CCSNe detection perspectives with Einstein Telescope with MUSE

The collapse of a massive star's core at the end of its life can trigger one of the most powerful phenomena in the Universe. Due to the intense and chaotic mass motions that occur during the explosion, core-collapse supernovae have long been considered potential sources of detectable gravitational waves. However, their inherently stochastic nature makes modeling and predicting these events extremely challenging, necessitating the development of model-independent methods to better understand them. In this study, we explore a deep learning approach that utilizes a convolutional neural network to classify time-frequency images. The method's performance has been evaluated on Einstein Telescope, a third-generation gravitational wave detector.