 - + # If desired (debug only), make plot to see what law looks like if plots: # Test plot: these should match nearly exactly @@ -109,7 +109,7 @@ def test_RedLawBrokenPowerLaw(plots=False): idx = np.where( (wave_test >= 1.27) & (wave_test < 1.63)) coeff = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2)) law_test[idx] = coeff * wave_test[idx] ** (-1*alpha2) - + # 1.27 - 0.8 idx = np.where( (wave_test >= 0.8) & (wave_test < 1.27)) coeff1 = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2)) @@ -124,7 +124,7 @@ def test_RedLawBrokenPowerLaw(plots=False): coeff3 = (0.8 ** (-1*alpha3)) / (0.8 ** (-1*alpha4)) coeff_f = coeff1 * coeff2 * coeff3 law_test[idx] = coeff_f * wave_test[idx] ** (-1*alpha4) - + assert np.sum(np.isnan(law_test)) == 0 # Put in terms of A_lambda / A_Ks, like the reddening object @@ -133,7 +133,7 @@ def test_RedLawBrokenPowerLaw(plots=False): # Compare law_test and the output from the redlaw object law_output = red_law.broken_powerlaw(wave_test, 1) - + assert len(law_test) == len(law_output) assert np.sum(np.isnan(law_output)) == 0 @@ -166,7 +166,7 @@ def test_RedLawBrokenPowerLaw(plots=False): idx2 = np.where(abs(wave_test-0.7) == np.min(abs(wave_test-0.7))) slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2]) assert abs(slope - (-1.0 * alpha4)) < 10**-4 - + # If desired (debug only), make plot to see what law looks like if plots: # Test plot: these should match nearly exactly @@ -195,9 +195,9 @@ def test_red_law_IsochronePhot(): metallicity = 0 # Metallicity in [M/H] # Define evolution/atmosphere models and extinction law - evo_model = evolution.MISTv1() + evo_model = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere - + # Also specify filters for synthetic photometry. filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f153m', 'nirc2,H', 'nirc2,Kp'] @@ -206,7 +206,7 @@ def test_red_law_IsochronePhot(): 'RL85', 'D16', 'F09,2.5,3', 'S16,1.55,0', 'DM16', 'H18b', 'NL18', 'C89,3.1', 'pl,2.12,0.9,2.4', 'broken_pl,[2.3,1.63,0.9],[2.23, 3.0],2.12'] - + aks_arr = [2.62, 2.46, 1.67, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3] for ii in range(len(redlaw_arr)): redlaw = reddening.get_red_law(redlaw_arr[ii]) @@ -214,15 +214,22 @@ def test_red_law_IsochronePhot(): aks = aks_arr[ii] # Try to run isochrone phot - iso_test = synthetic.IsochronePhot(logAge, aks, dist, metallicity=0, - evo_model=evo_model, atm_func=atm_func, - red_law=redlaw, filters=filt_list, - min_mass=0.95, max_mass=1.05) - # Now remove the iso file to make sure we recalc each time - cmd = 'rm iso_6.70_*_08000_p00.fits' - os.system(cmd) + iso_test = synthetic.IsochronePhot( + logAge, + aks, + dist, + metallicity=0, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + min_mass=0.95, + max_mass=1.05, + iso_dir='isochrones/', + recomp=True + ) print('----EL {0} works OK!-----'.format(redlaw_arr[ii])) - + return def test_all_EL(): @@ -247,5 +254,6 @@ def test_all_EL(): red_law = reddening.RedLawSchoedel10() red_law = reddening.RedLawNoguerasLara18() red_law = reddening.RedLawNoguerasLara20() + red_law = reddening.RedLawSODC(2.5) return diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index ec2628a2..9a304fb5 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1,15 +1,19 @@ +import os +import pdb import time +import spisea +import pytest +import warnings +import importlib import numpy as np import pylab as plt -import numpy as np -from spisea import reddening, evolution, atmospheres, ifmr -from spisea import synthetic as syn -from spisea.imf import imf -from spisea.imf import multiplicity -import pysynphot -import os -import pdb +from astropy.table import Table from scipy.spatial import cKDTree as KDTree +from spisea import synthetic as syn +from spisea.imf import imf, multiplicity +from spisea import reddening, evolution, atmospheres, ifmr + +spisea_path = os.path.dirname(spisea.__file__) def test_isochrone(plot=False): logAge = 6.7 @@ -26,33 +30,35 @@ def test_isochrone(plot=False): assert iso.points.meta['AKS'] == AKs assert iso.points.meta['DISTANCE'] == distance assert len(iso.points) > 100 - + if plot: - plt.figure(1) + plt.figure(1) iso.plot_HR_diagram() - + plt.figure(2) iso.plot_mass_luminosity() - return iso + #return iso def test_iso_wave(): """ - Test to make sure isochrones generated have spectra with the proper + Test to make sure isochrones generated have spectra with the proper wavelength range, and that the user has control over that wavelength range (propagated through IsochronePhot) """ # Define isochrone parameters - logAge = np.log10(5*10**6.) # Age in log(years) - AKs = 0.8 # extinction in mags + logAge = 6.7 # Age in log(years) + AKs = 2.7 # extinction in mags dist = 4000 # distance in parsec + metal = 0.0 # metallicity + iso_dir = f'{spisea_path}/tests/isochrones' # Define evolution/atmosphere models and extinction law (optional) - evo_model = evolution.MergedBaraffePisaEkstromParsec() + evo_model = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - # Also specify filters for synthetic photometry (optional). Here we use + # Also specify filters for synthetic photometry (optional). Here we use # the HST WFC3-IR F127M, F139M, and F153M filters filt_list = ['wfc3,ir,f127m'] @@ -65,11 +71,18 @@ def test_iso_wave(): # Make Isochrone object. Will use wave_range = [3000,52000]. # Make sure range matches to resolution of atmosphere. wave_range1 = [3000, 52000] - my_iso = syn.IsochronePhot(logAge, AKs, dist, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10, wave_range=wave_range1, - recomp=True) + my_iso = syn.IsochronePhot( + logAge, AKs, dist, + metallicity=metal, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + wave_range=wave_range1, + recomp=True, + iso_dir=iso_dir + ) test = my_iso.spec_list[0] @@ -79,11 +92,19 @@ def test_iso_wave(): # Now let's try changing the wave range. Is it carried through # properly? wave_range2 = [1200, 90000] - my_iso = syn.IsochronePhot(logAge, AKs, dist, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10, wave_range=wave_range2, - recomp=True) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + wave_range=wave_range2, + recomp=True, + iso_dir=iso_dir + ) test2 = my_iso.spec_list[0] @@ -93,13 +114,21 @@ def test_iso_wave(): # Does the error exception catch the bad wave_range? wave_range3 = [1200, 1000000] try: - my_iso = syn.IsochronePhot(logAge, AKs, dist, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10, wave_range=wave_range3, - recomp=True) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + wave_range=wave_range3, + recomp=True, + iso_dir=iso_dir + ) print('WAVE TEST FAILED!!! Should have crashed here, wavelength range out of bounds') - raise ValueError() + raise ValueError() except: print('Wavelength out of bound condition passed. Test is good') pass @@ -111,20 +140,28 @@ def test_IsochronePhot(plot=False): distance = 4000 filt_list = ['wfc3,ir,f127m', 'nirc2,J'] mass_sampling=1 - iso_dir = 'iso/' + iso_dir = f'{spisea_path}/tests/isochrones' evo_model = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere redlaw = reddening.RedLawNishiyama09() startTime = time.time() - iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir, + recomp=True + ) endTime = time.time() print('IsochronePhot generated in: %d seconds' % (endTime - startTime)) - # Typically takes 120 seconds if file is regenerated. + # Typically takes 40 seconds if file is regenerated. # Limited by pysynphot.Icat call in atmospheres.py assert iso.points.meta['LOGAGE'] == logAge @@ -135,42 +172,64 @@ def test_IsochronePhot(plot=False): assert 'm_nirc2_J' in iso.points.colnames if plot: - plt.figure(1) + plt.figure(1) iso.plot_CMD('mag814w', 'mag160w') - + plt.figure(2) iso.plot_mass_magnitude('mag160w') # Finally, let's test the isochronePhot file generation - assert os.path.exists('{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_p00.fits'.format(iso_dir, logAge, - AKs, str(distance).zfill(5))) - + metal_value = 0. + metal_sign = 'm' if metal_value < 0 else 'p' + assert os.path.exists(f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(distance).zfill(5)}_{metal_sign}{metal_value:.2f}.fits') + # Check 1: If we try to remake the isochrone, does it read the file rather than # making a new one - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == False - + # Check 2: Confirm that adding a new column to an existing isochrone works properly. # Does the new filter get added to the isochrone? And the old ones still there? # Does the computed data for the new filter match the same result if you fully regenerate the isochrone? - iso_new_addfilt = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list+['2mass,Ks'], - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new_addfilt = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list+['2mass,Ks'], + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new_addfilt.recalc == False assert 'm_2mass_Ks' in iso_new_addfilt.points.colnames assert 'm_nirc2_J' in iso_new_addfilt.points.colnames - - iso_new_3filt = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list+['2mass,Ks'], - mass_sampling=mass_sampling, iso_dir=iso_dir, - recomp=True) + + iso_new_3filt = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list+['2mass,Ks'], + mass_sampling=mass_sampling, + recomp=True, + iso_dir=iso_dir + ) np.testing.assert_almost_equal(iso_new_addfilt.points['m_2mass_Ks'], iso_new_3filt.points['m_2mass_Ks']) assert iso_new_3filt.recalc==True @@ -179,26 +238,47 @@ def test_IsochronePhot(plot=False): evo2 = evolution.MergedBaraffePisaEkstromParsec() mass_sampling=20 - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, - atm_func=atm_func, red_law=redlaw, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo2, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == True redlaw2 = reddening.RedLawHosek18b() - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, - atm_func=atm_func, red_law=redlaw2, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo2, + atm_func=atm_func, + red_law=redlaw2, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == True atm2 = atmospheres.get_castelli_atmosphere - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, - atm_func=atm2, red_law=redlaw2, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo2, + atm_func=atm2, + red_law=redlaw2, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == True @@ -211,27 +291,36 @@ def test_ResolvedCluster(): distance = 4000 cluster_mass = 10**5. mass_sampling=5 + iso_dir = f'{spisea_path}/tests/isochrones' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir, + recomp=True + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. - imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) - imf_powers = np.array([-1.3, -2.3, -2.3]) + imf_mass_limits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) + imf_powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) ########## # Start without multiplicity @@ -239,7 +328,7 @@ def test_ResolvedCluster(): my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) @@ -267,7 +356,7 @@ def test_ResolvedCluster(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) @@ -277,7 +366,7 @@ def test_ResolvedCluster(): assert np.sum(clust2['N_companions']) == len(cluster2.companions) ########## - # Plots + # Plots ########## # Plot an IR CMD and compare cluster members to isochrone. plt.figure(1) @@ -287,7 +376,7 @@ def test_ResolvedCluster(): plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c-') plt.gca().invert_yaxis() plt.xlabel('J - Kp (mag)') - plt.ylabel('J (mag') + plt.ylabel('J (mag)') # Plot a mass-magnitude relationship. plt.figure(2) @@ -297,7 +386,7 @@ def test_ResolvedCluster(): plt.gca().invert_yaxis() plt.xlabel('Mass (Msun)') plt.ylabel('J (mag)') - + # # Plot the spectrum of the most massive star # idx = cluster.mass.argmax() # plt.clf() @@ -317,21 +406,29 @@ def test_ResolvedClusterDiffRedden(): cluster_mass = 10**5. deltaAKs = 0.05 mass_sampling=5 + iso_dir = f'{spisea_path}/tests/isochrones' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] - + startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -344,13 +441,13 @@ def test_ResolvedClusterDiffRedden(): my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + cluster1 = syn.ResolvedClusterDiffRedden(iso, my_imf1, cluster_mass, deltaAKs) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) assert len(clust1) > 0 - + plt.figure(3) plt.clf() plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.') @@ -367,7 +464,7 @@ def test_ResolvedClusterDiffRedden(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedClusterDiffRedden(iso, my_imf2, cluster_mass, deltaAKs) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) @@ -377,7 +474,7 @@ def test_ResolvedClusterDiffRedden(): assert np.sum(clust2['N_companions']) == len(cluster2.companions) ########## - # Plots + # Plots ########## # Plot an IR CMD and compare cluster members to isochrone. plt.figure(1) @@ -399,7 +496,7 @@ def test_ResolvedClusterDiffRedden(): plt.ylabel('J (mag)') return - + def test_UnresolvedCluster(): log_age = 6.7 AKs = 0.0 @@ -407,9 +504,9 @@ def test_UnresolvedCluster(): metallicity=0 cluster_mass = 10**4. - startTime = time.time() + startTime = time.time() multi = multiplicity.MultiplicityUnresolved() - imf_in = imf.Kroupa_2001(multiplicity=multi) + imf_in = imf.Salpeter_Kirkpatrick_2024(multiplicity=multi) evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere iso = syn.Isochrone(log_age, AKs, distance, metallicity=metallicity, @@ -434,49 +531,57 @@ def test_ifmr_multiplicity(): distance = 1000 cluster_mass = 1e6 mass_sampling = 5 + iso_dir = f'{spisea_path}/tests/isochrones' # Test all filters filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J'] startTime = time.time() - evo = evolution.MISTv1() + evo = evolution.MergedPhillipsBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere ifmr_obj = ifmr.IFMR_Raithel18() red_law = reddening.RedLawNishiyama09() - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir, + recomp=True + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. - imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) - imf_powers = np.array([-1.3, -2.3, -2.3]) + imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf]) + imf_powers = np.array([-0.3, -1.3, -2.3, -2.3]) ########## # Start without multiplicity and IFMR ########## my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) - print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + print('Constructed IMF: %d seconds' % (time.time() - startTime)) + cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=ifmr_obj) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) - + ########## # Test with multiplicity and IFMR ########## multi = multiplicity.MultiplicityUnresolved() - my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, - multiplicity=multi) + my_imf2 = imf.Salpeter_Kirkpatrick_2024(multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj) clust2 = cluster2.star_systems comps2 = cluster2.companions @@ -493,18 +598,75 @@ def test_ifmr_multiplicity(): assert len(np.where(clust2['phase'] == 102)) > 0 assert len(np.where(clust1['phase'] == 103)) > 0 # BH assert len(np.where(clust2['phase'] == 103)) > 0 + assert len(np.where(clust1['phase'] == 90)) > 0 # BD + assert len(np.where(clust2['phase'] == 90)) > 0 - # Now check that we have companions that are WDs, NSs, and BHs + # Now check that we have companions that are WDs, NSs, BHs, and BDs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 + assert len(np.where(comps2['phase'] == 90)) > 0 # Make sure no funky phase designations (due to interpolation effects) # slipped through - idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 101) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 101) & (comps2['phase'] != 9) ) + + bd_masses = np.where((clust1['mass'] >= 0.01) & (clust1['mass'] <= 0.08)) + bd_mask = (clust1['mass'] >= 0.01) & (clust1['mass'] <= 0.08) + print(clust1[['mass', 'phase']][bd_masses]) + idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 90) & (clust1['phase'] != 9) ) assert len(idx[0]) == 0 + """ + 07/2024: Added more testing criteria for brown dwarf stars to ensure they are labeled appropriately for masses from 0.01 - 0.08 M_sun. + """ + # For cluster objects + MIN_MASS = 0.1 + BD_MIN_MASS = 0.01 + BD_MAX_MASS = 0.08 + + wd_idx = np.where(clust1['phase'] == 101) + ns_idx = np.where(clust1['phase'] == 102) + bh_idx = np.where(clust1['phase'] == 103) + bd_idx = np.where(clust1['phase'] == 90) + + print(clust1[bd_idx]) + assert np.all(clust1['mass'][wd_idx] > MIN_MASS) + assert np.all(clust1['mass'][ns_idx] > MIN_MASS) + assert np.all(clust1['mass'][bh_idx] > MIN_MASS) + assert np.all(clust1['mass'][bd_idx] < MIN_MASS) + + # For companion objects + comp_wd_idx = np.where(comps2['phase'] == 101) + comp_ns_idx = np.where(comps2['phase'] == 102) + comp_bh_idx = np.where(comps2['phase'] == 103) + comp_bd_idx = np.where(comps2['phase'] == 90) + assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_bd_idx] < MIN_MASS) + + # Ensure brown dwarfs are within 0.01 and 0.08 solar masses + assert np.all((clust1['mass'][bd_idx] >= BD_MIN_MASS) & + (clust1['mass'][bd_idx] <= BD_MAX_MASS)) + assert np.all((comps2['mass'][comp_bd_idx] >= BD_MIN_MASS) & + (comps2['mass'][comp_bd_idx] <= BD_MAX_MASS)) + + # Ensure no other objects are in the brown dwarf mass range + non_bd_idx = np.where((clust1['phase'] != 90) & + (clust1['mass'] >= BD_MIN_MASS) & + (clust1['mass'] <= BD_MAX_MASS)) + assert len(non_bd_idx[0]) == 0 # asserting no non-brown dwarf objects in BD mass range + + comp_non_bd_idx = np.where((comps2['phase'] != 90) & + (comps2['mass'] >= BD_MIN_MASS) & + (comps2['mass'] <= BD_MAX_MASS)) + print(comps2[comp_non_bd_idx]['phase', 'mass']) + assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + + # Ensure BD temperature assignment is working correctly + assert np.all(clust1['Teff'][bd_idx] != np.nan) + assert np.all(comps2['Teff'][comp_bd_idx] != np.nan) + return def test_metallicity(): @@ -512,35 +674,53 @@ def test_metallicity(): Test isochrone generation at different metallicities """ # Define isochrone parameters - logAge = np.log10(5*10**6.) - AKs = 0.8 - dist = 4000 + logAge = np.log10(5*10**6.) + AKs = 0.8 + dist = 4000 evo_model = evolution.MISTv1() atm_func = atmospheres.get_phoenixv16_atmosphere red_law = reddening.RedLawHosek18b() filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m'] + iso_dir = f'{spisea_path}/tests/isochrones' - # Start with a solar metallicity isochrone - metallicity= 0.0 + # Start with a solar metallicity isochrone + metallicity= 0. + metal_sign = 'm' if metallicity < 0 else 'p' # Make Isochrone object, with high mass_sampling to decrease compute time - my_iso = syn.IsochronePhot(logAge, AKs, dist, metallicity=metallicity, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + metallicity=metallicity, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + iso_dir=iso_dir + ) # Test isochrone properties assert my_iso.points.meta['METAL_IN'] == 0.0 - assert os.path.exists('iso_6.70_0.80_04000_p00.fits') + assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_p0.00.fits') # Now for non-solar metallicity metallicity= -1.5 # Make Isochrone object, with high mass_sampling to decrease compute time - my_iso = syn.IsochronePhot(logAge, AKs, dist, metallicity=metallicity, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + metallicity=metallicity, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + iso_dir=iso_dir + ) # MIST model sub-directory names changed in SPISEA v2.1.4 update; # changing what "metal_act" value was. version 1 of MIST grid @@ -555,12 +735,12 @@ def test_metallicity(): metal_act = np.log10(0.00047 / 0.0142) # For Mist isochrones else: metal_act = np.log10(0.00045 / 0.0142) # For Mist isochrones - + # Test isochrone properties assert my_iso.points.meta['METAL_IN'] == -1.5 assert np.isclose(my_iso.points.meta['METAL_ACT'], metal_act) - assert os.path.exists('iso_6.70_0.80_04000_m15.fits') - + assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_m1.50.fits') + return def test_cluster_mass(): @@ -570,21 +750,29 @@ def test_cluster_mass(): distance = 4000 cluster_mass = 10**5. mass_sampling = 5 + iso_dir = f'{spisea_path}/tests/isochrones' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() - + # Define evolution/atmosphere models and extinction law - evo = evolution.MISTv1() + evo = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -594,7 +782,7 @@ def test_cluster_mass(): # IFMR my_ifmr = ifmr.IFMR_Raithel18() - + ########## # Start without multiplicity @@ -602,7 +790,7 @@ def test_cluster_mass(): my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=my_ifmr) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) @@ -628,7 +816,7 @@ def test_cluster_mass(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=my_ifmr) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) @@ -642,7 +830,7 @@ def test_cluster_mass(): def test_keep_low_mass_stars(): """ - Test "keep_low_mass_stars = True" functionality introduced in v2.2 + Test "keep_low_mass_stars = True" functionality introduced in v2.2 """ # Define cluster parameters, pulling on an isochrone generated in an earlier test (since # we don't care about isochrone generation here @@ -651,26 +839,35 @@ def test_keep_low_mass_stars(): distance = 4000 cluster_mass = 10**5. mass_sampling = 5 + iso_dir = f'{spisea_path}/tests/isochrones' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] - + # Define evolution/atmosphere models and extinction law - evo = evolution.MISTv1() + evo = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir, + recomp=True + ) # Get the minimum mass in the isochrones. This should be the lowest # mass psosbile when keep_low_mass_stars == False. # Make sure this min mass is low enough for a reasonalbe test min_mass_iso = np.min(iso.points['mass']) assert min_mass_iso >= 0.05 - + # Define IMF + IFMR. Make sure IMF goes to really low masses, # below the 0.08 Msun limit of the MIST isochrones imf_min = 0.01 @@ -698,33 +895,41 @@ def test_keep_low_mass_stars(): return - + def test_compact_object_companions(): - + # Define cluster parameters logAge = 6.7 AKs = 2.4 distance = 4000 cluster_mass = 10**4. mass_sampling=5 + iso_dir = f'{spisea_path}/tests/isochrones' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) - + clust_multiplicity = multiplicity.MultiplicityResolvedDK() massLimits = np.array([0.2, 0.5, 1, 120]) # mass segments @@ -739,6 +944,160 @@ def test_compact_object_companions(): assert (len(nan_lum_companions) == 0) | (all(np.isnan(nan_lum_companions['phase'])) == False) +def _require_cosmic(): + """ + Skip the calling test if the optional `cosmic` package is not installed, + emitting a warning so the skip is visible (rather than silent). + """ + if importlib.util.find_spec('cosmic') is None: + msg = ('COSMIC integration test skipped: the optional `cosmic` package ' + 'is not installed. Run in an environment with COSMIC (e.g. ' + '`astro_cosmic`) to exercise these tests.') + warnings.warn(msg) + pytest.skip(msg) + + return + +def test_COSMIC_evolve(): + """ + Test the COSMIC external evolution model's evolve() method directly on a + small, hand-built set of star systems and companions. Uses a young, low-mass + population so no compact remnants/disruptions occur, keeping the run fast + and avoiding the merger/disruption branches. + + Skipped (with a warning) if the optional `cosmic` package is not installed. + """ + _require_cosmic() + + from astropy.table import Table + + # Build a minimal star_systems table: 2 binaries + 1 single + star_systems = Table() + star_systems['mass'] = np.array([1.0, 0.9, 0.5]) + star_systems['isMultiple'] = np.array([True, True, False]) + star_systems['N_companions'] = np.array([1, 1, 0]) + star_systems['systemMass'] = np.array([1.5, 1.3, 0.5]) + + # Companions for the first two systems + companions = Table() + companions['system_idx'] = np.array([0, 1]) + companions['mass'] = np.array([0.5, 0.4]) + companions['log_a'] = np.array([1.0, 1.5]) # log10(AU) + companions['e'] = np.array([0.1, 