Identifying novelty among millions nightly of candidates from the Rubin Observatory

Emille Ishida (LPCA)

To fully harness the scientific potential of Rubin data, we require analysis methods capable of dealing with large data volumes that can identify promising transients within minutes for follow-up coordination. In this talk I will present Fink, a Rubin community broker developed to face these challenges. Fink is based on high-end technology and designed for fast and efficient analysis of big data streams. More importantly, it has been designed to answer specific science questions which are unique to different science cases. I will describe how the filters within Fink are constructed and what are the steps necessary to prepare a science module given specific requirements, which can be from simple cuts to complex machine learning models. I will also describe the lessons learned from ZTF and first outcomes from Rubin data.