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B ilby-MCMC: an MCMC sampler for gravitational-wave inference.
- Source :
-
Monthly Notices of the Royal Astronomical Society . Oct2021, Vol. 507 Issue 2, p2037-2051. 15p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MARKOV chain Monte Carlo
*GRAVITATIONAL waves
*ALGORITHMS
*SAMPLING errors
Subjects
Details
- Language :
- English
- ISSN :
- 00358711
- Volume :
- 507
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Monthly Notices of the Royal Astronomical Society
- Publication Type :
- Academic Journal
- Accession number :
- 152381919
- Full Text :
- https://doi.org/10.1093/mnras/stab2236