Accelerating Multi-Model Bayesian Inference, Model Selection and Systematic Studies for Gravitational Wave Astronomy

Understand

Gravitational wave models are used to infer the properties of black holes in merging binaries from the observed gravitational wave signals through Bayesian inference.

• Although we have access to a large collection of signal models that are sufficiently accurate to infer the properties of black holes, for some signals, small discrepancies in the models lead to systematic differences in the inferred properties.
• In order to provide a single estimate for the properties of the black holes, it is preferable to marginalize over the model uncertainty.
• Bayesian model averaging is a commonly used technique to marginalize over multiple models, however, it is computationally expensive.