Stellar ages play a crucial role in understanding the formation and evolution of stars and Galaxies, which pose many challenges while determining in practice. In this paper, we have introduced a new machine-learning method, Gaussian process regression (GPR), to estimate the stellar ages, which is different from the traditional isochrone fitting method, which fully utilizes the information provided by previous studies. To demonstrate the performance of our method, we have applied it to the field stars of two important phases of evolution, main-sequence turn-off (MSTO) stars and giants, whose ages and masses are available in the literature. Also, GPR is applied to the red giants of open clusters (e.g., M67). Results showed that the ages given by GPR are in better agreement with those given by isochrone fitting methods. The ages are also estimated from various other machine-learning methods (e.g., support vector regression, neural networks, and random forest) and are compared with ...

中文翻译：

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使用高斯过程回归估计恒星的年龄和质量

恒星年龄在理解恒星和星系的形成和演化中起着至关重要的作用，这在实践中会带来很多挑战。在本文中，我们引入了一种新的机器学习方法，即高斯过程回归（GPR），以估计星体年龄，这与传统的等时线拟合方法不同，后者充分利用了先前研究提供的信息。为了证明我们方法的性能，我们将其应用于演化的两个重要阶段的野外恒星，即主序关闭（MSTO）恒星和巨星，它们的年龄和质量在文献中都可以找到。此外，GPR应用于开放星团的红色巨人（例如M67）。结果表明，GPR给出的年龄与等时线拟合方法给出的年龄更加吻合。