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Spectral identification and classification of dusty stellar sources using spectroscopic and multiwavelength observations through machine learning

Published online by Cambridge University Press:  01 August 2025

Sepideh Ghaziasgar Open the ORCID record for Sepideh Ghaziasgar [Opens in a new window] ,
Amirhossein Masoudnezhad ,
Atefeh Javadi Open the ORCID record for Atefeh Javadi [Opens in a new window] ,
Jacco Th. van Loon ,
Habib G. Khosroshahi  and
Negin Khosravaninezhad Open the ORCID record for Negin Khosravaninezhad [Opens in a new window]
Sepideh Ghaziasgar*
Affiliation:
School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran
Amirhossein Masoudnezhad
Affiliation:
Department of Physics, Sharif University of Technology, P.O. Box 11155-9161, Tehran, Iran
Atefeh Javadi
Affiliation:
School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran
Jacco Th. van Loon
Affiliation:
Astrophysics Group, Lennard-Jones Laboratories, Keele University, ST5 5BG, UK
Habib G. Khosroshahi
Affiliation:
School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran
Negin Khosravaninezhad
Affiliation:
Department of Physics, Sharif University of Technology, P.O. Box 11155-9161, Tehran, Iran Department of Physics and Astronomy, University of California Riverside, CA 92521, USA
*
email: sepide.ghaziasgar@gmail.com
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Abstract

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected over the IR sky. Spectroscopic data is typically used to identify specific infrared sources. However, our goal is to determine how well these sources can be identified using multiwavelength data. Consequently, we developed a robust training set of spectra of confirmed sources from the Large and Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-Spec Spitzer Infrared Spectrograph (IRS) spectral catalogs. Subsequently, we applied various learning classifiers to distinguish stellar subcategories comprising young stellar objects (YSOs), C-rich asymptotic giant branch (CAGB), O-rich AGB stars (OAGB), Red supergiant (RSG), and post-AGB stars. We have classified around 700 counts of these sources. It should be highlighted that despite utilizing the limited spectroscopic data we trained, the accuracy and models’ learning curve provided outstanding results for some of the models. Therefore, the Support Vector Classifier (SVC) is the most accurate classifier for this limited dataset.

Keywords

stars: classificationstars: AGB and post-AGBstars: dustgalaxies: spectroscopic cataloggalaxies: multiwavelength observationsmethods: data analysis

Information

Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 67 - 72
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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