PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals

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...

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Bibliographic Details
Published inPublications of the Astronomical Society of the Pacific Vol. 131; no. 996; pp. 24503 - 24518
Main Authors Biwer, C. M., Capano, Collin D., De, Soumi, Cabero, Miriam, Brown, Duncan A., Nitz, Alexander H., Raymond, V.
Format Journal Article
LanguageEnglish
Published Philadelphia The Astronomical Society of the Pacific 01.02.2019
IOP Publishing
Astronomical Society of the Pacific (ASP)
Subjects
Astronomy
ASTRONOMY AND ASTROPHYSICS
Bayesian analysis
Black holes
Coalescence
Gravitational waves
methods: data analysis
methods: statistical
Parameter estimation
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Summary: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.
Bibliography:PASP-100629.R2
USDOE
LA-UR-18-26452
89233218CNA000001
National Science Foundation (NSF)
ISSN:0004-6280
1538-3873
DOI:10.1088/1538-3873/aaef0b