FINK, a new generation of broker for the LSST community

March 2021 • 2021MNRAS.501.3272M

Authors • Möller, Anais • Peloton, Julien • Ishida, Emille E. O. • Arnault, Chris • Bachelet, Etienne • Blaineau, Tristan • Boutigny, Dominique • Chauhan, Abhishek • Gangler, Emmanuel • Hernandez, Fabio • Hrivnac, Julius • Leoni, Marco • Leroy, Nicolas • Moniez, Marc • Pateyron, Sacha • Ramparison, Adrien • Turpin, Damien • Ansari, Réza • Allam, Tarek, Jr. • Bajat, Armelle • Biswas, Biswajit • Boucaud, Alexandre • Bregeon, Johan • Campagne, Jean-Eric • Cohen-Tanugi, Johann • Coleiro, Alexis • Dornic, Damien • Fouchez, Dominique • Godet, Olivier • Gris, Philippe • Karpov, Sergey • Nebot Gomez-Moran, Ada • Neveu, Jérémy • Plaszczynski, Stephane • Savchenko, Volodymyr • Webb, Natalie

Abstract • 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 automatized 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 that 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.

Links

• SIMBAD http://simbad.u-strasbg.fr/simbo.pl?bibcode=2021MNRAS.501.3272M
• PDF http://academic.oup.com/mnras/pdf-lookup/doi/10.1093/mnras/staa3602
• NED https://ned.ipac.caltech.edu/uri/NED::InRefcode/2021MNRAS.501.3272M
• PREPRINT http://arxiv.org/abs/2009.10185
• ELECTR https://doi.org/10.1093%2Fmnras%2Fstaa3602