Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning
文献类型:期刊论文
作者 | Li JJ(李佳佳)1,4,5; Wang JL(王锦良)1,4; Ji KF(季凯帆)1,4; Liu, Chao2,4; Chen HL(陈海亮)1,4; Han ZW(韩占文)1,3,4; Chen XF(陈雪飞)1,3,4,5 |
刊名 | Monthly Notices of the Royal Astronomical Society |
出版日期 | 2024 |
卷号 | 527期号:2页码:2251-2260 |
ISSN号 | 0035-8711 |
关键词 | (stars:) binaries: general (techniques:) photometric line identification methods: statistical |
DOI | 10.1093/mnras/stad3047 |
产权排序 | 第1完成单位 |
文献子类 | Journal article (JA) |
英文摘要 | The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a method to differentiate single MS stars from double MS binaries from the Chinese Space Station Telescope (CSST) Surv e y with machine learning. This method is reliable and efficient to identify binaries with mass ratios between 0.20 and 0.80, which is independent of the mass ratio distribution. But the number of binaries identified with this method is not a good approximation to the number of binaries in the original sample due to the low detection efficiency of binaries with mass ratios smaller than 0.20 or larger than 0.80. Therefore, we have improved this point by using the detection efficiencies of our method and an empirical mass ratio distribution and then can infer the binary fraction in the sample. Once the CSST data are available, we can identify MS binaries with our trained multi-layer perceptron model and derive the binary fraction of the sample. © 2023 The Author(s). |
学科主题 | 天文学 |
URL标识 | 查看原文 |
出版地 | GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND |
WOS关键词 | SUBDWARF-B-STARS ; POPULATION SYNTHESIS ; GALACTIC POPULATION ; COMPACT OBJECTS ; MESA ISOCHRONES ; CCD PHOTOMETRY ; WIDE BINARIES ; FRACTION ; MASS ; MULTIPLICITY |
资助项目 | National Key Research and Development Program of China[2021YFA1600403] |
WOS研究方向 | Astronomy & Astrophysics |
语种 | 英语 |
出版者 | OXFORD UNIV PRESS |
WOS记录号 | WOS:001143378500049 |
资助机构 | National Key Research and Development Program of China[2021YFA1600403] |
版本 | 出版稿 |
源URL | [http://ir.ynao.ac.cn/handle/114a53/26527] |
专题 | 云南天文台_大样本恒星演化研究组 天文技术实验室 |
通讯作者 | Li JJ(李佳佳); Chen XF(陈雪飞) |
作者单位 | 1.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650011, China; 2.Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, China; 3.Center for Astronomical Mega-Science, Chinese Academy of Sciences, Beijing, 100012, China; 4.School of Astronomy and Space Science, University of Chinese, Academy of Sciences, Beijing, 100049, China; 5.International Centre of Superno vae, Yunnan Key Laboratory, Kunming, 650216, China |
推荐引用方式 GB/T 7714 | Li JJ,Wang JL,Ji KF,et al. Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning[J]. Monthly Notices of the Royal Astronomical Society,2024,527(2):2251-2260. |
APA | Li JJ.,Wang JL.,Ji KF.,Liu, Chao.,Chen HL.,...&Chen XF.(2024).Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning.Monthly Notices of the Royal Astronomical Society,527(2),2251-2260. |
MLA | Li JJ,et al."Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning".Monthly Notices of the Royal Astronomical Society 527.2(2024):2251-2260. |
入库方式: OAI收割
来源:云南天文台
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