Machine-learning Regression of Stellar Effective Temperatures in the Second Gaia Data Release
文献类型：期刊论文
| 作者 | Bai,Yu1 ; Liu,JiFeng1,2; Bai,ZhongRui1; Wang,Song1; Fan,DongWei3
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| 刊名 | The Astronomical Journal
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| 出版日期 | 2019-08-02 |
| 卷号 | 158期号:2 |
| 关键词 | methods: data analysis stars: fundamental parameters techniques: spectroscopic |
| ISSN号 | 0004-6256 |
| DOI | 10.3847/1538-3881/ab3048 |
| 英文摘要 | Abstract This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second Gaia data release, based on the combination of the stars in four spectroscopic surveys: the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for Galactic Understanding and Exploration, the Apache Point Observatory Galactic Evolution Experiment, and the Radial Velocity Extension. This combination, of about four million stars, enables us to construct one of the largest training samples for the regression and further predict reliable stellar temperatures with a rms error of 191 K. This result is more precise than that given by the Gaia second data release that is based on about sixty thousands stars. After a series of data cleaning processes, the input features that feed the regressor are carefully selected from the Gaia parameters, including the colors, the 3D position, and the proper motion. These Gaia parameters are used to predict effective temperatures for 132,739,323 valid stars in the second Gaia data release. We also present a new method for blind tests and a test for external regression without additional data. The machine-learning algorithm fed with the parameters only in one catalog provides us with an effective approach to maximize the sample size for prediction, and this methodology has a wide application prospect in future studies of astrophysics. |
| 语种 | 英语 |
| WOS记录号 | IOP:0004-6256-158-2-AB3048 |
| 出版者 | The American Astronomical Society |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/27181]  |
| 专题 | 中国科学院国家天文台 |
| 作者单位 | 1.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang Distict, Beijing 100012, People’s Republic of China; ybai@nao.cas.cn 2.College of Astronomy and Space Sciences, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China 3.National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang Distict, Beijing 100012, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | Bai,Yu,Liu,JiFeng,Bai,ZhongRui,et al. Machine-learning Regression of Stellar Effective Temperatures in the Second Gaia Data Release[J]. The Astronomical Journal,2019,158(2). |
| APA | Bai,Yu,Liu,JiFeng,Bai,ZhongRui,Wang,Song,&Fan,DongWei.(2019).Machine-learning Regression of Stellar Effective Temperatures in the Second Gaia Data Release.The Astronomical Journal,158(2). |
| MLA | Bai,Yu,et al."Machine-learning Regression of Stellar Effective Temperatures in the Second Gaia Data Release".The Astronomical Journal 158.2(2019). |
入库方式： OAI收割
来源：国家天文台
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