Multivariate Regression Analysis of Gravitational Waves from Rotating Core Collapse

TL;DR

This paper introduces a multivariate regression model using principal component analysis to analyze and estimate parameters of gravitational waves from rotating core-collapse supernovae, effectively identifying key physical parameters and predicting waveforms.

Contribution

The paper presents a novel linear regression approach based on principal components for analyzing gravitational waveforms from core collapse supernovae, accommodating non-linear parameter dependencies.

Findings

Successfully identifies key physical parameters influencing waveforms.

Capable of predicting waveforms from physical parameters.

Effective in noisy detector environments.

Abstract

We present a new multivariate regression model for analysis and parameter estimation of gravitational waves observed from well but not perfectly modeled sources such as core-collapse supernovae. Our approach is based on a principal component decomposition of simulated waveform catalogs. Instead of reconstructing waveforms by direct linear combination of physically meaningless principal components, we solve via least squares for the relationship that encodes the connection between chosen physical parameters and the principal component basis. Although our approach is linear, the waveforms' parameter dependence may be non-linear. For the case of gravitational waves from rotating core collapse, we show, using statistical hypothesis testing, that our method is capable of identifying the most important physical parameters that govern waveform morphology in the presence of simulated detector noise. We also demonstrate our method's ability to predict waveforms from a principal component basis given a set of physical progenitor parameters.