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Volume: 31 | Article ID: art00013
A CNN adapted to time series for the classification of Supernovae
  DOI :  10.2352/ISSN.2470-1173.2019.14.COLOR-090  Published OnlineJanuary 2019

Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophysical models, and thus, for the classification, they usually use a machine learning approach in two-steps, which consists in, first, extracting features, and second, using a classifier. In this paper, we are specifically studying the supernovae phenomenon and especially the binary classification “I.a supernovae versus not-I.a supernovae”. We present two Convolutional Neural Networks (CNNs) defeating the current state-of-the-art. The first one is adapted to time series and thus to the treatment of supernovae light-curves. The second one is based on a Siamese CNN and is suited to the nature of data, i.e. their sparsity and their weak quantity (small learning database).

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Anthony BRUNEL, Johanna PASQUET, Jérôome PASQUET, Nancy RODRIGUEZ, Frédéric COMBY, Dominique FOUCHEZ, Marc CHAUMONT, "A CNN adapted to time series for the classification of Supernovaein Proc. IS&T Int’l. Symp. on Electronic Imaging: Color Imaging XXIV: Displaying, Processing, Hardcopy, and Applications,  2019,  pp 90-1 - 90-9,

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