CCSNe detection perspectives with Einstein Telescope

¹ Università di Roma La Sapienza, I-00185 Roma, Italy
² INFN, Sezione di Roma, I-00185 Roma, Italy
³ Departamento de Astronomía y Astrofísica, Universitat de València, Dr. Moliner 50, 46100, Burjassot (Valencia), Spain
⁴ Institute for Gravitational and Subatomic Physics (GRASP), Department of Physics, Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands
⁵ Nikhef, Science Park 105, 1098 XG Amsterdam, The Netherlands

^* e-mail: alessandro.veutro@uniroma1.it

Published online: 6 March 2025

Abstract

Core collapse supernovae are the most energetic explosions in the modern Universe and, because of their properties, they are considered a potential source of detectable gravitational waveforms for long time. The main obstacles to their detection are the weakness of the signal and its complexity, which cannot be modeled, making it almost impossible to apply matching filter techniques as the ones used for detecting compact binary coalescences. Although the first obstacle will probably be overcome by next-generation gravitational wave detectors, the second one can be overcome by adopting machine learning techniques. In this contribution, a novel method based on a classification procedure of the time-frequency images using a convolutional neural network will be described, showing the CCSN detection capability of the next-generation gravitational wave detectors, with a focus on the Einstein Telescope.