Inferring Astrophysical Parameters of Core-Collapse Supernovae from their Gravitational-Wave Emission

TL;DR

This paper develops a Bayesian parameter estimation method using an asymmetric chirplet model to analyze gravitational-wave signals from core-collapse supernovae, aiming to infer source properties and explosion dynamics.

Contribution

It introduces a novel signal model and demonstrates its effectiveness in extracting key astrophysical parameters from simulated supernova gravitational-wave data.

Findings

Effective reconstruction of time-frequency emission modes.

Ability to constrain proto-neutron star mass and radius.

Potential to estimate shock revival timing.

Abstract

Nearby core-collapse supernovae (CCSNe) are powerful multi-messenger 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 high-frequency 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 proto-neutron star mass and radius, the turbulent kinetic energy onto the proto-neutron star, and the time of shock revival.