A machine-learning photometric classifier for massive stars in nearby galaxies

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Abstract
Context . Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments. Aims . However, spectral classifications are challenging to obtain in large numbers, especially for distant galaxies. We addressed this by leveraging machine-learning techniques. Methods . We combined Spitzer photometry and Pan-STARRS1 optical data to classify point sources in 26 galaxies within 5 Mpc, and a metallicity range 0.07–1.36 Z ⊙ . Gaia data release 3 (DR3) astrometry was used to remove foreground sources. Classifications are derived using a machine-learning model developed in our previous work. Results . We report classifications for 1 147 650 sources, with 276 657 sources (~24%) being robust. Among these are 120 479 red supergiants (RSGs; ~11%). The classifier performs well even at low metallicities (~0.1 Z ⊙ ) and distances under 1.5 Mpc, with a slight decrease in accuracy beyond ~3 Mpc due to Spitzer ’s resolution limits. We also identified 21 luminous RSGs (log( L/L ⊙ ) ≥ 5.5), 159 dusty yellow hypergiants in M31 and M33, as well as 6 extreme RSGs (log( L/L ⊙ ) ≥ 6) in M31, challenging observed luminosity limits. Class trends with metallicity align with expectations, although biases exist. Conclusions . This catalog serves as a valuable resource for individual-object studies and James Webb Space Telescope target selection. It enables the follow-up on luminous RSGs and yellow hypergiants to refine our understanding of their evolutionary pathways. Additionally, we provide the largest spectroscopically confirmed catalog of extragalactic massive stars and candidates to date, beyond the Clouds, comprising 5273 sources (including ~330 other objects).

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