The core collapse of a massive star at the end of its life can give rise to one of the most powerful phenomena in the Universe. Because of violent mass motions that take place during the explosion, core-collapse supernovae have been considered a potential source of detectable gravitational waveforms for decades. However, their intrinsic stochasticity makes ineffective the use of modeled techniques such as matched filtering, forcing us to develop model independent technique to unveil their nature. In this work we present the MUSE pipeline, which is based on a classification procedure of the time-frequency images using a convolutional neural network. The network is trained on phenomenological waveforms that are built to mimic the main common features observed in numerical simulation. The method is finally tested on a representative 3D simulation catalog in the context of the Einstein Telescope, a third generation gravitational wave telescope. Among the three detector geometries considered here, the 2L with a relative inclination of 45° is the one achieving the best results, thus being able to detect a Kuroda2016-like waveform with an efficiency above 90% at 50 kpc.

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• Gravitational waves
• Novae & supernovae