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

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.