Speaker
Massimo Brescia
(Istituto Nazionale di Astrofisica (INAF))
Description
Astronomy has entered the multi-messenger data era and Data Science has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like Rubin-LSST, requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the capabilities of human operators and crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. I will introduce the machine/deep learning paradigms suitable to explore Time Domain Astronomy in an efficient and semi-automatic way.
