Speaker
Mario Lauriano
(INAF - Osservatorio Astronomico di Palermo, Università di Padova)
Description
The GalRSG project is a long-term, high-cadence, multi-band photometric monitoring campaign designed to detect pre-supernova variability and intense mass-loss events in Red Supergiants (RSGs), which are key progenitors of Type II supernovae. By applying advanced machine learning techniques to high-precision photometric data, the project aims to identify anomalies and mass-loss signatures in stellar light curves. Given the large volume and multi-band nature of the data, machine learning is essential for efficient analysis. This work will improve our understanding of the late evolutionary stages of massive stars and the physical processes leading up to core-collapse supernova explosions.