FINK, a new generation of broker for the LSST community

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.

Automated summary

FINK is a broker designed to support science with large time-domain alert streams, offering traditional broker features as well as real-time transient classification improved by advanced learning techniques. This tool aims to enhance the accuracy of scientific output from LSST photometric data and increase the frequency of new discoveries.