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Rare Object Search From Low-S/N Stellar Spectra in SDSS

文献类型：期刊论文

作者Wu, Minglei2; Pan, Jingchang2; Yi, Zhenping2; Wei, Peng1
刊名IEEE ACCESS
出版日期2020
卷号8页码:66475-66488
ISSN号2169-3536
关键词Principal component analysis Search problems Astronomy Support vector machines Wavelength division multiplexing Feature extraction Random forests SDSS stellar spectra machine learning rare object search
DOI10.1109/ACCESS.2020.2983745
英文摘要Rare objects such as white dwarf & x002B;main sequence (WDMS) and cataclysmic variables (CVs) are very important for studying the evolution of the galaxy and the universe. The large amount of spectra obtained by the large sky surveys such as the Sloan Digital Sky Survey (SDSS) are rich sources of these rare objects. However, a considerable fraction of these spectra are low-S/N spectra. These low-S/N spectra contain similar useful information as the high-S/N spectra, and making better use of these spectra can significantly improve the chance of finding rare objects. Nevertheless, little research has been done on them. In this study we propose a novel method based on the combination of PCA (Principal Components Analysis) and CFSFDP (Clustering by Fast Search and Find of Density Peak) to search for rare objects from low-S/N spectra. The PCA first extracts principal components from high-S/N spectra to generate general feature spectra and reconstructs low-S/N stellar spectra with these general feature spectra. Then the CFSFDP calculates the Local Density and the Distance of the reconstructed spectra, and select the outliers through the decision graph quickly and accurately. We first apply our method to spectra in SDSS stellar classification template library with adding white gaussian noise to search for rare objects (carbon stars, carbon white dwarfs, carbon & x005F;lines, white dwarfs and white dwarfs magnetic). Then we apply our method to observed spectra with different low-S/Ns from SDSS and compared with Lick-index & x002B;K-means and Support Vector Machines (SVM). The experimental results show that our method has a higher efficiency compared to other methods.
WOS关键词CATACLYSMIC VARIABLES CANDIDATES ; MAIN SEQUENCE BINARIES ; CLUSTERING ANALYSIS ; WHITE-DWARFS ; LAMOST ; CLASSIFICATION ; GALAXY ; DISCOVERY ; SAMPLE ; STARS
资助项目National Natural Science Foundation of China[U1931209] ; National Natural Science Foundation of China[11603012] ; National Natural Science Foundation of China[11873037] ; National Natural Science Foundation of China[11603014] ; National Natural Science Foundation of China[11803016]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000527415800008
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.bao.ac.cn/handle/114a11/55100]  
专题中国科学院国家天文台
通讯作者Pan, Jingchang
作者单位1.Chinese Acad Sci, Natl Astron Observ, Key Lab Opt Astron, Beijing 100012, Peoples R China
2.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Minglei,Pan, Jingchang,Yi, Zhenping,et al. Rare Object Search From Low-S/N Stellar Spectra in SDSS[J]. IEEE ACCESS,2020,8:66475-66488.
APA Wu, Minglei,Pan, Jingchang,Yi, Zhenping,&Wei, Peng.(2020).Rare Object Search From Low-S/N Stellar Spectra in SDSS.IEEE ACCESS,8,66475-66488.
MLA Wu, Minglei,et al."Rare Object Search From Low-S/N Stellar Spectra in SDSS".IEEE ACCESS 8(2020):66475-66488.

入库方式： OAI收割

来源：国家天文台

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