Data Mining of Cataclysmic Variables Candidates in Massive Spectra
JIANG Bin1,2, LUO A-li1, ZHAO Yong-heng1
1. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Beijing 100012, China 2. School of Mechanical, Electrical &Information Engineering, Shandong University at Weihai, Weihai 264209, China
Abstract：An automatic and efficient method for LAMOST’s massive spectral data reduction is presented in this paper. The identified cataclysmic variables were selected as templates to construct the feature space by PCA (the principal component analysis), and most of the non-candidates were excluded by the method using support vector machine. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. Fifty eight new CVs candidates were found in the experiment, showing that our approach to finding special celestial bodies can be practical in LAMOST.
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