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Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy
Open accessAbstract
Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources’ astrophysical properties. We introduce a user-friendly Bayesian inference library for gravitational-wave astronomy, B ilby. This P ython code provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and reliable gravitational-wave parameter estimation on both real, freely available data from LIGO/Virgo and simulated data. We provide a suite of examples for the analysis of compact binary mergers and other types of signal models, including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, implement new likelihood functions, and add new detectors. B ilby has additional functionality to do population studies using hierarchical Bayesian modeling. We provide an example in which we infer the shape of the black hole mass distribution from an ensemble of observations of binary black hole mergers.
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Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Astropy: A community Python package for astronomy
The Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A., Price-Whelan, A. M., et al. (2013) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201322068 · Cited by 14,620
Cited by
Pratten, G., García-Quirós, C., Colleoni, M., Ramos-Buades, A., Estellés, H., Mateu-Lucena, M., Jaume, R., Haney, M., Keitel, D., Thompson, J. E., et al. (2021) · Physical Review D · doi:10.1103/physrevd.103.104056 · Cited by 555
Metadata
- Authors
- Ashton, G., Hübner, M., Lasky, P. D., Talbot, C., Ackley, K., Biscoveanu, S., Chu, Q., Divakarla, A., Easter, P. J., Goncharov, B., Vivanco, F. H., Harms, J., Lower, M. E., Meadors, G. D., Melchor, D., Payne, E., Pitkin, M. D., Powell, J., Sarin, N., Smith, R. J. E., & Thrane, E.
- Venue
- The Astrophysical Journal Supplement Series
- Publisher
- American Astronomical Society
- Published
- 2019-04-01
- Volume
- 241
- Issue
- 2
- Pages
- 27
- ISSN
- 0067-0049 (ISSN-L)
- 1538-4365
- OpenAlex
- W2899782607
- Cited by
- 1,138
- References
- 2
- FWCI
- 44.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Ashton, G., Hübner, M., Lasky, P. D., Talbot, C., Ackley, K., Biscoveanu, S., Chu, Q., Divakarla, A., Easter, P. J., Goncharov, B., Vivanco, F. H., Harms, J., Lower, M. E., Meadors, G. D., Melchor, D., Payne, E., Pitkin, M. D., Powell, J., Sarin, N., . . . Thrane, E. (2019). Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy. The Astrophysical Journal Supplement Series. https://doi.org/10.3847/1538-4365/ab06fc
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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