论文标题
升线网：低质量和高质量恒星的扩展光谱模型
APOGEE Net: An expanded spectral model of both low mass and high mass stars
论文作者
论文摘要
我们训练一个卷积神经网络Apogee Net，以预测$ t_ \ mathrm {eff} $，$ \ log g $，对于某些恒星，[fe/h]，基于Apogee Spectra。这是第一个适用于这些数据的管道，它能够以自吻的方式估算这些参数，不仅是低质量恒星（例如主要序列矮人，矮人，前序列序列星和红色巨人），而且还具有$ t_ \ t_ \ t_ \ mathrm {eff} $ 50,000 k中的高质量恒星，包括超过50,000 k，包括热蓝色的蓝色蓝色和蓝色的蓝色Suppergiant。本文介绍的约65万星的目录允许对恒星形成的历史不仅是银河系的历史，而且还构成了麦芽云的云，因为可以通过$ t_ \ nod fipere在$ t_ \ nod of cate中更清洁地选择了这些星系中不同部分的不同类型的物体。
We train a convolutional neural network, APOGEE Net, to predict $T_\mathrm{eff}$, $\log g$, and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these parameters in a self-consistent manner not only for low mass stars, (such as main sequence dwarfs, pre-main sequence stars, and red giants), but also high mass stars with $T_\mathrm{eff}$ in excess of 50,000 K, including hot dwarfs and blue supergiants. The catalog of ~650,000 stars presented in this paper allows for a detailed investigation of the star forming history of not just the Milky Way, but also of the Magellanic clouds, as different type of objects tracing different parts of these galaxies can be more cleanly selected through their distinct placement in $T_\mathrm{eff}$-$\log g$ parameter space than in previous APOGEE catalogs produced through different pipelines.
