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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*
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
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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.

Type
Contributed Paper
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]

References

Bonanos, A. Z., et al. 2009, AJ, 138, 1003 CrossRefGoogle Scholar
Bonanos, A. Z., et al. 2022 Google Scholar
Crowther, P. A., & Walborn, N. R. 2011, MNRAS, 416, 1311 CrossRefGoogle Scholar
Davidson, K. 2020, Galaxies, 8, 10 Google Scholar
de Jager, C. 1998, A&A Rev., 8, 145 Google Scholar
de Mink, S. E., Sana, H., Langer, N., Izzard, R. G., & Schneider, F. R. N. 2014, ApJ, 782, 7 CrossRefGoogle Scholar
de Wit, S., Bonanos, A. Z., Tramper, F., Yang, M., Maravelias, G., Boutsia, K., Britavskiy, N., & Zapartas, M. 2022 Google Scholar
Dorn-Wallenstein, T. Z., Davenport, J. R. A., Huppenkothen, D., & Levesque, E. M. 2021, ApJ, 913, 32 Google Scholar
Gal-Yam, A. 2012, Science, 337, 927 Google Scholar
Kobulnicky, H. A., et al. 2014, ApJS, 213, 34 CrossRefGoogle Scholar
Kraus, M. 2019, Galaxies, 7, 83 Google Scholar
Levesque, E. M. 2017, Astrophysics of Red SupergiantsCrossRefGoogle Scholar
Lorenzo, M., Garcia, M., Najarro, F., Herrero, A., Cerviño, M., & Castro, N. 2022, arXiv e-prints, arXiv:2207.09700Google Scholar
Maravelias, G., Bonanos, A. Z., Tramper, F., de Wit, S., Yang, M., & Bonfini, P. 2022, arXiv e-prints, arXiv:2203.08125Google Scholar
Massey, P. 2002, ApJS, 141, 81 Google Scholar
Munoz-Sanchez, G., Maravelias, G., Bonanos, A. Z., Tramper, F., de Wit, S., & Yang, M. 2022 Google Scholar
Neill, J. D., et al. 2011, ApJ, 727, 15 Google Scholar
Ren, Y., Jiang, B., Yang, M., Wang, T., Jian, M., & Ren, T. 2021, ApJ, 907, 18 CrossRefGoogle Scholar
Sana, H., et al. 2014, ApJS, 215, 15 CrossRefGoogle Scholar
Smith, N. 2014, ARA&A, 52, 487 Google Scholar
Vink, J. S. 2021, arXiv e-prints, arXiv:2109.08164Google Scholar