Testing General Relativity using Bayesian model selection: Applications to observations of gravitational waves from compact binary systems
Walter Del Pozzo, John Veitch, Alberto Vecchio

TL;DR
This paper presents a Bayesian model selection framework to test General Relativity using gravitational wave data from binary systems, demonstrating its application with a simple alternative theory involving massive gravitons.
Contribution
It introduces a Bayesian approach for comparing gravitational theories with gravitational wave observations and develops a method to combine multiple observations for stronger inference.
Findings
Bayesian model selection effectively distinguishes between GR and alternative theories.
The framework quantifies parameter inference from gravitational wave data.
Combining multiple observations enhances the strength of tests.
Abstract
Second generation interferometric gravitational wave detectors, such as Advanced LIGO and Advanced Virgo, are expected to begin operation by 2015. Such instruments plan to reach sensitivities that will offer the unique possibility to test General Relativity in the dynamical, strong field regime and investigate departures from its predictions, in particular using the signal from coalescing binary systems. We introduce a statistical framework based on Bayesian model selection in which the Bayes factor between two competing hypotheses measures which theory is favored by the data. Probability density functions of the model parameters are then used to quantify the inference on individual parameters. We also develop a method to combine the information coming from multiple independent observations of gravitational waves, and show how much stronger inference could be. As an introduction and…
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