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

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arxiv 2106.08730 v2 pith:47OZQ3JE submitted 2021-06-16 gr-qc astro-ph.HE

Gregory Ashton , Colm Talbot This is my paper · ORCID
classification gr-qcastro-ph.HE
keywords bilby-mcmcsamplerposterioranalysisrobustsamplessamplingalgorithm
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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 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.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects

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    A single RJMCMC run can rank BBH, NSBH, and BNS waveform models and deliver the favored model's parameter posteriors, validated on injections and two real GW events.

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    astro-ph.IM 2026-07 accept novelty 6.0 of 10

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  3. Establishing Compactness as a Population Observable in Gravitational-Wave Astronomy

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    Hierarchical analysis of GWTC-3 yields C_eff = 0.5^{+0.3}_{-0.1} consistent with black holes and limits low-compactness exotic binary merger rate to <0.7 Gpc^{-3} yr^{-1}.

  4. Flexible Gravitational-Wave Parameter Estimation with Transformers

    gr-qc 2025-12 conditional novelty 6.0 of 10

    Dingo-T1 is one transformer model that adapts at inference to arbitrary detector subsets and frequency cuts for gravitational-wave parameter estimation.

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    Simulated cosmic-string lensing of binary black holes predicts Einstein Telescope would detect the events and infer Gµ ≈ 1e-10, though the source geometry is assumed ad hoc.

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  7. On the use and interpretation of signal-model indistinguishability measures for gravitational-wave astronomy

    gr-qc 2025-06 accept novelty 5.0 of 10

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