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Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm
Si, Jian-Min1,2,3; Li, Yin-Bi1; Luo, A-Li1,3; Tu, Liang-Ping4; Shi, Zhi-Xin1,2,3; Zhang, Jian-Nan1; Wei, Peng1,3; Zhao, Gang1; Wu, Yi-Hong2; Wu, Fu-Chao2; Zhao, Yong-Heng1
Source PublicationRESEARCH IN ASTRONOMY AND ASTROPHYSICS
2015-10-01
Volume15Issue:10Pages:1671-1694
SubtypeArticle
AbstractCarbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spectral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum classification plane in the J - H versus H - K-s color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - K-s colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and amplitudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.
KeywordMethods: Data Analysis Methods: Statistical Stars: Carbon Binaries Stars: Variables
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.1088/1674-4527/15/10/005
WOS KeywordDIGITAL SKY SURVEY ; HIGH GALACTIC LATITUDES ; WHITE-DWARF COMPANION ; VARIABILITY SURVEY ; DATA RELEASE ; APM SURVEY ; MILKY-WAY ; CLASSIFICATION ; HALO ; CH
Indexed BySCI
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000362825700005
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10336
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Key Lab Pattern Recognit, Beijing 100095, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Liaoning Univ Sci & Technol, Sch Sci, Anshan 144051, Peoples R China
Recommended Citation
GB/T 7714
Si, Jian-Min,Li, Yin-Bi,Luo, A-Li,et al. Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2015,15(10):1671-1694.
APA Si, Jian-Min.,Li, Yin-Bi.,Luo, A-Li.,Tu, Liang-Ping.,Shi, Zhi-Xin.,...&Zhao, Yong-Heng.(2015).Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,15(10),1671-1694.
MLA Si, Jian-Min,et al."Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 15.10(2015):1671-1694.
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