We applied our methodology on different supernovae light curve databases. It is designed to remove the problem of non-representativeness between the training and test databases coming from the limitations of the spectroscopic follow-up. PELICAN can deal with the sparsity and the irregular sampling of light curves. It takes light curves as input, without any additional features. We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the characterization and the classification of supernovae light curves. Montpellier, AgroParisTech, Cirad, CNRS, Irstea, Montpellier, FranceĪix-Marseille Université, CNRS, ENSAM, Université De Toulon, LIS UMR 7020, France Johanna Pasquet 1, Jérôme Pasquet 2 ,3 ,4, Marc Chaumont 5 and Dominique Fouchez 1Īix Marseille Univ., CNRS/IN2P3, CPPM, Marseille, FranceĮ-mail: Université Paul Valéry, Montpellier, France Astronomical objects: linking to databases.Including author names using non-Roman alphabets.Suggested resources for more tips on language editing in the sciences Punctuation and style concerns regarding equations, figures, tables, and footnotes
