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Deep-learnt classification of light curves
2017
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time
doi:10.1109/ssci.2017.8280984
dblp:conf/ssci/MahabalSGPDDG17
fatcat:xnftyt2savdi3ihveovovw2vgu