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Adding SSA tutorial
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‎tutorials/spitzer/plot_Spitzer_IRS_spectra.md‎

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---
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jupytext:
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text_representation:
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extension: .md
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format_name: myst
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format_version: 0.13
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jupytext_version: 1.18.1
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kernelspec:
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name: python3
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display_name: python3
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language: python
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---
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# Plot Spitzer IRS Spectra
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## Learning Goals
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By the end of this tutorial, you will be able to :
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- Discover which spectroscopy catalogs are available through IRSA’s Simple Spectral Access (SSA) service
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- Use Irsa.query_ssa() to search for spectra at specific sky positions
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- Retrieve and visualize infrared spectra from the IRSA archive
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- Adapt the workflow shown here to your own science use cases
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## Introduction
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In this tutorial we use a published sample of debris disk host stars to demonstrate how to search for and retrieve infrared spectra using IRSA’s SSA service.
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The targets are drawn from the catalog of debris disks compiled by Mittal et al. (2015, ApJ, 798, 87), which identifies stars exhibiting infrared excesses indicative of circumstellar dust.
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Debris disks trace the remnants of planet formation and are valuable probes of disk evolution, dust composition, and dynamical interactions with planets.
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Infrared spectroscopy is particularly powerful for studying debris disks because many of the diagnostic features of dust grains—such as silicate emission bands—appear at mid‑infrared wavelengths.
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The Spitzer Infrared Spectrograph (IRS) provides sensitive, low‑ and moderate‑resolution spectra in this regime, enabling measurements of dust temperature, composition, and structure.
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Querying the IRSA archive for Spitzer IRS observations allows us to connect published debris‑disk samples with archival spectroscopy and explore these systems in greater physical detail.
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### Instructions
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Feel free to adapt this notebook to your own targets and science goals.
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The functions and overall structure shown here are intended to be reusable for searching IRSA for spectra of interest using SSA.
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### Input
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- A list of sky positions (RA, Dec)
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### Output
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- Tables describing available spectra
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Optional plots of flux versus wavelength
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## Imports
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```{code-cell} ipython3
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# Uncomment the next line to install dependencies if needed.
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# !pip install astropy "astroquery>=0.4.10"
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```
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```{code-cell} ipython3
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import numpy as np
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import matplotlib.pyplot as plt
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from astropy.coordinates import SkyCoord
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import astropy.units as u
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from astropy.table import Table
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from astroquery.vizier import Vizier
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from astroquery.ipac.irsa import Irsa
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import warnings
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#suppress warnings about cache
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warnings.filterwarnings(
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"ignore",
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message="XDG_CACHE_HOME is set",
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category=UserWarning,
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)
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```
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## 1. Exploring SSA catalogs available at IRSA
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Before querying for spectra, it is often useful to see which spectral collections are available through SSA. IRSA hosts spectra from multiple missions and instruments, each grouped into collections.
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```{code-cell} ipython3
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Irsa.list_collections()
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```
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Each entry corresponds to a distinct spectral data collection (for example, Spitzer IRS enhanced products). You can use these collection names with query_ssa(collection=...) to control which archive holdings are searched. Users interested in other instruments or wavelength ranges are encouraged to explore this list and substitute a different collection name below.
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## 2. Define the targets
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We begin by loading a published catalog of debris disk host stars from VizieR.
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```{code-cell} ipython3
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vizier = Vizier() # this instantiates Vizier with its default parameters
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vizier.ROW_LIMIT = 150
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# VizieR catalog identifier for Mittal et al. (2015)
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mittal = "J/ApJ/798/87"
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debris_disks = vizier.get_catalogs(mittal)
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```
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### 2.1 Explore the target catalog
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Let’s inspect the contents of the returned catalog and identify the columns we need.
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```{code-cell} ipython3
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debris_disks
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```
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```{code-cell} ipython3
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#we really want the first table, not the refs table
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debris_disks = debris_disks[0]
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debris_disks
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```
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```{code-cell} ipython3
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debris_disks.colnames
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```
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## 3. Find IRSA spectroscopy
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Lets see if any of these debris disks have spectra in the IRSA archive
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```{code-cell} ipython3
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# IRSA queries require sky coordinates,
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# so we convert the RA and Dec columns into a vectorized SkyCoord object.
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coords = SkyCoord(ra=debris_disks["_RA"],
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dec=debris_disks["_DE"],
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unit=u.deg,
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frame='icrs')
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# Just to make this tutorial run faster, we will limit the number of debris disks
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coords = coords[0:10]
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```
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The function below queries IRSA’s SSA service for Spitzer IRS enhanced spectra near each target position. It optionally plots the retrieved spectra and includes inline comments explaining each step.
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We provide this as a function so it can be easily lifted from this tutorial and used in your own work.
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```{code-cell} ipython3
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def query_and_plot_spectra(positions, *, plot=True, verbose = True):
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"""
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Query IRSA for Spitzer IRS spectra near each target position using SSA.
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Parameters
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----------
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positions : astropy.coordinates.SkyCoord
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Vectorized SkyCoord object containing target positions.
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plot : bool, optional
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If True, generate plots of flux versus wavelength.
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verbose : bool, optional
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If True, print status messages about query results.
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"""
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#for each set of coordinates
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for i, sc in enumerate(positions):
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# Retrieve the target name from the debris disk catalog
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target_name = debris_disks["Name"][i]
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# Query the IRSA SSA service around this position
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result = Irsa.query_ssa(pos=sc,
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radius=5*u.arcsec,
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collection='spitzer_irsenh')
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# Handle cases with no available spectra
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if result is None or len(result) == 0:
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if verbose:
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print(f"No IRS spectra available for target {target_name}")
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continue
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# Let the user know we have a winner
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if verbose:
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print(f"Found {len(result)} spectrum(s) for {target_name}")
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# Loop through each spectrum returned for the object
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for j, row in enumerate(result):
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# Each SSA row includes an access URL pointing to the spectrum
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spectrum_url = row['access_url']
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# Read the spectrum into an Astropy Table
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single_spec = Table.read(spectrum_url, format="ipac")
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# If plotting is enabled, plot the spectrum.
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if plot:
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plt.figure()
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if len(result) > 1:
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label = row['ObsID'] if 'ObsID' in row.colnames else f"Spectrum {j+1}"
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plt.plot(single_spec['wavelength'], single_spec['flux_density'], label=label)
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else:
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plt.plot(single_spec['wavelength'], single_spec['flux_density'])
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plt.xlabel("Wavelength (μm))")
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plt.ylabel("Flux ")
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plt.title(f"Spitzer IRS Spectrum for {target_name}")
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# If more than one spectrum was found, add a legend
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if len(result) > 1:
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plt.legend()
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plt.tight_layout()
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plt.show()
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```
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```{code-cell} ipython3
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query_and_plot_spectra(coords)
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```
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## Acknowledgements
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- [IPAC-IRSA](https://irsa.ipac.caltech.edu/)
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## About this notebook
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**Authors:** IPAC Science Platform Team, including Troy Raen, Brigitta Sipőcz, Jessica Krick, Andreas Faisst, Vandana Desai
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**Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions
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or problems.
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**Updated:** 2026-01-13
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**Runtime:** As of the date above, this notebook takes about 3 minutes to run to completion on a machine with 8GB RAM and 2 CPU. This runtime is
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heavily dependent on archive servers which means runtime will vary for users".)
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**AI:** AI-based tools were used to assist in drafting and refining this tutorial, with all content reviewed and validated by the authors.

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