AI 中文总结
该研究开发了一种卷积神经网络分类器，可在APOGEE和BOSS光谱中识别年龄小于40 Myr的年轻恒星，能有效区分Teff和log(g)相近的主序前恒星与演化恒星，减少YSO候选体的污染。
AI 中文摘要
我们开发了一种卷积神经网络分类器，用于在APOGEE光谱中对年龄小于40百万年的年轻恒星进行光谱识别。该分类器对多种与恒星年轻相关的特征敏感，包括在年轻恒星中常见的旋转展宽，以及多条似乎表明存在大量星斑光球的离散谱线。该模型成功地在广泛的恒星范围内识别出年轻恒星，在区分年轻的M型和K型矮星时表现出最强的性能，同时对更热的恒星也保持了有用的区分能力。这项工作即使在主序前恒星与演化程度更高的恒星具有相近的有效温度（Teff）和表面重力对数（log(g)）时，也能更稳健地将它们与后者区分开，为大幅减少测光选择的年轻恒星天体（YSO）候选体中的污染提供了可靠手段。除了构建用于APOGEE光谱的分类器外，我们还对光学BOSS光谱中的年轻恒星进行了分类。
英文摘要
We develop a convolutional neural network classifier that performs spectroscopic identification of stellar youth (<40 Myr) in APOGEE spectra. This classifier is sensitive to several youth-related features, including rotational broadening, which is common in younger stars, and to several discrete lines that appear to be indicative of a very spotted photosphere. The model is successful at identifying youth across a wide range of stars, achieving its strongest performance at discriminating young M and K dwarfs, while maintaining useful discriminatory power for hotter stars as well. This work enables more robust separation of pre-main-sequence stars from more evolved sources in the field even when they have comparable Teff and log(g), providing a reliable means to substantially reduce contamination in photometrically selected YSO candidates. In addition to constructing a classifier for APOGEE spectra, we also perform classification of young stars in optical BOSS spectra.
Comments13 pages, 7 figures, 1 figure set, to be published in the Astronomical Journal
