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

2019; Institute of Physics; Volume: 131; Issue: 996 Linguagem: Inglês

10.1088/1538-3873/aaef0b

1538-3873

Christopher M. Biwer, C. D. Capano, D. DeBra, M. Cabero, D. Brown, A. Nitz, V. Raymond,

Geophysics and Gravity Measurements

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.