Title: A machine-learning photometric classifier for massive stars in nearby galaxies II. The catalog Show Chinese title

Title: 银河系附近大质量恒星的机器学习测光分类器。II. 目录

Abstract: 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 \textit{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$_\odot$. \textit{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 ($\sim24\%$) being robust. Among these are 120,479 Red Supergiants (RSGs; $\sim11\%$). The classifier performs well even at low metallicities ($\sim0.1$ Z$_\odot$) and distances under 1.5 Mpc, with a slight decrease in accuracy beyond $\sim3$ Mpc due to \textit{Spitzer}'s resolution limits. We also identified 21 luminous RSGs ($\textrm{log}(L/L_\odot)\ge5.5$), 159 dusty Yellow Hypergiants in M31 and M33, as well as 6 extreme RSGs ($\textrm{log}(L/L_\odot)\ge6$) 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 \textit{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 $\sim330$ other objects). Show Chinese abstract

Abstract: 质量损失是恒星演化的一个关键方面，特别是在演化的大质量恒星中，但间歇性质量损失仍然知之甚少。为了研究这一问题，我们需要各种银河环境中演化的巨大恒星群体。然而，大量获得光谱分类具有挑战性，尤其是对于遥远的星系。我们通过利用机器学习技术解决了这个问题。我们将\textit{斯皮策}光度学和 Pan-STARRS1 光学数据相结合，用于分类 5 兆秒差距内 26 个星系中的点源，并且金属丰度范围为 0.07-1.36 Z$_\odot$。使用由 Maravelias 等人（2022）开发的机器学习模型，并结合\textit{盖亚}DR3 天文测量数据去除了前景源。我们使用 Maravelias 等人开发的机器学习模型来得出分类结果。我们报告了 1,147,650 个源的分类，其中 276,657 个源（$\sim24\%$）是可靠的。其中包括 120,479 颗红超巨星（RSGs；$\sim11\%$）。 分类器即使在金属丰度较低（$\sim0.1$Z$_\odot$）和距离小于1.5 Mpc的情况下也表现良好，在超过$\sim3$Mpc时由于\textit{斯皮策}的分辨率限制，准确率略有下降。 我们还确定了21颗明亮的红超巨星（$\textrm{log}(L/L_\odot)\ge5.5$）、159颗M31和M33中的尘埃包裹黄超巨星，以及M31中的6颗极端红超巨星（$\textrm{log}(L/L_\odot)\ge6$），这些恒星挑战了观测到的光度极限。 金属丰度与分类趋势与预期一致，尽管存在偏差。 该目录为单个目标的研究和\textit{詹姆斯·韦布}空间望远镜的目标选择提供了宝贵的资源。 它使我们能够进一步研究明亮的红超巨星和黄超巨星，以完善对其演化路径的理解。 此外，我们提供了迄今为止最大的具有光谱确认的 massive 星星和候选星星表，包括 5,273 个源（包括 $\sim330$ 其他对象）。