Orateur
Description
Next generation experiments such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will provide an unprecedented volume of time-domain data opening a new era of optical big data in astronomy. To fully harness the power of these surveys, new methods must be developed to deal with large data volumes and to coordinate resources for follow-up of promising candidates.
In this talk I will present Fink, a broker developed to face these challenges. Fink is based on high-end technology and designed for fast and efficient analysis of big data streams. I will present its traditional broker features and highlight the state-of-the-art machine learning techniques used to generate classification scores for a variety of time-domain phenomena: supernovae, microlensing events, solar system objects and anomalies as well as the exploration of the multi-messenger astronomy sector (gamma ray bursts, neutrinos, gravitational waves). I will detail the first results obtained by the Fink collaboration on selected science cases using the ZTF alert stream data between 2019 and 2020, and highlight the challenges towards the processing of LSST alert data.
