Mining unusual and rare stellar spectra from large spectroscopic survey data sets using the outlier-detection method

	Mining unusual and rare stellar spectra from large spectroscopic survey data sets using the outlier-detection method
	Wei, Peng1,2 ; Luo, Ali1,3 ; Li, Yinbi1 ; Pan, Jingchang 3; Tu, Liangping 1,4; Jiang, Bin 1,2,3; Kong, Xiao1 ; Shi, Zhixin 1,2,5; Yi, Zhenping 1,2,3; Wang, Fengfei 1,2
刊名	MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
	2013-05-01
卷号	431 期号:2 页码:1800-1811
关键词	methods: data analysis surveys binaries: spectroscopic stars: carbon stars: emission-line, Be novae, cataclysmic variables
英文摘要	The large number of spectra obtained from sky surveys such as the Sloan Digital Sky Survey (SDSS) and the survey executed by the Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST, also called GuoShouJing Telescope) provide us with opportunities to search for peculiar or even unknown types of spectra. In response to the limitations of existing methods, a novel outlier-mining method, the Monte Carlo Local Outlier Factor (MCLOF), is proposed in this paper, which can be used to highlight unusual and rare spectra from large spectroscopic survey data sets. The MCLOF method exposes outliers automatically and efficiently by marking each spectrum with a number, i.e. using outlier index as a flag for an unusual and rare spectrum. The Local Outlier Factor (LOF) represents how unusual and rare a spectrum is compared with other spectra and the Monte Carlo method is used to compute the global LOF for each spectrum by randomly selecting samples in each independent iteration. Our MCLOF method is applied to over half a million stellar spectra (classified as STAR by the SDSS Pipeline) from the SDSS data release 8 (DR8) and a total of 37 033 spectra are selected as outliers with signal-to-noise ratio (S/N) >= 3 and outlier index >= 0.85. Some of these outliers are shown to be binary stars, emission-line stars, carbon stars and stars with unusual continuum. The results show that our proposed method can efficiently highlight these unusual spectra from the survey data sets. In addition, some relatively rare and interesting spectra are selected, indicating that the proposed method can also be used to mine rare, even unknown, spectra. The proposed method can be applicable not only to spectral survey data sets but also to other types of survey data sets. The spectra of all peculiar objects selected by our MCLOF method are available from a user-friendly website: http://sciwiki.lamost.org/Miningdr8/.
收录类别	SCI
语种	英语
WOS记录号	WOS:000318345200062
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/5951]
专题	国家天文台_光学天文研究部
作者单位	1.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.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China 4.Liaoning Univ Sci & Technol, Sch Sci, Anshan 144051, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wei, Peng,Luo, Ali,Li, Yinbi,et al. Mining unusual and rare stellar spectra from large spectroscopic survey data sets using the outlier-detection method[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2013,431(2):1800-1811.
APA	Wei, Peng.,Luo, Ali.,Li, Yinbi.,Pan, Jingchang.,Tu, Liangping.,...&Zhao, Yongheng.(2013).Mining unusual and rare stellar spectra from large spectroscopic survey data sets using the outlier-detection method.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,431(2),1800-1811.
MLA	Wei, Peng,et al."Mining unusual and rare stellar spectra from large spectroscopic survey data sets using the outlier-detection method".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 431.2(2013):1800-1811.