论文标题
GALAH调查：使用机器学习分类算法的新样本的极具金属贫困星的新样本
The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm
论文作者
论文摘要
极为金属贫困（EMP）的恒星提供了以银河系早期化学富集的宝贵探测。在这里，我们利用Galah调查的大量$ \ sim600,000 $高分辨率高分辨率恒星光谱以及机器学习算法找到54个候选人，其中估计[Fe/h]〜$ \ leq $〜-3.0，6，其中6个具有[Fe/H]我们的样本包括$ \ sim 20 \％$主序列EMP候选者，\ EMP调查异常高。我们发现样品的幅度限制金属性分布函数与以前使用更复杂的选择标准的工作一致。我们提出的方法具有在下一代大规模恒星光谱调查中应用的显着潜力，该调查将使可用的光谱数据很好地扩展到数百万恒星中。
Extremely Metal-Poor (EMP) stars provide a valuable probe of early chemical enrichment in the Milky Way. Here we leverage a large sample of $\sim600,000$ high-resolution stellar spectra from the GALAH survey plus a machine learning algorithm to find 54 candidates with estimated [Fe/H]~$\leq$~-3.0, 6 of which have [Fe/H]~$\leq$~-3.5. Our sample includes $\sim 20 \%$ main sequence EMP candidates, unusually high for \emp surveys. We find the magnitude-limited metallicity distribution function of our sample is consistent with previous work that used more complex selection criteria. The method we present has significant potential for application to the next generation of massive stellar spectroscopic surveys, which will expand the available spectroscopic data well into the millions of stars.
