Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
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 model including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, how to implement new likelihood functions, and how to add new detectors. Bilby has additional functionality to do population studies using hierarchical Bayesian modelling. 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.
Keywords
Cite
@article{arxiv.1811.02042,
title = {Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy},
author = {Gregory Ashton and Moritz Huebner and Paul D. Lasky and Colm Talbot and Kendall Ackley and Sylvia Biscoveanu and Qi Chu and Atul Divarkala and Paul J. Easter and Boris Goncharov and Francisco Hernandez Vivanco and Jan Harms and Marcus E. Lower and Grant D. Meadors and Denyz Melchor and Ethan Payne and Matthew D. Pitkin and Jade Powell and Nikhil Sarin and Rory J. E. Smith and Eric Thrane},
journal= {arXiv preprint arXiv:1811.02042},
year = {2019}
}
Comments
Submitted for publication