Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy
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, Bilby....
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| Veröffentlicht in: | The Astrophysical journal. Supplement series 2019-04, Vol.241 (2), p.27 |
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| creator | Ashton, Gregory Hübner, Moritz Lasky, Paul D. Talbot, Colm Ackley, Kendall Biscoveanu, Sylvia Chu, Qi Divakarla, Atul Easter, Paul J. Goncharov, Boris Vivanco, Francisco Hernandez Harms, Jan Lower, Marcus E. Meadors, Grant D. Melchor, Denyz Payne, Ethan Pitkin, Matthew D. Powell, Jade Sarin, Nikhil Smith, Rory J. E. Thrane, Eric |
| description | 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, Bilby. This Python 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. Bilby 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. |
| doi_str_mv | 10.3847/1538-4365/ab06fc |
| format | Article |
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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. Bilby 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.</description><identifier>ISSN: 0067-0049</identifier><identifier>EISSN: 1538-4365</identifier><identifier>DOI: 10.3847/1538-4365/ab06fc</identifier><language>eng</language><publisher>Saskatoon: The American Astronomical Society</publisher><subject>Astronomy ; Bayesian analysis ; Binary stars ; Black holes ; Computer simulation ; Gravitation ; Gravitational waves ; Mass distribution ; methods: data analysis ; methods: statistical ; Neutron stars ; Parameter estimation ; Population studies ; stars: black holes ; stars: neutron ; Statistical inference ; Stellar evolution ; Supernova remnants ; Supernovae</subject><ispartof>The Astrophysical journal. Supplement series, 2019-04, Vol.241 (2), p.27</ispartof><rights>2019. The American Astronomical Society. 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E.</creatorcontrib><creatorcontrib>Thrane, Eric</creatorcontrib><title>Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy</title><title>The Astrophysical journal. Supplement series</title><addtitle>APJS</addtitle><addtitle>Astrophys. J. Suppl</addtitle><description>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, Bilby. This Python 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. 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| ispartof | The Astrophysical journal. Supplement series, 2019-04, Vol.241 (2), p.27 |
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| subjects | Astronomy Bayesian analysis Binary stars Black holes Computer simulation Gravitation Gravitational waves Mass distribution methods: data analysis methods: statistical Neutron stars Parameter estimation Population studies stars: black holes stars: neutron Statistical inference Stellar evolution Supernova remnants Supernovae |
| title | Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy |
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