Data mining for cataclysmic variables in the Large Sky Area Multi-Object Fibre Spectroscopic Telescope archive
Data mining for cataclysmic variables in the Large Sky Area Multi-Object Fibre Spectroscopic Telescope archive
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DOI：
10.1093/mnras/sts665
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发表时间：
2013-04
影响因子：
4.8
通讯作者：
B. Jiang;A. Luo;Yongheng Zhao;P. Wei
中科院分区：
文献类型：
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作者：
B. Jiang;A. Luo;Yongheng Zhao;P. Wei
随着大天空面积多目标光纤光谱望远镜(LAMOST)的投入使用，一维光谱数据档案正在逐步发布。LAMOST的目标之一是在数据中搜索特殊对象，如灾变(Cv)。提出了一种将支持向量机(SVM)技术与主成分分析(PCA)相结合的光谱识别方法。通过主成分分析进行降维和特征提取后，利用支持向量机对光谱数据进行fi分类，剔除了大部分非连续波。可以手动或通过模板匹配算法来识别fiNAL简化列表。实验表明，该数据挖掘方法能够有效、高效地从LAMOST数据库中进行fi和fi。我们报告了10个激变变星的fi正定，其中两个是新发现。此外，该方法也适用于天文望远镜数据中其他特殊天体的挖掘。
With the commissioning year of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST), the data archive of one-dimensional spectra is being released gradually. Searching for special objects like cataclysmic variables (CVs) in the data is one of LAMOST’s objectives. This paper presents a novel method to identify CVs from optical spectra by using the support-vector machine (SVM) technique combined with principal-component analysis (PCA). After dimension reduction and feature extraction by PCA, spectral data are classified by SVM and most non-CVs are excluded. The final reduced list can be identified manually or by a template-matching algorithm. Experiments show that this data-mining method can find CVs from the LAMOST data base in an effective and efficient manner. We report the identification of 10 cataclysmic variables, of which two are new discoveries. In addition, this method is also applicable to mining other special celestial objects in sky-survey telescope data.