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

TL;DR

Bilby-MCMC is a new MCMC sampler optimized for gravitational-wave data analysis, offering robustness and parallelization capabilities, with significant efficiency improvements from machine learning proposals.

Contribution

Introduces Bilby-MCMC, a parallel-tempered ensemble MCMC sampler with machine learning proposals, validated for gravitational-wave inference and robustness compared to existing methods.

Findings

Learning proposals improve efficiency over 10-fold.

Posterior samples are more robust and pass validation tests.

Less efficient than dynesty in evidence estimation but more reliable.

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 parallelised 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.