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tutorials/spitzer/plot_Spitzer_IRS_spectra.md

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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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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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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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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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```
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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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Before querying for spectra, it is often useful to see which spectral collections are available through SSA.
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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(servicetype='ssa')
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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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Each entry corresponds to a distinct spectral data collection (for example, Spitzer IRS enhanced products).
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You can use these collection names with `query_ssa(collection=...)` to control which archive holdings are searched.
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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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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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The function below queries IRSA’s SSA service for Spitzer IRS enhanced spectra near each target position.
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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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**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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**Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions 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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**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.
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This runtime is 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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