Emille Ishida (with support from Julien Peloton and Anais Moller)
Next generation experiments such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will provide an unprecedented volume of time-domain data, opening a new era of big data in astronomy. To fully harness the power of these surveys, 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 we will present Fink, a broker developed to face the challenges of selecting the most promising events from the 10 million candidates per night to be detected by Rubin. Fink is based on high-end technology and designed for fast and efficient analysis of big flows. We will highlight the state-of-the-art machine learning techniques used to generate early classification scores for a variety of time-domain phenomena including kilonovae and supernovae, as well as satellites glitches. Such methods include Deep Learning and Active Learning approaches to coherently incorporate available information, delivering increasingly more accurate added values throughout the duration of the survey. We will also highlight the potential for discovery of new categories of sources and how we can optimize for discovery in the era of LSST.
