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

Ashton, Gregory and Hübner, Moritz and Lasky, Paul D. and Talbot, Colm and Ackley, Kendall and Biscoveanu, Sylvia and Chu, Qi and Divakarla, Atul and Easter, Paul J. and Goncharov, Boris and Hernandez Vivanco, Francisco and Harms, Jan and Lower, Marcus E. and Meadors, Grant D. and Melchor, Denyz and Payne, Ethan and Pitkin, Matthew D. and Powell, Jade and Sarin, Nikhil and Smith, Rory J.~E. and Thrane, Eric (2019) BILBY : A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy. The Astrophysical Journal Supplement Series, 241 (2): 27. ISSN 0067-0049

Text (1811.02042)
1811.02042.pdf - Accepted Version
Available under License Unspecified.
Download (1MB)

Abstract

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.