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Fast non-Markovian sampler for estimating gravitational-wave posteriors

Tiwari, Vaibhav, Hoy, Charlie, Fairhurst, Stephen ORCID: https://orcid.org/0000-0001-8480-1961 and MacLeod, Duncan ORCID: https://orcid.org/0000-0002-1395-8694 2023. Fast non-Markovian sampler for estimating gravitational-wave posteriors. Physical Review D (particles, fields, gravitation, and cosmology) 108 (2) , 023001. 10.1103/PhysRevD.108.023001

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Abstract

This article introduces varaha, an open-source, fast, non-Markovian sampler for estimating gravitational-wave posteriors. varaha differs from existing nested sampling algorithms by gradually discarding regions of low likelihood, rather than gradually sampling regions of high likelihood. This alternative mindset enables varaha to freely draw samples from anywhere within the high-likelihood region of the parameter space, allowing for analyses to complete in significantly fewer cycles. This means that varaha can significantly reduce both the wall and CPU time of all analyses. varaha offers many benefits, particularly for gravitational-wave astronomy where Bayesian inference can take many days, if not weeks, to complete. For instance, varaha can be used to estimate accurate sky locations, astrophysical probabilities and source classifications within minutes, which is particularly useful for multimessenger follow-up of binary neutron star observations; varaha localizes GW170817 ∼ 30 times faster than lalinference. Although only aligned-spin, dominant multipole waveform models can be used for gravitational-wave analyses, it has the potential to include additional physics. We envision varaha being used for gravitational-wave studies, particularly estimating parameters using expensive waveform models, analyzing subthreshold gravitational-wave candidates, generating simulated data for population studies, and rapid posterior estimation for binary neutron star mergers.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Publisher: American Physical Society
ISSN: 2470-0010
Date of First Compliant Deposit: 19 December 2023
Date of Acceptance: 2 June 2023
Last Modified: 03 Jan 2024 11:30
URI: https://orca.cardiff.ac.uk/id/eprint/164973

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