Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy

By G. Ashton, M. Huebner, P. Lasky, Colm Talbot, K. Ackley, S. Biscoveanu, Q. Chu, Atul Divarkala, P. Easter, B. Goncharov and 11 more. Astrophysical Journal Supplement Series, 2018

995 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/0527e6b4aa58ab5583d954919e8d6779f034a61d

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Citations in the corpus

GW231123: A Binary Black Hole Merger with Total Mass 190–265 M⊙ (2025, positive): “…to produce the online dataset used for low-latency searches (Abbott et al. 2020b; Klimenko et al. 2016b; Tsukada et al. 2023; Ewing et al. 2024; Dal Canton et al. 2021; Chu et al. 2022; Aubin et al. 2021) and parameter estimation (Singer & Price 2016; Ashton et al. 2019; Pankow et al. 2015).”
Observation of Gravitational Waves from the Coalescence of a 2.5–4.5 M ⊙ Compact Object and a Neutron Star (2024, methodology): “We use the Bilby (Ashton et al. 2019; Romero-Shaw et al. 2020) or Parallel Bilby (Smith et al. 2020) inference libraries to generate samples from the posterior distribution of the source parameters us-ing a nested sampling (Skilling 2006) algorithm, as implemented in the Dynesty software package (Sp”
Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue (2020, methodology): “Bilby is a user-friendly Bayesian inference library that can be used to analyse gravitational-wave signals to infer their source properties (Ashton et al. 2019).”
Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo (2019, positive): “, parameter estimation such as bilby [175], LALInference [176] and Bayeswave [177, 178].”
GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run (2021, positive): “For analyses using IMRPhenomXPHM and IMRPhenomP_NRTidal we use the Bilby inference package [142,166], together with the nested sampling [167] method implemented in the Dynesty sampler [168], or the Markov-chain Monte Carlo sampler implemented in the LALInference package [139,169 – 171].”