Generation of a supervised classification algorithm for time-series variable stars with an application to the LINEAR dataset

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• 作者：K.B. Johnston ; ^{kyjohnston2000@my.fit.edu} ; H.M. Oluseyi ^{holuseyi@fit.edu}
• 关键词：Supervised classification ; Anomaly detection ; Statistical performance ; Stellar variability
• 刊名：New Astronomy
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：35-47
• 全文大小：4034 K
• 卷排序：52

文摘

We present a new supervised classification methodology for the analysis of time series variables. We apply this analysis to the LINEAR survey dataset. An anomaly detection algorithm is developed, as are improved estimates of performance.