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

MPG-Autoren
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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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Zitation

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.


Zitierlink: https://hdl.handle.net/21.11116/0000-0002-DEC9-2
Zusammenfassung
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.