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Carbon Stars Identified from LAMOST DR4 Using Machine Learning
Li, Yin-Bi1; Luo, A-Li1; Du, Chang-De1,2,3; Zuo, Fang1; Wang, Meng-Xin1,2; Zhao, Gang1; Jiang, Bi-Wei4; Zhang, Hua-Wei5; Liu, Chao1; Qin, Li1,2; Wang, Rui1,2; Du, Bing1,2; Guo, Yan-Xin1,2; Wang B(王博)6; Han ZW(韩占文)6; Xiang, Mao-Sheng1,9; Huang, Yang7; Chen, Bing-Qiu7; Chen, Jian-Jun1; Kong, Xiao1,2; Hou, Wen1; Song, Yi-Han1; Wang, You-Fen1; Wu, Ke-Fei1,2; Zhang, Jian-Nan1; Zhang, Yong8; Wang, Yue-Fei8; Cao, Zi-Huang1; Hou, Yong-Hui8; Zhao, Yong-Heng1; Li, Yin-Bi(Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China)
发表期刊ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
2018-02-01
卷号234期号:2
DOI10.3847/1538-4365/aaa415
产权排序第6完成单位
收录类别SCI
关键词Material: Machine-readable Table
摘要In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find these stars from more than 7 million spectra. As a by-product, 17 carbon-enhanced metal-poor turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes: 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes, C-J(H), C-J(R), and C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Besides spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and that they are likely to be in binary systems with compact white dwarf companions.
项目资助者National Natural Science Foundation of China(11303036, 11390371/4) ; Special Funding for Advanced Users ; National Basic Research Program of China (973 Program)(2014CB845700) ; National Development and Reform Commission
语种英语
学科领域天文学
文章类型Article
ISSN0067-0049
URL查看原文
WOS记录号WOS:000424258800002
WOS研究方向Astronomy & Astrophysics
WOS类目Astronomy & Astrophysics
关键词[WOS]HIGH GALACTIC LATITUDES ; LOW METAL ABUNDANCE ; DIGITAL SKY SURVEY ; CH STARS ; BINARY NATURE ; BARIUM STARS ; SURVEY 2MASS ; POOR STARS ; TELESCOPE ; CATALOG
引用统计
文献类型期刊论文
条目标识符http://ir.ynao.ac.cn/handle/114a53/12134
专题大样本恒星演化研究组
中国科学院天体结构与演化重点实验室
通讯作者Li, Yin-Bi(Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China)
作者单位1.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, People's Republic of China
2.University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
3.Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing 100190, People's Republic of China
4.Department of Astronomy, Beijing Normal University, Beijing 100875, People's Republic of China
5.Department of Astronomy, School of Physics, Peking University, Beijing 100871, People's Republic of China
6.Key Laboratory for the Structure and Evolution of Celestial Objects, Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, People's Republic of China
7.South-Western Institute for Astronomy Research, Yunnan University, Kunming 650500, People's Republic of China
8.Nanjing Institute of Astronomical Optics & Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042, People's Republic of China
9.LAMOST Fellow.
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GB/T 7714
Li, Yin-Bi,Luo, A-Li,Du, Chang-De,et al. Carbon Stars Identified from LAMOST DR4 Using Machine Learning[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2018,234(2).
APA Li, Yin-Bi.,Luo, A-Li.,Du, Chang-De.,Zuo, Fang.,Wang, Meng-Xin.,...&Li, Yin-Bi.(2018).Carbon Stars Identified from LAMOST DR4 Using Machine Learning.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,234(2).
MLA Li, Yin-Bi,et al."Carbon Stars Identified from LAMOST DR4 Using Machine Learning".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 234.2(2018).
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