Abstract Machine learning (automated processes that learn by example in order to classify,
predict, discover, or generate new data) and artificial intelligence (methods by which a …

An array of large observational programs using ground-based and space-borne telescopes
is planned in the next decade. The forthcoming wide-field sky surveys are expected to …

We introduce SuperNNova, an open-source supernova photometric classification framework
that leverages recent advances in deep neural networks. Our core algorithm is a recurrent …

Since the beginning of the 21st century, the fields of astronomy and astrophysics have
experienced significant growth at observational and computational levels, leading to the …

Context. Classification will be an important first step for upcoming surveys aimed at detecting
billions of new sources, such as LSST and Euclid, as well as DESI, 4MOST, and MOONS …

We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the
characterization and the classification of supernovae light curves. It takes light curves as …

Large-scale sky surveys have played a transformative role in our understanding of
astrophysical transients, only made possible by increasingly powerful machine learning …

The advent of wide-field sky surveys has led to the growth of transient and variable source
discoveries. The data deluge produced by these surveys has necessitated the use of …

In this paper we introduce a reliable, fully automated and fast algorithm to detect extended
extragalactic radio sources (cluster of galaxies, filaments) in existing and forthcoming …

During the last decade, a considerable amount of effort has been made to classify variable
stars using different machine-learning techniques. Typically, light curves are represented as …