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Spectral Feature Extraction for DB White Dwarfs Through Machine Learning Applied to New Discoveries in the Sdss DR12 and DR14
Kong,Xiao1,2; Luo,A-Li1,2; Li,Xiang-Ru3; Wang,You-Fen1; Li,Yin-Bi1; Zhao,Jing-Kun1
2018-06-26
Source PublicationPublications of the Astronomical Society of the Pacific
ISSN0004-6280
Volume130Issue:990
AbstractAbstract Using a machine learning (ML) method, we mine DB white dwarfs (DBWDs) from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 12 and DR14. The ML method consists of two parts: feature extraction and classification. The least absolute shrinkage and selection operator (LASSO) is used for the spectral feature extraction by comparing high quality data of a positive sample group with negative sample groups. In both the training and testing sets, the positive sample group is composed of a selection of 300 known DBWDs, while the negative sample groups are obtained from all types of SDSS spectra. In the space of the LASSO detected features, a support vector machine is then employed to build classifiers that are used to separate the DBWDs from the non-DBWDs for each individual type. Depending on the classifiers, the DBWD candidates are selected from the entire SDSS data set. After visual inspection, 2808 spectra (2029 objects) are spectroscopically confirmed. By checking the samples with the literature, there are 58 objects with 60 spectra that are newly identified, including a newly discovered AM CVn. Finally, we measure their effective temperatures (Teff), surface gravities (log g), and radial velocities, before compiling them into a catalog.
Keywordcatalogs methods: data analysis surveys white dwarfs
DOI10.1088/1538-3873/aac7a8
Language英语
WOS IDIOP:0004-6280-130-990-aac7a8
PublisherThe Astronomical Society of the Pacific
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/35511
Collection中国科学院国家天文台
Affiliation1.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, People's Republic of China; lal@nao.cas.cn
2.University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
3.School of mathematical Sciences, South China Normal University, Guangzhou, 510631, People's Republic of China
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kong,Xiao,Luo,A-Li,Li,Xiang-Ru,et al. Spectral Feature Extraction for DB White Dwarfs Through Machine Learning Applied to New Discoveries in the Sdss DR12 and DR14[J]. Publications of the Astronomical Society of the Pacific,2018,130(990).
APA Kong,Xiao,Luo,A-Li,Li,Xiang-Ru,Wang,You-Fen,Li,Yin-Bi,&Zhao,Jing-Kun.(2018).Spectral Feature Extraction for DB White Dwarfs Through Machine Learning Applied to New Discoveries in the Sdss DR12 and DR14.Publications of the Astronomical Society of the Pacific,130(990).
MLA Kong,Xiao,et al."Spectral Feature Extraction for DB White Dwarfs Through Machine Learning Applied to New Discoveries in the Sdss DR12 and DR14".Publications of the Astronomical Society of the Pacific 130.990(2018).
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