Speaker
Description
The collapse of the core 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 almost impossible modeling and predicting the outcome of these processes, forcing us to develop model independent technique to unveil their nature. In this work, a deep learning approach, based on on a classification procedure of the time-frequency images using a Convolutional Neural Network, has been explored and its performances has been tested on the future 3G Gravitational Wave detectors.
