  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.
