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Journal Article

PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals

MPS-Authors
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Capano,  Collin
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Cabero,  Miriam
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Nitz,  Alexander H.
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

/persons/resource/persons192117

Raymond,  Vivien
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Fulltext (public)

1807.10312.pdf
(Preprint), 8MB

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Citation

Biwer, C. M., Capano, C., De, S., Cabero, M., Brown, D. A., Nitz, A. H., et al. (2019). PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals. Publications of the Astronomical Society of the Pacific, 131(996): 024503. doi:10.1088/1538-3873/aaef0b.


Cite as: http://hdl.handle.net/21.11116/0000-0002-DEC9-2
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 posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.