Investigating Binary Black Hole Mergers with Principal Component Analysis

TL;DR

This paper introduces a principal component analysis and Bayesian model selection method to classify gravitational wave signals from binary black hole mergers, aiming to identify features that can improve simulations and template construction.

Contribution

It presents a novel algorithm combining PCA and Bayesian model selection for analyzing gravitational wave signals, enhancing the ability to distinguish physical effects in waveform catalogs.

Findings

Effective classification of waveforms using PCA and Bayesian methods.

Potential to improve simulation accuracy and template construction.

Demonstrated robustness across different physical effects.

Abstract

Despite recent progress in numerical simulations of the coalescence of binary black hole systems, highly asymmetric spinning systems and the construction of accurate physical templates remain challenging and computationally expensive. We explore the feasibility of a prompt and robust test of whether the signals exhibit evidence for generic features that can educate new simulations. We form catalogs of numerical relativity waveforms with distinct physical effects and compute the relative probability that a gravitational wave signal belongs to each catalog. We introduce an algorithm designed to perform this task for coalescence signals using principal component analysis of waveform catalogs and Bayesian model selection and demonstrate its effectiveness.