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

C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown +2 more
7/26/2018
astro-ph.IMastro-ph.GAgr-qc

Official code: gwastro/pycbc-inference-paper (8 stars, last commit 2018-12-07).

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.

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Code Implementations(1)

gwastro/pycbc-inference-paperOfficial100%

Data Release associated with the PyCBC Inference paper. This includes posterior samples for GW150914, LVT151012, and GW151226 along with configuration files and run scripts,

85Shell, Jupyter NotebookJul 10, 20187 years ago
bayesian-inferenceblack-holedata-releasegravitational-wavesgw150914+5 more

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Cite this paper

@article{biwer2018pycbc,
  title  = {PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals},
  author = {C. M. Biwer and Collin D. Capano and Soumi De and Miriam Cabero and Duncan A. Brown and Alexander H. Nitz and V. Raymond},
  year   = {2018},
  eprint = {1807.10312},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1807.10312}
}

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