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

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. Here, 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.

Authors:
ORCiD logo [1];  [2];  [3];  [2];  [3];  [2];  [4]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Syracuse Univ., Syracuse, NY (United States)
  2. Max-Planck-Institut für Gravitationsphysik, Hannover (Germany)
  3. Syracuse Univ., Syracuse, NY (United States)
  4. Max-Planck-Institut für Gravitationsphysik, Potsdam (Germany); Cardiff Univ., Wales (United Kingdom)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Science Foundation (NSF); USDOE
OSTI Identifier:
1542829
Report Number(s):
LA-UR-18-26452
Journal ID: ISSN 0004-6280
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Publications of the Astronomical Society of the Pacific
Additional Journal Information:
Journal Volume: 131; Journal Issue: 996; Journal ID: ISSN 0004-6280
Publisher:
Astronomical Society of the Pacific (ASP)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; gravitational waves; methods: data analysis; methods: statistical

Citation Formats

Biwer, Christopher Michael, Capano, Collin D., De, Soumi, Cabero, Miriam, Brown, Duncan A., Nitz, Alexander H., and Raymond, Vivien. PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals. United States: N. p., 2019. Web. doi:10.1088/1538-3873/aaef0b.
Biwer, Christopher Michael, Capano, Collin D., De, Soumi, Cabero, Miriam, Brown, Duncan A., Nitz, Alexander H., & Raymond, Vivien. PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals. United States. doi:10.1088/1538-3873/aaef0b.
Biwer, Christopher Michael, Capano, Collin D., De, Soumi, Cabero, Miriam, Brown, Duncan A., Nitz, Alexander H., and Raymond, Vivien. Fri . "PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals". United States. doi:10.1088/1538-3873/aaef0b. https://www.osti.gov/servlets/purl/1542829.
@article{osti_1542829,
title = {PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals},
author = {Biwer, Christopher Michael and Capano, Collin D. and De, Soumi and Cabero, Miriam and Brown, Duncan A. and Nitz, Alexander H. and Raymond, Vivien},
abstractNote = {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. Here, 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.},
doi = {10.1088/1538-3873/aaef0b},
journal = {Publications of the Astronomical Society of the Pacific},
number = 996,
volume = 131,
place = {United States},
year = {2019},
month = {1}
}

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Cited by: 14 works
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