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Using machine learning to investigate the populations of dusty evolved stars in various metallicities

Published online by Cambridge University Press:  29 August 2024

Grigoris Maravelias Open the ORCID record for Grigoris Maravelias [Opens in a new window] ,
Alceste Z. Bonanos Open the ORCID record for Alceste Z. Bonanos [Opens in a new window] ,
Frank Tramper Open the ORCID record for Frank Tramper [Opens in a new window] ,
Stephan de Wit Open the ORCID record for Stephan de Wit [Opens in a new window] ,
Ming Yang ,
Paolo Bonfini ,
Emmanuel Zapartas ,
Konstantinos Antoniadis ,
Evangelia Christodoulou  and
Gonzalo Muñoz-Sanchez Open the ORCID record for Gonzalo Muñoz-Sanchez [Opens in a new window]
Grigoris Maravelias*
Affiliation:
IAASARS, National Observatory of Athens, Greece Institute of Astrophysics, FORTH, Greece
Alceste Z. Bonanos
Affiliation:
IAASARS, National Observatory of Athens, Greece
Frank Tramper
Affiliation:
Institute of Astronomy, KU Leuven, Belgium
Stephan de Wit
Affiliation:
IAASARS, National Observatory of Athens, Greece Department of Physics, National and Kapodistrian University of Athens, Greece
Ming Yang
Affiliation:
IAASARS, National Observatory of Athens, Greece Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, People’s Republic of China
Paolo Bonfini
Affiliation:
Ballista Technology Group, Florida, USA
Emmanuel Zapartas
Affiliation:
IAASARS, National Observatory of Athens, Greece
Konstantinos Antoniadis
Affiliation:
IAASARS, National Observatory of Athens, Greece Department of Physics, National and Kapodistrian University of Athens, Greece
Evangelia Christodoulou
Affiliation:
IAASARS, National Observatory of Athens, Greece Department of Physics, National and Kapodistrian University of Athens, Greece
Gonzalo Muñoz-Sanchez
Affiliation:
IAASARS, National Observatory of Athens, Greece Department of Physics, National and Kapodistrian University of Athens, Greece
*
email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

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Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic mass loss although definitely present observationally, is not included in the models, while its role is currently undetermined. A major hindrance is the lack of large enough samples of classified stars. We attempted to address this by applying an ensemble machine-learning approach using color indices (from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining the probabilities from three different algorithms. We trained on M31 and M33 sources with known spectral classification, which we grouped into Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet and background galaxies/AGNs. We then applied the classifier to about one million Spitzer point sources from 25 nearby galaxies, spanning a range of metallicites (). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

Keywords

Stars: massiveStars mass-lossStars: evolutionGalaxies: individual WLM, M31, IC 1613, M33, Sextans AMethods: statistical
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 18 , Symposium S361: Massive Stars Near and Far , May 2022 , pp. 454 - 459
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Astronomical Union

Footnotes

†

contact: [email protected]

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