We examine the potential for using the LIGO-Virgo-KAGRA network of gravitational-wave detectors to provide constraints on the physical properties of core-collapse supernovae through the observation of their gravitational radiation. We use waveforms generated by 14 of the latest 3D hydrodynamic core-collapse supernova simulations, which are added to noise samples based on the predicted sensitivities of the GW detectors during the O5 observing run. Then we use the BayesWave algorithm to model-independently reconstruct the gravitational-wave waveforms, which are used as input for various machine learning algorithms. Our results demonstrate how these algorithms perform in terms of i) indicating the presence of specific features of the progenitor or the explosion, ii) predicting the explosion mechanism, and iii) estimating the mass and angular velocity of the progenitor, as a function of the signal-to-noise ratio of the observed supernova signal. The conclusions of our study highlight the potential for GW observations to complement electromagnetic detections of supernovae by providing unique information about the exact explosion mechanism and the dynamics of the collapse.
- Astrophysics - High Energy Astrophysical Phenomena
- General Relativity and Quantum Cosmology