Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy

Gregory Ashton, Moritz Hübner, Paul D. Lasky, Colm Talbot, Kendall Ackley, Sylvia Biscoveanu, Qi Chu, Atul Divakarla, Paul J. Easter, Boris Goncharov, Francisco Hernandez Vivanco, Jan Harms, Marcus E. Lower, Grant D. Meadors, Denyz Melchor, Ethan Payne, Matthew D. Pitkin, Jade Powell, Nikhil Sarin, Rory J.E. Smith & 1 others Eric Thrane

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish
Article number27
JournalAstrophysical Journal, Supplement Series
Volume241
Issue number2
DOIs
Publication statusPublished - 1 Apr 2019

Fingerprint

astronomy
inference
gravitational waves
merger
syntax
LIGO (observatory)
binary stars
mass distribution
neutron stars
supernovae
astrophysics
infrastructure
library
detectors
modeling
parameter estimation

Cite this

Ashton, G., Hübner, M., Lasky, P. D., Talbot, C., Ackley, K., Biscoveanu, S., ... Thrane, E. (2019). Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy. Astrophysical Journal, Supplement Series, 241(2), [27]. https://doi.org/10.3847/1538-4365/ab06fc
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. / Bilby : A user-friendly Bayesian inference library for gravitational-wave astronomy. In: Astrophysical Journal, Supplement Series. 2019 ; Vol. 241, No. 2.
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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.",
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Ashton, G, Hübner, M, Lasky, PD, Talbot, C, Ackley, K, Biscoveanu, S, Chu, Q, Divakarla, A, Easter, PJ, Goncharov, B, Vivanco, FH, Harms, J, Lower, ME, Meadors, GD, Melchor, D, Payne, E, Pitkin, MD, Powell, J, Sarin, N, Smith, RJE & Thrane, E 2019, 'Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy' Astrophysical Journal, Supplement Series, vol. 241, no. 2, 27. https://doi.org/10.3847/1538-4365/ab06fc

Bilby : A user-friendly Bayesian inference library for gravitational-wave astronomy. / 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.

In: Astrophysical Journal, Supplement Series, Vol. 241, No. 2, 27, 01.04.2019.

Research output: Contribution to journalArticle

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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 - Vivanco, Francisco Hernandez

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

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

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U2 - 10.3847/1538-4365/ab06fc

DO - 10.3847/1538-4365/ab06fc

M3 - Article

VL - 241

JO - The Astrophysical Journal Supplement Series

JF - The Astrophysical Journal Supplement Series

SN - 0067-0049

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