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DOE OSTI · 1542829

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

Biwer, Christopher Michael·Capano, Collin D.·De, Soumi·Cabero, Miriam·Brown, Duncan A.·Nitz, Alexander H.·Raymond, Vivien
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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. Here, 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.

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79 ASTRONOMY AND ASTROPHYSICSgravitational wavesmethods: data analysismethods: statistical

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Biwer, Christopher Michael, Capano, Collin D., De, Soumi, Cabero, Miriam, Brown, Duncan A., Nitz, Alexander H., Raymond, Vivien. 2019-01-11. PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals. https://doi.org/10.1088/1538-3873%2Faaef0b

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