A CNN adapted to time series for the classification of Supernovae

Abstract : Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cos-mology 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, Jérôme Pasquet, Nancy Rodriguez, Frédéric Comby, et al.. A CNN adapted to time series for the classification of Supernovae. Electronic Imaging, Jan 2019, Burlingame, CA, United States. pp.13-19. ⟨lirmm-01968489⟩

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