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:
-
[1];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Syracuse Univ., Syracuse, NY (United States)
- Max-Planck-Institut für Gravitationsphysik, Hannover (Germany)
- Syracuse Univ., Syracuse, NY (United States)
- Max-Planck-Institut für Gravitationsphysik, Potsdam (Germany); Cardiff Univ., Wales (United Kingdom)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (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. https://doi.org/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. https://doi.org/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 = {Fri Jan 11 00:00:00 EST 2019},
month = {Fri Jan 11 00:00:00 EST 2019}
}
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