Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning

doi:10.1093/mnras/stad3047

YNAO OpenIR > 大样本恒星演化研究组

	Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning
	Li JJ(李佳佳)1,2,3 ; Wang JL(王锦良)1,2 ; Ji KF(季凯帆)1,2 ; Liu, Chao 2,4; Chen HL(陈海亮)1,2 ; Han ZW(韩占文)1,2,5 ; Chen XF(陈雪飞)1,2,3,5
发表期刊	Monthly Notices of the Royal Astronomical Society
	2024-01
卷号	527 期号:2 页码:2251-2260
DOI	10.1093/mnras/stad3047
产权排序	第1完成单位
收录类别	SCI ; EI
关键词	(stars:) binaries: general (techniques:) photometric line identification methods: statistical
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) Survey 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.
资助项目	National Natural Science Foundation of China[12125303]; National Natural Science Foundation of China[12288102]; National Natural Science Foundation of China[12090040/3]; National Key R&D Program of China[2021YFA1600403]; Yunnan Fundamental Research Projects[202201BC070003]; International Centre of Supernovae, Yunnan Key Laboratory[202302AN360001]; Yunnan Revitalization Talent Support Programme Science & Technology Champion Project[202305AB350003]; China Manned Space Project[CMS-CSST-2021-A10]
项目资助者	National Natural Science Foundation of China[12125303, 12288102, 12090040/3] ; National Key R&D Program of China[2021YFA1600403] ; Yunnan Fundamental Research Projects[202201BC070003] ; International Centre of Supernovae, Yunnan Key Laboratory[202302AN360001] ; Yunnan Revitalization Talent Support Programme Science & Technology Champion Project[202305AB350003] ; China Manned Space Project[CMS-CSST-2021-A10]
语种	英语
学科领域	天文学 ; 恒星与银河系
文章类型	Article
出版者	OXFORD UNIV PRESS
出版地	GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
ISSN	0035-8711
URL	查看原文
WOS记录号	WOS:001143378500049
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
关键词[WOS]	SUBDWARF-B-STARS ; POPULATION SYNTHESIS ; GALACTIC POPULATION ; COMPACT OBJECTS ; MESA ISOCHRONES ; CCD PHOTOMETRY ; WIDE BINARIES ; FRACTION ; MASS ; MULTIPLICITY
EI入藏号	20234915143672
EI主题词	Efficiency
EI分类号	656.1 Space Flight - 657.2 Extraterrestrial Physics and Stellar Phenomena - 723.4 Artificial Intelligence - 913.1 Production Engineering
引用统计
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.ynao.ac.cn/handle/114a53/26527
专题	大样本恒星演化研究组天文技术实验室
作者单位	1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650011, P. R. China; 2.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, P. R. China; 3.International Centre of Supernovae, Yunnan Key Laboratory, Kunming 650216, P. R. China; 4.Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, P. R. China; 5.Center for Astronomical Mega-Science, Chinese Academy of Sciences, Beijing 100012, P. R. China
第一作者单位	中国科学院云南天文台
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	李佳佳.,王锦良.,季凯帆.,Liu, Chao.,陈海亮.,...&陈雪飞.(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	李佳佳,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.

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