When is Enough Good Enough in Gravitational Wave Source Modeling?

TL;DR

This paper explores Bayesian model selection to determine the most plausible gravitational waveform models for analyzing data from the Laser Interferometer Space Antenna, addressing the limitations of assuming a single waveform model.

Contribution

It introduces Bayesian model selection as a method to choose among multiple waveform models, improving gravitational wave data analysis.

Findings

Bayesian model selection effectively identifies the best waveform model.

The approach enhances parameter estimation accuracy.

It provides a systematic framework for model comparison in gravitational wave analysis.

Abstract

A typical approach to developing an analysis algorithm for analyzing gravitational wave data is to assume a particular waveform and use its characteristics to formulate a detection criteria. Once a detection has been made, the algorithm uses those same characteristics to tease out parameter estimates from a given data set. While an obvious starting point, such an approach is initiated by assuming a single, correct model for the waveform regardless of the signal strength, observation length, noise, etc. This paper introduces the method of Bayesian model selection as a way to select the most plausible waveform model from a set of models given the data and prior information. The discussion is done in the scientific context for the proposed Laser Interferometer Space Antenna.