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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - BILBY
T2 - A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy
AU - Ashton, Gregory
AU - Hübner, Moritz
AU - Lasky, Paul D.
AU - Talbot, Colm
AU - Ackley, Kendall
AU - Biscoveanu, Sylvia
AU - Chu, Qi
AU - Divakarla, Atul
AU - Easter, Paul J.
AU - Goncharov, Boris
AU - Hernandez Vivanco, Francisco
AU - Harms, Jan
AU - Lower, Marcus E.
AU - Meadors, Grant D.
AU - Melchor, Denyz
AU - Payne, Ethan
AU - Pitkin, Matthew D.
AU - Powell, Jade
AU - Sarin, Nikhil
AU - Smith, Rory J.~E.
AU - Thrane, Eric
N1 - Author was employed at another UK HEI at the time of submission and was deposited at Imperial College London Repository, see link https://spiral.imperial.ac.uk/handle/10044/1/46087
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - gravitational waves
KW - methods: data analysis
KW - methods: statistical
KW - stars: black holes
KW - stars: neutron
KW - Astrophysics - Instrumentation and Methods for Astrophysics
KW - Astrophysics - High Energy Astrophysical Phenomena
KW - General Relativity and Quantum Cosmology
U2 - 10.3847/1538-4365/ab06fc
DO - 10.3847/1538-4365/ab06fc
M3 - Journal article
VL - 241
JO - The Astrophysical Journal Supplement Series
JF - The Astrophysical Journal Supplement Series
SN - 0067-0049
IS - 2
M1 - 27
ER -