Inferring astrophysical parameters of core-collapse supernovae from their gravitational-wave emission

Nearby core-collapse supernovae (CCSNe) are powerful multimessenger sources for gravitational-wave, neutrino, and electromagnetic telescopes as they emit gravitational waves in the ideal frequency band for ground based detectors. Once a CCSN gravitational-wave signal is detected, we will need to determine the parameters of the signal and understand how those parameters relate to the source's explosion, progenitor, and remnant properties. This is a challenge due to the stochastic nature of CCSN explosions, which is imprinted on their time series gravitational waveforms. In this paper, we perform Bayesian parameter estimation of CCSN signals using an asymmetric chirplet signal model to represent the dominant highfrequency mode observed in spectrograms of CCSN gravitational-wave signals. We use design sensitivity Advanced LIGO noise and CCSN waveforms from four different hydrodynamical supernova simulations with a range of different progenitor stars. We determine how well our model can reconstruct time-frequency images of the emission modes and show how well we can determine parameters of the signal such as the frequency, amplitude, and duration. We show how the parameters of our signal model may allow us to place constraints on the protoneutron star mass and radius, the turbulent kinetic energy onto the protoneutron star, and the time of shock revival.

Automated summary

In this paper, Bayesian parameter estimation and asymmetric chirplet signal model are used to study the characteristics and parameters of CCSN signals, exploring the potential to constrain parameters such as protoneutron star mass, radius, and turbulent kinetic energy.