海量光谱中激变变星候选体的数据挖掘

Data Mining of Cataclysmic Variables Candidates in Massive Spectra

提出一套适用于海量光谱自动快速筛选激变变星的方法。利用已证认的激变变星光谱作为模板,使用主分量分析提取主特征后构造光谱特征矩阵,将海量光谱利用光谱特征矩阵映射到特征空间后,使用支持向量机排除大部分非候选体,最后对较少数量的候选体进行模板匹配并证认,结果作为反馈进一步丰富模板库。实验发现了58个新的激变变星候选体,表明了该方法的可行性,为在LAMOST海量光谱中快速搜索激变变星等稀少天体提供了有效途径。

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.