An Enhanced Sample of Galactic Red Supergiants Reveals Spiral Structures

TL;DR

This study develops a neural network classifier to identify Galactic Red Supergiants using Gaia spectra, resulting in a catalog of 2,436 RSGs that effectively trace the Milky Way's spiral arms and structure.

Contribution

The paper introduces a novel machine learning approach to identify RSGs from Gaia spectra, significantly expanding the sample for Galactic structure studies.

Findings

Identified 2,436 high-confidence RSGs in the Milky Way.

RSGs trace Galactic spiral arms effectively.

Classification method shows high reliability and correlation with known structures.

Abstract

Red supergiants (RSGs), representing a kind of massive young stellar population, have rarely been used to probe the structure of the Milky Way, mainly due to the long-standing scarcity of Galactic RSG samples. The Gaia BP/RP spectra (hereafter XP), which cover a broad wavelength range, provide a powerful tool for identifying RSGs. In this work, we develop a feedforward neural network classifier that assigns to each XP spectrum a probability of being an RSG, denoted as $\mathrm{P(RSG)}$. We perform ten independent runs with randomly divided training and validation sets, and apply each run to all XP spectra of stars with $G < 12$ mag. By selecting sources with $\mathrm{P(RSG)} \geq 0.9$, ten high-confidence candidate samples are obtained. A star is considered a ture Galactic RSG only if it appears in at least eight of these samples, yielding a final catalog of 2,436 objects. These RSGs show a clear spatial correlation with OB stars and trace the Galactic spiral arms well, confirming the reliability of our classification, and highlighting their potential to serve as powerful tracers of the Milky Way's structure.