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While gravitational waves have been detected from mergers of binary blackholes and binary neutron stars, signals from core collapse supernovae, the mostenergetic explosions in the modern Universe, have not been detected yet. Herewe present a new method to analyse the data of the LIGO, Virgo and KAGRAnetwork to enhance the detection efficiency of this category of signals. Themethod takes advantage of a peculiarity of the gravitational wave signalemitted in the core collapse supernova and it is based on a classificationprocedure of the time-frequency images of the network data performed by aconvolutional neural network trained to perform the task to recognize thesignal. We validate the method using phenomenological waveforms injected inGaussian noise whose spectral properties are those of the LIGO and Virgoadvanced detectors and we conclude that this method can identify the signalbetter than the present algorithm devoted to select gravitational wavetransient signal.

A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae