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

TL;DR

This study employs ensemble machine learning on infrared and optical data to classify dusty evolved stars across various metallicities, enhancing understanding of mass loss in stellar evolution.

Contribution

It introduces a novel machine learning approach to classify large samples of dusty evolved stars across different metallicities, addressing previous sample size limitations.

Findings

Classified about one million stars across 25 galaxies.

Analyzed the distribution of stellar populations with metallicity.

Provided insights into episodic mass loss in low-metallicity environments.

Abstract

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.