Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations

TL;DR

This paper develops a Bayesian framework to analyze noisy gravitational wave signals from core collapse supernovae, accurately estimating physical parameters and classifying progenitor rotation, advancing gravitational wave data analysis methods.

Contribution

It introduces a Bayesian principal component regression model with model selection and machine learning classification for supernova gravitational wave signals, incorporating unknown signal arrival times.

Findings

Predicted the ratio of rotational kinetic energy with 90% credible intervals of ~0.04 and ~0.06.

Classified precollapse differential rotation effectively, especially for rapid rotators.

Selected 14 principal components as optimal for signal reconstruction.

Abstract

Using the latest numerical simulations of rotating stellar core collapse, we present a Bayesian framework to extract the physical information encoded in noisy gravitational wave signals. We fit Bayesian principal component regression models with known and unknown signal arrival times to reconstruct gravitational wave signals, and subsequently fit known astrophysical parameters on the posterior means of the principal component coefficients using a linear model. We predict the ratio of rotational kinetic energy to gravitational energy of the inner core at bounce by sampling from the posterior predictive distribution, and find that these predictions are generally very close to the true parameter values, with $90\%$ credible intervals $\sim 0.04$ and $\sim 0.06$ wide for the known and unknown arrival time models respectively. Two supervised machine learning methods are implemented to classify precollapse differential rotation, and we find that these methods discriminate rapidly rotating progenitors particularly well. We also introduce a constrained optimization approach to model selection to find an optimal number of principal components in the signal reconstruction step. Using this approach, we select 14 principal components as the most parsimonious model.