Using machine learning to investigate the populations of dusty evolved stars in various metallicities
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866913580555698176 |
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| author | Maravelias, Grigoris Bonanos, Alceste Z. Tramper, Frank de Wit, Stephan Yang, Ming Bonfini, Paolo Zapartas, Emmanuel Antoniadis, Konstantinos Christodoulou, Evangelia Muñoz-Sanchez, Gonzalo |
| author_facet | Maravelias, Grigoris Bonanos, Alceste Z. Tramper, Frank de Wit, Stephan Yang, Ming Bonfini, Paolo Zapartas, Emmanuel Antoniadis, Konstantinos Christodoulou, Evangelia Muñoz-Sanchez, Gonzalo |
| contents | 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 ($1/15$ to $\sim3~Z_{\odot}$). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_06303 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Using machine learning to investigate the populations of dusty evolved stars in various metallicities Maravelias, Grigoris Bonanos, Alceste Z. Tramper, Frank de Wit, Stephan Yang, Ming Bonfini, Paolo Zapartas, Emmanuel Antoniadis, Konstantinos Christodoulou, Evangelia Muñoz-Sanchez, Gonzalo Astrophysics of Galaxies Solar and Stellar Astrophysics 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 ($1/15$ to $\sim3~Z_{\odot}$). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity. |
| title | Using machine learning to investigate the populations of dusty evolved stars in various metallicities |
| topic | Astrophysics of Galaxies Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2209.06303 |