Finally…after some years from the initial paper on the method I managed to put everything together to publish the second part of the machine-learning classifier for massive stars in nearby galaxies. This work is actually the application of the classifier to the whole sample of 26 galaxies and about 1.1 million sources. One of the most challenging tasks was to collect all know sources with spectroscopic classification from literature. For this we had to check definitely 100+ papers to build the most complete (and robust) reference catalog for massive stars to date! Moreover, I believe that this preliminary study of how the fractions of the various populations relate with metallicity can uncover valuable information regarding the evolution of massive stars and their populations in different galactic environments. However, more thorough research is needed to address all existing biases.
That is the end of an amazing journey that started back in 2018! Many thanks to Alceste for trusting me on this project and to all of my colleagues that helped me in this endeavor.
A machine-learning photometric classifier for massive stars in nearby galaxies II. The catalog
G. Maravelias, A. Z. Bonanos, K. Antoniadis, G. Munoz-Sanchez, E. Christodoulou, S. de Wit, E. Zapartas, K. Kovlakas, F. Tramper, P. Bonfini, S. Avgousti
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. However, spectral classifications are challenging to obtain in large numbers, especially for distant galaxies. We addressed this by leveraging machine-learning techniques. 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 DR3 astrometry was used to remove foreground sources. Classifications are derived using a machine-learning model developed by Maravelias et al. (2022). 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, though biases exist. This catalog serves as a valuable resource for individual-object studies and James Webb Space Telescope target selection. It enables 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 massive stars and candidates to date, comprising 5,273 sources (including ∼330 other objects).
arXiv: 2504.01232 (subm to A&A)