Abstract :
Although our understanding of stellar evolution has significantly advanced in the last two decades, many questions remain unresolved, particularly regarding the evolutionary pathways of massive stars. This uncertainty has led to a "classification zoo" of stellar types, which despite their distinct characteristics, often share common observables. Do all massive stars follow a similar evolutionary track? How do they transition between phases? These questions are particularly relevant for understanding both present-day stellar evolution and that in the early Universe, where massive stars play a crucial role. Among the key factors that drive the evolution of massive stars is mass loss. Unfortunately, it is not well-understood beyong the main-sequence, especially when experiencing episodic mass loss. A major challenge has been the lack of large, well-classified samples across various galactic environments. To address this, we developed a machine-learning classifier based on multiwavelength photometric and astrometric data from Spitzer, Pan-STARRS1, and Gaia. I will describe the challenges we encountered and the its application to 26 galaxies within 5 Mpc, spannin a metallicity range of 0.07–1.36 Z⊙. We classified over 1.1 million sources, with ~277,000 robust identifications. We explored class trends with metallicity and, although biases exist, we find that they align with expectations. Notable results include the detection of ~160 dusty Yellow Hypergiants, and 6 extreme RSGs (log(L/L⊙) ≥ 6), pushing known luminosity boundaries. A key outcome of this work is the largest spectroscopically confirmed catalog of massive stars and candidates to date, comprising 5,273 sources (including ∼ 330 other objects). This work provides the largest catalog of classified massive stars to date and offers a powerful tool for studying mass-loss processes and selecting follow-up targets, especially with next-generation observatories like JWST.
