Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Accelerating multimodel Bayesian inference, model selection, and systematic studies for gravitational wave astronomy

Hoy, Charlie 2022. Accelerating multimodel Bayesian inference, model selection, and systematic studies for gravitational wave astronomy. Physical Review D 106 (8) , 083003. 10.1103/PhysRevD.106.083003

[thumbnail of Published.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview


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. An elegant solution is to simultaneously infer the model and model properties in a joint Bayesian analysis. In this work we demonstrate that a joint Bayesian analysis can not only accelerate but also account for model-dependent systematic differences in the inferred black hole properties. We verify this technique by analyzing 100 randomly chosen simulated signals and also the real gravitational wave signal GW200129_065458. We find that not only do we infer statistically identical properties as those obtained using Bayesian model averaging, but we can sample over a set of three models on average 2.5× faster. In other words, a joint Bayesian analysis that marginalizes over three models takes on average only 20% more time than a single model analysis. We then demonstrate that this technique can be used to accurately and efficiently quantify the support for one model over another, thereby assisting in Bayesian model selection.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Advanced Research Computing @ Cardiff (ARCCA)
Physics and Astronomy
Publisher: American Physical Society
ISSN: 2470-0010
Date of First Compliant Deposit: 15 February 2023
Date of Acceptance: 21 September 2022
Last Modified: 10 Jun 2024 09:37

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics