A machine learning approach for identification and classification of symbiotic stars using 2MASS and WISE
A machine learning approach for identification and classification of symbiotic stars using 2MASS and WISE
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DOI：
10.1093/mnras/sty3359
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发表时间：
2018-12
影响因子：
4.8
通讯作者：
S. Akras;M. Leal-Ferreira;L. Guzman-Ramirez;G. Ramos-Larios
中科院分区：
文献类型：
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作者：
S. Akras;M. Leal-Ferreira;L. Guzman-Ramirez;G. Ramos-Larios
在基于最新共生星（SySts）目录的系列论文中的第二篇论文中，我们提出了一种使用机器学习算法（例如分类树）在光度测量中识别和区分SySts与其他Halpha发射体的新方法，线性判别分析和K-最近邻。这项工作背后的动机是在2 MASS和WISE调查所涵盖的近红外和中红外区域内寻找可能的颜色指数。一些诊断色-色图生成的所有已知的银河系SySts和几类恒星的对象，模仿SySts，如行星状星云，后AGB，米拉，单K和M巨星，激变变量，Be，AeBe，YSO，弱和经典的金牛T星，沃尔夫-拉叶。分类树算法揭示，主要是J-H，W1-W 4和Ks-W3，其次是H-W2，W1-W2和W3-W 4是识别SySts的理想颜色指标。线性判别分析方法也适用于确定2 MASS和AllWISE幅度的线性组合，更好地区分SySts。使用LDA分量上的K-最近邻方法确定源是SySt的概率。通过将我们的分类树模型应用于候选SySts列表（论文I），候选SySts的IPHAS列表和DR 2 VPHAS+目录，我们找到了125个（72个新候选者）通过我们的标准的来源，同时我们还恢复了90%的已知银河系SySts。
In this second paper in a series of papers based on the most-up-to-date catalogue of symbiotic stars (SySts), we present a new approach for identifying and distinguishing SySts from other Halpha emitters in photometric surveys using machine learning algorithms such as classification tree, linear discriminant analysis, and K-nearest neighbour. The motivation behind of this work is to seek for possible colour indices in the regime of near- and mid-infrared covered by the 2MASS and WISE surveys. A number of diagnostic colour-colour diagrams are generated for all the known Galactic SySts and several classes of stellar objects that mimic SySts such as planetary nebulae, post-AGB, Mira, single K and M giants, cataclysmic variables, Be, AeBe, YSO, weak and classical T Tauri stars, and Wolf-Rayet. The classification tree algorithm unveils that primarily J-H, W1-W4 and Ks-W3 and secondarily H-W2, W1-W2 and W3-W4 are ideal colour indices to identify SySts. Linear discriminant analysis method is also applied to determine the linear combination of 2MASS and AllWISE magnitudes that better distinguish SySts. The probability of a source being a SySt is determined using the K-nearest neighbour method on the LDA components. By applying our classification tree model to the list of candidate SySts (Paper I), the IPHAS list of candidate SySts, and the DR2 VPHAS+ catalogue, we find 125 (72 new candidates) sources that pass our criteria while we also recover 90 per cent of the known Galactic SySts.