|
Next generation experiments such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will provide a rich source of information for multi-messenger astronomy tasks. To fully harness the power of these surveys, we require analysis methods capable of dealing with large streams, and which can identify promising transients within minutes for follow-up coordination. In this talk I will present Fink, an astronomy broker specifically designed for LSST. Fink is based on high-end technology and designed for fast and efficient analysis of big data streams. It has been chosen as one of the official LSST brokers who will receive the full data stream. I 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. I will also describe the current efforts being developed in Australia (https://www.ozgrav.org/ozfink-workshop-2023.html) that will enable easy access to LSST the data stream through Fink, and discuss the possibility to develop tailored filters and science modules for other applications. In combination with other efforts already developed within the Fink community, this collaboration has the potential to boost scientific outcomes from searches for electromagnetic counterparts of gravitational waves. |
|