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

This is actually a preview of what will follow after the first paper of the machine-learning classifier. We put it into action to get predictions for a number of galaxies and we start exploring the results. Of more interest is the fractions of the populations with metallicity, although a more detailed study is needed to take care of all caveats.

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

Grigoris Maravelias, Alceste Z. Bonanos, Frank Tramper, Stephan de Wit, Ming Yang, Paolo Bonfini, Emmanuel Zapartas, Konstantinos Antoniadis, Evangelia Christodoulou, Gonzalo Muñoz-Sanchez

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 ∼3 Z⊙). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

The fractions, of the predicted class members over the total sample size for each galaxy, with metallicity.

arXiv: 2209.06303

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