Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, annotation, selection and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification which is continuously improved by using state-of-the-art Deep Learning and Adaptive Learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for diverse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper we introduce Fink, its science motivation, architecture and current status including first science verification cases using the Zwicky Transient Facility alert stream.
@article{arxiv.2009.10185,
title = {Fink, a new generation of broker for the LSST community},
author = {Anais Möller and Julien Peloton and Emille E. O. Ishida and Chris Arnault and Etienne Bachelet and Tristan Blaineau and Dominique Boutigny and Abhishek Chauhan and Emmanuel Gangler and Fabio Hernandez and Julius Hrivnac and Marco Leoni and Nicolas Leroy and Marc Moniez and Sacha Pateyron and Adrien Ramparison and Damien Turpin and Réza Ansari and Tarek Allam and Armelle Bajat and Biswajit Biswas and Alexandre Boucaud and Johan Bregeon and Jean-Eric Campagne and Johann Cohen-Tanugi and Alexis Coleiro and Damien Dornic and Dominique Fouchez and Olivier Godet and Philippe Gris and Sergey Karpov and Ada Nebot Gomez-Moran and Jérémy Neveu and Stephane Plaszczynski and Volodymyr Savchenko and Natalie Webb},
journal= {arXiv preprint arXiv:2009.10185},
year = {2020}
}