Multi-waveform inference of gravitational waves

TL;DR

This paper introduces a method to account for modeling uncertainties in gravitational wave signal analysis by combining multiple waveform approximants into a single marginalized likelihood, improving inference accuracy.

Contribution

It presents a novel mixture-model likelihood approach that marginalizes over waveform model uncertainties using existing Bayesian evidence calculations.

Findings

Enables combined inference from multiple waveform models.

Reduces systematic errors due to waveform modeling uncertainties.

Integrates seamlessly with current Bayesian inference workflows.

Abstract

Bayesian inference of gravitational wave signals is subject to systematic error due to modelling uncertainty in waveform signal models, coined approximants. A growing collection of approximants are available which use different approaches and make different assumptions to ease the process of model development. We provide a method to marginalize over the uncertainty in a set of waveform approximants by constructing a mixture-model multi-waveform likelihood. This method fits into existing workflows by determining the mixture parameters from the per-waveform evidences, enabling the production of marginalized combined sample sets from independent runs.