0.2]) + companions['i'] = np.array([30.0, 60.0]) # degrees + companions['Omega'] = np.array([0.0, 0.0]) + companions['omega'] = np.array([0.0, 0.0]) + + evo = evolution.COSMIC(keep_disrupted_companions=False, keep_COSMIC_tables=True) + ss, comp = evo.evolve(star_systems, companions, logAge=8.0, metallicity=0.0) + + # Check that evolve() populated the expected output columns + expected_cols = ['mass_current', 'Teff', 'L', 'logg', 'phase', + 'kick', 'kick_x', 'kick_y', 'kick_z'] + for col in expected_cols: + assert col in ss.colnames, 'star_systems missing column {0}'.format(col) + assert col in comp.colnames, 'companions missing column {0}'.format(col) + + # Core bookkeeping invariant: companion count must match companion table length + assert np.sum(ss['N_companions']) == len(comp) + + # Scalar kick must be the magnitude of the kick vector components + np.testing.assert_allclose( + ss['kick'], np.sqrt(ss['kick_x']**2 + ss['kick_y']**2 + ss['kick_z']**2)) + np.testing.assert_allclose( + comp['kick'], np.sqrt(comp['kick_x']**2 + comp['kick_y']**2 + comp['kick_z']**2)) + + # Young, low-mass stars should remain stellar (no compact remnants here). + # Compact-object phase codes are 101 (WD), 102 (NS), 103 (BH). + assert np.all(ss['phase'] < 100) + assert np.all(comp['phase'] < 100) + + # keep_COSMIC_tables=True should store the raw COSMIC output tables + for attr in ['bpp', 'bcm', 'initC', 'kick_info']: + assert hasattr(evo, attr), 'COSMIC model missing table {0}'.format(attr) + + return + +def test_COSMIC_ResolvedCluster(): + """ + Test the full COSMIC cluster pipeline, mirroring the Cluster_w_COSMIC + tutorial: build an IsochronePhotExternalEvolution, then a ResolvedCluster + with binary multiplicity (CSF_max=1, companion_max=True), and check the + output tables. + + Skipped (with a warning) if the optional `cosmic` package is not installed. + """ + _require_cosmic() + + # Cluster/isochrone parameters (kept small/cheap) + logAge = 9.0 + AKs = 0.0 + distance = 4000 + metallicity = 0.0 + cluster_mass = 10**3. + mass_sampling = 10 + atm_grid_dir = f'{spisea_path}/tests/atm_cosmic' + + filt_list = ['ubv,V'] + + # External evolution model (keep tables so we can verify them) + evo = evolution.COSMIC(keep_COSMIC_tables=True) + atm_func = atmospheres.get_merged_atmosphere_w_bb_supplement + red_law = reddening.RedLawCardelli(3.1) + + iso = syn.IsochronePhotExternalEvolution( + logAge, + AKs, + distance, + metallicity=metallicity, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + atm_grid_dir=atm_grid_dir, + mass_sampling=mass_sampling, + recomp=False + ) + + # COSMIC only supports binaries: resolved multiplicity, no higher-order systems + clust_multiplicity = multiplicity.MultiplicityResolvedDK(CSF_max=1, companion_max=True) + my_imf = imf.Kroupa_2001(multiplicity=clust_multiplicity) + + cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass) + star_systems = cluster.star_systems + companions = cluster.companions + + # Basic sanity: stars and companions were produced + assert len(star_systems) > 0 + assert len(companions) > 0 + + # Core bookkeeping invariant + assert np.sum(star_systems['N_companions']) == len(companions) + + # Kick columns present and self-consistent on both tables + for tab in (star_systems, companions): + for col in ['kick', 'kick_x', 'kick_y', 'kick_z']: + assert col in tab.colnames + np.testing.assert_allclose( + tab['kick'], np.sqrt(tab['kick_x']**2 + tab['kick_y']**2 + tab['kick_z']**2)) + + # Synthetic photometry column should exist + assert 'm_ubv_V' in star_systems.colnames + + # All phase codes should be in the allowed set: + # 0-9 stellar phases, 101 (WD), 102 (NS), 103 (BH) + allowed_phases = set(range(0, 10)) | {101, 102, 103} + ss_phases = set(np.unique(star_systems['phase']).astype(int).tolist()) + comp_phases = set(np.unique(companions['phase']).astype(int).tolist()) + assert ss_phases.issubset(allowed_phases), 'Unexpected star phases: {0}'.format(ss_phases - allowed_phases) + assert comp_phases.issubset(allowed_phases), 'Unexpected companion phases: {0}'.format(comp_phases - allowed_phases) + + # keep_COSMIC_tables=True should expose the raw COSMIC tables on the evo model + for attr in ['bpp', 'bcm', 'initC', 'kick_info']: + assert hasattr(iso.evo_model, attr), 'COSMIC model missing table {0}'.format(attr) + + return + #=================================# # Additional timing functions #=================================# @@ -748,17 +1107,25 @@ def time_test_cluster(): AKs = 2.7 distance = 4000 cluster_mass = 10**4 + iso_dir = f'{spisea_path}/tests/isochrones' startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() filt_list = ['nirc2,J', 'nirc2,Kp'] - - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list) + + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) imf_limits = np.array([0.07, 