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Bilby-MCMC: An MCMC sampler for gravitational-wave inference

Ashton, G. and Talbot, C. (2021) Bilby-MCMC: An MCMC sampler for gravitational-wave inference. Monthly Notices of the Royal Astronomical Society, 507 (2). pp. 2037-2051. ISSN 0035-8711. doi:10.1093/mnras/stab2236.

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We introduce BILBY-MCMC, a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. BILBY-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate that learning proposals can produce over a 10-fold improvement in efficiency by reducing the autocorrelation time. Using a variety of standard and problem-specific tests, we validate the ability of the BILBY-MCMC sampler to produce independent posterior samples and estimate the Bayesian evidence. Compared to the widely used DYNESTY nested sampling algorithm, BILBY-MCMC is less efficient in producing independent posterior samples and less accurate in its estimation of the evidence. However, we find that posterior samples drawn from the BILBY-MCMC sampler are more robust: never failing to pass our validation tests. Meanwhile, the DYNESTY sampler fails the difficult-to-sample Rosenbrock likelihood test, over constraining the posterior. For CBC problems, this highlights the importance of cross-sampler comparisons to ensure results are robust to sampling error. Finally, BILBY-MCMC can be embarrassingly and asynchronously parallelized making it highly suitable for reducing the analysis wall-time using a High Throughput Computing environment. BILBY-MCMC may be a useful tool for the rapid and robust analysis of gravitational-wave signals during the advanced detector era and we expect it to have utility throughout astrophysics.

Item Type:Article
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URLURL TypeDescription Paper ItemScripts
Ashton, G.0000-0001-7288-2231
Talbot, C.0000-0003-2053-5582
Additional Information:© 2021 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( Accepted 2021 July 23. Received 2021 July 7; in original form 2021 June 16. Published: 05 August 2021. We thank Michael Williams, John Veitch, Ben Farr, and Moritz Hübner for useful comments during the development of this work. We thank Will Farr, whose review of this work led to several improvements and clarifications in our exposition. CT acknowledges support of the National Science Foundation, and the LIGO Laboratory. We are grateful for computational resources provided by Cardiff University, and funded by an STFC grant ST/I006285/1 supporting UK Involvement in the Operation of Advanced LIGO. We are also grateful to computing resourced provided by the LIGO Laboratory computing clusters at California Institute of Technology and LIGO Hanford Observatory supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This work makes use of the SCIPY (Virtanen et al. 2020), NUMPY (Oliphant 2006; Van Der Walt, Colbert & Varoquaux 2011; Harris et al. 2020), and PESUMMARY (Hoy & Raymond 2020) packages for data analysis and visualization. Data Availability: No new data were generated or analysed in support of this research. The scripts used to perform all verification checks and additional figures are available from
Funding AgencyGrant Number
Science and Technology Facilities Council (STFC)ST/I006285/1
Subject Keywords:gravitational waves – methods: data analysis – stars: neutron
Issue or Number:2
Record Number:CaltechAUTHORS:20211026-154441776
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Official Citation:G Ashton, C Talbot, B ILBY-MCMC: an MCMC sampler for gravitational-wave inference, Monthly Notices of the Royal Astronomical Society, Volume 507, Issue 2, October 2021, Pages 2037–2051,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111645
Deposited By: Tony Diaz
Deposited On:26 Oct 2021 15:59
Last Modified:26 Oct 2021 15:59

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