B ilby-MCMC: an MCMC sampler for gravitational-wave inference. Issue 2 (5th August 2021)

• Record Type: Journal Article
• Authors: Ashton, G; Talbot, C
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 507:Issue 2(2021)
• Page Start: 2037
• Page End: 2051
• Publication Date: 2021-08-05
• Subjects: gravitational waves -- methods: data analysis -- stars: neutron; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab2236
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: 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.