Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce

TL;DR

This paper introduces a Bayesian method utilizing principal component analysis to reconstruct gravitational wave signals from rotating stellar core collapse, enabling extraction of physical parameters like progenitor mass and rotation from detector data.

Contribution

The novel approach combines PCA and Bayesian inference to accurately reconstruct gravitational wave signals and infer physical parameters of stellar core collapse events.

Findings

Successful reconstruction of gravitational wave signals from simulated data

Estimation of physical parameters such as progenitor mass and rotation

Demonstrated potential for extracting core collapse physics from detector observations

Abstract

Presented in this paper is a technique that we propose for extracting the physical parameters of a rotating stellar core collapse from the observation of the associated gravitational wave signal from the collapse and core bounce. Data from interferometric gravitational wave detectors can be used to provide information on the mass of the progenitor model, precollapse rotation and the nuclear equation of state. We use waveform libraries provided by the latest numerical simulations of rotating stellar core collapse models in general relativity, and from them create an orthogonal set of eigenvectors using principal component analysis. Bayesian inference techniques are then used to reconstruct the associated gravitational wave signal that is assumed to be detected by an interferometric detector. Posterior probability distribution functions are derived for the amplitudes of the principal component analysis eigenvectors, and the pulse arrival time. We show how the reconstructed signal and the principal component analysis eigenvector amplitude estimates may provide information on the physical parameters associated with the core collapse event.