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, Alexander H. Nitz, V. Raymond

Research output: Contribution to journalArticle

25 Scopus citations

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 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.

Original languageEnglish (US)
Article number024503
JournalPublications of the Astronomical Society of the Pacific
Volume131
Issue number996
DOIs
StatePublished - Feb 1 2019

Keywords

  • Gravitational waves
  • Methods: data analysis
  • Methods: statistical
  • Online material: color figures

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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