PyCBC inference: a python-based parameter estimation toolkit for compact binary coalescence signals

C. M. Biwer; Collin D. Capano; Soumi De; Miriam Cabero; Duncan A. Brown; Alexander H. Nitz; V. Raymond

doi:10.1088/1538-3873/aaef0b

PyCBC inference: a python-based parameter estimation toolkit for compact binary coalescence signals

C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown, Alexander H. Nitz, V. Raymond

Department of Physics

Research output: Contribution to journal › Article › peer-review

172 Scopus citations

Abstract

We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters’ posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.

Original language	English (US)
Article number	024503
Journal	Publications of the Astronomical Society of the Pacific
Volume	131
Issue number	996
DOIs	https://doi.org/10.1088/1538-3873/aaef0b
State	Published - Feb 1 2019

Keywords

Gravitational waves
Methods: data analysis
Methods: statistical
Online material: color figures

ASJC Scopus subject areas

Astronomy and Astrophysics
Space and Planetary Science

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10.1088/1538-3873/aaef0b

Cite this

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title = "PyCBC inference: a python-based parameter estimation toolkit for compact binary coalescence signals",

abstract = "We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters{\textquoteright} posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.",

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author = "Biwer, {C. M.} and Capano, {Collin D.} and Soumi De and Miriam Cabero and Brown, {Duncan A.} and Nitz, {Alexander H.} and V. Raymond",

year = "2019",

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AU - Biwer, C. M.

AU - Capano, Collin D.

AU - De, Soumi

AU - Cabero, Miriam

AU - Brown, Duncan A.

AU - Nitz, Alexander H.

AU - Raymond, V.

PY - 2019/2/1

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N2 - We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters’ posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.

AB - We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters’ posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.

KW - Gravitational waves

KW - Methods: data analysis

KW - Methods: statistical

KW - Online material: color figures

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M3 - Article

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JO - Publications of the Astronomical Society of the Pacific

JF - Publications of the Astronomical Society of the Pacific

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M1 - 024503

ER -

PyCBC inference: a python-based parameter estimation toolkit for compact binary coalescence signals

Abstract

Keywords

ASJC Scopus subject areas

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