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Density estimation with Gaussian processes for gravitational wave posteriors

D'Emilio, V., Green, R. and Raymond, V. ORCID: 2021. Density estimation with Gaussian processes for gravitational wave posteriors. Monthly Notices of the Royal Astronomical Society 508 (2) , 2090–2097. 10.1093/mnras/stab2623

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The properties of black hole and neutron-star binaries are extracted from gravitational waves (GW) signals using Bayesian inference. This involves evaluating a multidimensional posterior probability function with stochastic sampling. The marginal probability distributions of the samples are sometimes interpolated with methods such as kernel density estimators. Since most post-processing analysis within the field is based on these parameter estimation products, interpolation accuracy of the marginals is essential. In this work, we propose a new method combining histograms and Gaussian processes (GPs) as an alternative technique to fit arbitrary combinations of samples from the source parameters. This method comes with several advantages such as flexible interpolation of non-Gaussian correlations, Bayesian estimate of uncertainty, and efficient resampling with Hamiltonian Monte Carlo.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Additional Information: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
Publisher: Royal Astronomical Society
ISSN: 0035-8711
Funders: STFC
Date of First Compliant Deposit: 29 October 2021
Date of Acceptance: 10 September 2021
Last Modified: 09 May 2023 15:36

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