0.5, 150]) @@ -766,23 +1133,30 @@ def time_test_cluster(): multi = multiplicity.MultiplicityUnresolved() my_imf = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass) print('Constructed cluster: %d seconds' % (time.time() - startTime)) return - + def model_young_cluster_object(resolved=False): log_age = 6.5 AKs = 0.1 distance = 8000.0 cluster_mass = 10000. - + multi = multiplicity.MultiplicityUnresolved() imf_in = imf.Kroupa_2001(multiplicity=multi) - evo = evolution.MergedPisaEkstromParsec() + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere - iso = syn.Isochrone(log_age, AKs, distance, evo, mass_sampling=10) + iso = syn.Isochrone( + log_age, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + mass_sampling=10 + ) if resolved: cluster = syn.ResolvedCluster(iso, imf_in, cluster_mass) @@ -804,30 +1178,31 @@ def model_young_cluster_object(resolved=False): plt.plot(wave, flux, 'k.') return - + def time_test_mass_match(): log_age = 6.7 AKs = 2.7 distance = 4000 cluster_mass = 5e3 - + iso_dir = f'{spisea_path}/tests/isochrones' + imf_in = imf.Kroupa_2001(multiplicity=None) start_time = time.time() - iso = syn.IsochronePhot(log_age, AKs, distance) + iso = syn.IsochronePhot(log_age, AKs, distance, iso_dir=iso_dir) iso_masses = iso.points['mass'] print('Generated iso masses in {0:.0f} s'.format(time.time() - start_time)) start_time = time.time() star_masses, isMulti, compMass, sysMass = imf_in.generate_cluster(cluster_mass) print('Generated cluster masses in {0:.0f} s'.format(time.time() - start_time)) - + def match_model_masses1(isoMasses, starMasses): indices = np.empty(len(starMasses), dtype=int) - + for ii in range(len(starMasses)): theMass = starMasses[ii] - + dm = np.abs(isoMasses - theMass) mdx = dm.argmin() @@ -838,12 +1213,12 @@ def match_model_masses1(isoMasses, starMasses): indices[ii] = mdx return indices - + def match_model_masses2(isoMasses, starMasses): isoMasses_tmp = isoMasses.reshape((len(isoMasses), 1)) kdt = KDTree(isoMasses_tmp) - + starMasses_tmp = starMasses.reshape((len(starMasses), 1)) q_results = kdt.query(starMasses_tmp, k=1) indices = q_results[1] @@ -852,7 +1227,7 @@ def match_model_masses2(isoMasses, starMasses): idx = np.where(dm_frac > 0.1)[0] indices[idx] = -1 - + return indices print('Test #1 START') @@ -883,7 +1258,7 @@ def FeH_from_Z(Z): metal = np.array([2.0e-4, 1.0e-3, 2.0e-3, 2.0e-2]) #ensure that all Spera metallicity regimes are represented FeH = FeH_from_Z(metal) #generate death mass takes metallicty as [Fe/H] - #want to get a good range of masses for Spera, should expect 8 invalids, 8 WDs, 3 NSs, and 9 BHs + #want to get a good range of masses for Spera, should expect 8 invalids, 8 WDs, 3 NSs, and 9 BHs ZAMS = np.array([-0.2*np.ones(len(FeH)), 0.2*np.ones(len(FeH)), 4.0*np.ones(len(FeH)), 9.2*np.ones(len(FeH)), 15.0*np.ones(len(FeH)), 30.0*np.ones(len(FeH)), 150.0*np.ones(len(FeH))]) @@ -975,7 +1350,7 @@ def test_Spera15_IFMR_7(): assert len(BH_idx) == 10 , "There are not the right number of BHs for the Spera15 IFMR" return - + def generate_Raithel18_IFMR(): """ Make a set of objects using the Raithel18 IFMR for the purposes of testing @@ -984,7 +1359,7 @@ def generate_Raithel18_IFMR(): """ Raithel = ifmr.IFMR_Raithel18() - ZAMS = np.array([-0.2, 0.2, 1.0, 7.0, 10.0, 14.0, 16.0, 18.0, 18.6, 22.0, 26.0, 28.0, 50.0, 61.0, 119.0, 121.0]) + ZAMS = np.array([-0.2, 0.2, 1.0, 7.0, 10.0, 14.0, 16.0, 18.0, 18.6, 22.0, 26.0, 28.0, 50.0, 61.0, 119.0, 121.0]) #3 invalid indices, 2 WDs, cannot make statements about #of BHs and NSs because the Raithel IFMR has some randomness output_array = Raithel.generate_death_mass(ZAMS) @@ -1052,3 +1427,95 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" return + +def test_ResolvedCluster_random_state(): + """ + Test that the random state is properly set in ResolvedCluster, such that two clusters with the same seed have the same stars. + """ + log_age = 6.7 + AKs = 2.7 + distance = 4000 + cluster_mass = 10**4. + iso_dir = f'{spisea_path}/tests/isochrones' + + evo = evolution.MergedBaraffePisaEkstromParsec() + atm_func = atmospheres.get_merged_atmosphere + red_law = reddening.RedLawNishiyama09() + filt_list = ['nirc2,J', 'nirc2,Kp'] + + iso = syn.IsochronePhot( + log_age, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + iso_dir=iso_dir, + recomp=True + ) + + imf_limits = np.array([0.07, 0.5, 150]) + imf_powers = np.array([-1.3, -2.35]) + imf_multi = multiplicity.MultiplicityUnresolved() + imf_test = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=imf_multi) + + # Test that the same random seed produces the same cluster + imf_test.rng = np.random.default_rng(seed=42) + result1 = imf_test.generate_cluster(cluster_mass) + imf_test.rng = np.random.default_rng(seed=42) + result2 = imf_test.generate_cluster(cluster_mass) + np.testing.assert_equal(result1, result2) + + # Test that two clusters generated with the same seed have the same star systems and companions + cluster1 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42) + cluster2 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42) + np.testing.assert_array_equal(cluster1.star_systems, cluster2.star_systems) + + old_star_systems = Table.read(f'{spisea_path}/tests/test_data/star_systems.fits') + old_companion = Table.read(f'{spisea_path}/tests/test_data/companions.fits') + + for key in old_star_systems.colnames: + #np.testing.assert_array_equal(cluster1.star_systems[key], old_star_systems[key]) + np.testing.assert_allclose(cluster1.star_systems[key], old_star_systems[key], rtol=1e-5, atol=1e-8) + + for key in old_companion.colnames: + # Require values are consistent within reasonable bounds + # np.testing.assert_array_equal(cluster1.companions[key], old_companion[key]) + np.testing.assert_allclose(cluster1.companions[key], old_companion[key], rtol=1e-5, atol=1e-8) + + return + +def test_ResolvedCluster_no_companions(): + """ + Test case where no companions get generated to + make sure we don't get any errors. This relies on using + a specific seed that results in no companions being generated. + """ + + # Define cluster parameters + logAge = 6.7 + AKs = 2.4 + distance = 4000 + cluster_mass = 10**2 + iso_dir = f'{spisea_path}/tests/isochrones' + + # Test filters + filt_list = ['nirc2,J', 'nirc2,Kp'] + evo = evolution.MergedBaraffePisaEkstromParsec() + atm_func = atmospheres.get_merged_atmosphere + red_law = reddening.RedLawNishiyama09() + + iso = syn.IsochronePhot(logAge, AKs, distance, metallicity=0, + evo_model=evo, atm_func=atm_func, + red_law=red_law, filters=filt_list, + iso_dir=iso_dir) + + imf_multi = multiplicity.MultiplicityResolvedDK() + my_imf = imf.Kroupa_2001(multiplicity=imf_multi) + + cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass, + keep_low_mass_stars=True, seed=1074) + + assert(~np.any(cluster.star_systems['isMultiple'])) diff --git a/tox.ini b/tox.ini deleted file mode 100644 index 27d48961..00000000 --- a/tox.ini +++ /dev/null @@ -1,93 +0,0 @@ -[tox] -envlist = - py{36,37,38}-test{,-alldeps,-devdeps}{,-cov} - py{36,37,38}-test-numpy{116,117,118} - py{36,37,38}-test-astropy{30,40,lts} - build_docs - linkcheck - codestyle -requires = - setuptools >= 30.3.0 - pip >= 19.3.1 -isolated_build = true -indexserver = - NIGHTLY = https://pypi.anaconda.org/scipy-wheels-nightly/simple - -[testenv] -# Suppress display of matplotlib plots generated during docs build -setenv = MPLBACKEND=agg - -# Pass through the following environment variables which may be needed for the CI -passenv = HOME WINDIR LC_ALL LC_CTYPE CC CI TRAVIS - -# Run the tests in a temporary directory to make sure that we don't import -# this package from the source tree -changedir = .tmp/{envname} - -# tox environments are constructed with so-called 'factors' (or terms) -# separated by hyphens, e.g. test-devdeps-cov. Lines below starting with factor: -# will only take effect if that factor is included in the environment name. To -# see a list of example environments that can be run, along with a description, -# run: -# -# tox -l -v -# -description = - run tests - alldeps: with all optional dependencies - devdeps: with the latest developer version of key dependencies - oldestdeps: with the oldest supported version of key dependencies - cov: and test coverage - numpy116: with numpy 1.16.* - numpy117: with numpy 1.17.* - numpy118: with numpy 1.18.* - astropy30: with astropy 3.0.* - astropy40: with astropy 4.0.* - astropylts: with the latest astropy LTS - -# The following provides some specific pinnings for key packages -deps = - - numpy116: numpy==1.16.* - numpy117: numpy==1.17.* - numpy118: numpy==1.18.* - - astropy30: astropy==3.0.* - astropy40: astropy==4.0.* - astropylts: astropy==4.0.* - - devdeps: :NIGHTLY:numpy - devdeps: git+https://github.com/astropy/astropy.git#egg=astropy - -# The following indicates which extras_require from setup.cfg will be installed -extras = - test - alldeps: all - -commands = - pip freeze - !cov: pytest --pyargs spisea {toxinidir}/docs {posargs} - cov: pytest --pyargs spisea {toxinidir}/docs --cov spisea --cov-config={toxinidir}/setup.cfg {posargs} - -[testenv:build_docs] -changedir = docs -description = invoke sphinx-build to build the HTML docs -extras = docs -commands = - pip freeze - sphinx-build -W -b html . _build/html - -[testenv:linkcheck] -changedir = docs -description = check the links in the HTML docs -extras = docs -commands = - pip freeze - sphinx-build -W -b linkcheck . _build/html - -[testenv:codestyle] -skip_install = true -changedir = . -description = check code style, e.g. with flake8 -deps = flake8 -commands = flake8 spisea --count --max-line-length=100