  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).
