Year: 2025
Type: conference-paper
Abstract: 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.
DOI: 10.22323/1.501.0973
Authors: Alessandro Veutro, Irene Di Palma, Marco Drago, Pablo Cerdá-Durán, Melissa Portilla López, Fulvio Ricci
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414437658
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