CASIA OpenIR
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, Bo6; Han, Zhan-Wen6; Xiang, Mao-Sheng1; 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
Source PublicationASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
ISSN0067-0049
2018-02-01
Volume234Issue:2
SubtypeArticle
AbstractIn 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.
KeywordMaterial: Machine-readable Table
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.3847/1538-4365/aaa415
WOS KeywordHIGH GALACTIC LATITUDES ; LOW METAL ABUNDANCE ; DIGITAL SKY SURVEY ; CH STARS ; BINARY NATURE ; BARIUM STARS ; SURVEY 2MASS ; POOR STARS ; TELESCOPE ; CATALOG
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(11303036 ; Special Funding for Advanced Users ; National Basic Research Program of China (973 Program)(2014CB845700) ; National Development and Reform Commission ; 11390371/4)
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000424258800002
PublisherIOP PUBLISHING LTD
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24472
Collection中国科学院自动化研究所
Corresponding AuthorLi, Yin-Bi
Affiliation1.Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing 100190, Peoples R China
4.Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China
5.Peking Univ, Sch Phys, Dept Astron, Beijing 100871, Peoples R China
6.Chinese Acad Sci, Yunnan Observ, Key Lab Struct & Evolut Celestial Objects, Kunming 650216, Yunnan, Peoples R China
7.Yunnan Univ, South Western Inst Astron Res, Kunming 650500, Yunnan, Peoples R China
8.Chinese Acad Sci, Nanjing Inst Astron Opt & Technol, Natl Astron Observ, Nanjing 210042, Jiangsu, Peoples R China
Recommended Citation
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.,...&Zhao, Yong-Heng.(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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