Neural Network to interpret neutrino’s signal from supernovae gravitational collapse - Espíndola, Isadora; Schneider, André (Cadernos de Astronomia, 2026)
Massive stars end their life cycles when their inert core collapses and a protoneutron star (PNS) is born, often followed up by a supernova explosion. If such supernova were to occur in the Milky Way or a nearby galaxy, thousands to millions of neutrinos would be detected on laboratories on Earth. In this work we use a Long short-term memory (LSTM) neural newtwork to infer the gravitational mass of a PNS from the neutrinos it emits in the first two seconds after its formation. Neutrino data was obtained from spherically symmetric (1D) simulations of core-collapse supernovae of 18 progenitor stars employing two equations of state as well as two heating factors to emulate multidimensional effects in 1D simulations. We tested our LSTM network in data from core-collapse simulations of 49 stars not used in the training and showed that, in 18 cases, the neural network is able to reproduce the gravitational mass evolution of the PNS with only small deviations (R2 ≳ 0.80). We discuss how the neural network can be improved and the steps still needed so that it can be used to analyze future neutrino detections. To the best of our knowledge, this is the first work of its kind and will be useful as a parameterization of future works.