论文标题
识别狼疮恒星形成区域中年轻恒星对象的随机森林方法
A Random Forest Approach to Identifying Young Stellar Object Candidates in the Lupus Star-Forming Region
论文作者
论文摘要
恒星形成区域内恒星成员的识别和表征对于恒星形成的许多方面至关重要，包括对初始质量函数，室外磁盘的演变和恒星形成历史的形式化。先前对狼疮形成区域的调查已经通过红外过量和积聚签名确定成员。我们使用机器学习来基于两个空间观察者的调查来识别狼疮的新候选成员：ESA的Gaia和NASA的Spitzer。来自Gaia的数据版本2的星体测量以及来自Spitzer空间望远镜上的红外阵列摄像头（IRAC）以及其他调查的天文和光度数据，并将其编译为随机森林（RF）分类器的目录中的目录。测试了RF分类器，以找到最佳功能，会员列表，非会员身份识别方案，插补方法，训练集班级加权和处理数据中类不平衡的方法。我们列出了狼疮恒星形成区域的27名候选人，以进行光谱随访。大多数候选人都位于云V和VI中，那里只有一名确认的狼疮成员。这些云可能代表了较老的恒星形成人群。
The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation history. Previous surveys of the Lupus star-forming region have identified members through infrared excess and accretion signatures. We use machine learning to identify new candidate members of Lupus based on surveys from two space-based observatories: ESA's Gaia and NASA's Spitzer. Astrometric measurements from Gaia's Data Release 2 and astrometric and photometric data from the Infrared Array Camera (IRAC) on the Spitzer Space Telescope, as well as from other surveys, are compiled into a catalog for the Random Forest (RF) classifier. The RF classifiers are tested to find the best features, membership list, non-membership identification scheme, imputation method, training set class weighting and method of dealing with class imbalance within the data. We list 27 candidate members of the Lupus star-forming region for spectroscopic follow-up. Most of the candidates lie in Clouds V and VI, where only one confirmed member of Lupus was previously known. These clouds likely represent a slightly older population of star-formation.
