Unveiling gravitational waves from core-collapse supernovae with MUSE

TL;DR

This paper introduces MUSE, a neural network-based pipeline for detecting gravitational waves from core-collapse supernovae, overcoming the challenges posed by their stochastic signals and improving detection efficiency with third-generation GW detectors.

Contribution

The paper presents a novel, model-independent classification method using CNNs to identify supernova gravitational wave signals in noisy data.

Findings

Achieves over 90% detection efficiency for certain waveforms at 50 kpc.

Best detector geometry is 2L with 45° inclination.

Method is effective on 3D simulation data for Einstein Telescope.

Abstract

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 modelled techniques such as matched filtering, forcing us to develop model independent technique to unveil their nature. In this work we present 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 Einstein Telescope, a third generation GW telescope. Among the three detector geometries considered here, the 2L with a relative inclination of $45^\circ$ is the one achieving the best results, thus being able to detect a Kuroda2016-like waveform with an efficiency above $90\%$ at 50 kpc.