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Deep-learnt classification of light curves

Mahabal, A. and Sheth, K. and Gieseke, F. and Pai, A. and Djorgovski, S. G. and Drake, A. J. and Graham, M. J. (2017) Deep-learnt classification of light curves. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE , Piscataway, NJ, pp. 1-8. ISBN 978-1-5386-2727-3.

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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 series to two-dimensional light curve representations in order to classify them using modern deep learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several possible exciting extensions.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Mahabal, A.0000-0003-2242-0244
Sheth, K.0000-0002-5496-4118
Djorgovski, S. G.0000-0002-0603-3087
Graham, M. J.0000-0002-3168-0139
Additional Information:© 2017 IEEE. This work, and CRTS survey, was supported in part by the NSF grants AST-0909182, AST-1313422, AST-1413600, and AST-1518308, and by the Ajax Foundation. KS thanks IIT Gandhinagar and the Caltech SURF program.
Funding AgencyGrant Number
Ajax FoundationUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Record Number:CaltechAUTHORS:20180208-145104828
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Official Citation:A. Mahabal et al., "Deep-learnt classification of light curves," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 1-8. doi: 10.1109/SSCI.2017.8280984
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84742
Deposited By: Ruth Sustaita
Deposited On:09 Feb 2018 00:24
Last Modified:09 Mar 2020 13:18

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