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Mass loss is a key property to understand stellar evolution and in particularfor low-metallicity environments. Our knowledge has improved dramatically overthe last decades both for single and binary evolutionary models. However,episodic mass loss although definitely present observationally, is not includedin the models, while its role is currently undetermined. A major hindrance isthe lack of large enough samples of classified stars. We attempted to addressthis by applying an ensemble machine-learning approach using color indices(from IR/Spitzer and optical/Pan-STARRS photometry) as features and combiningthe probabilities from three different algorithms. We trained on M31 and M33sources with known spectral classification, which we grouped intoBlue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayetand background galaxies/AGNs. We then applied the classifier to about onemillion Spitzer point sources from 25 nearby galaxies, spanning a range ofmetallicites ($1/15$ to $\sim3~Z_{\odot}$). Equipped with spectralclassifications we investigated the occurrence of these populations withmetallicity.

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