2019
Biwer, Christoper M.; Capano, Collin; De, Soumi; Cabero, Miriam; Brown, Duncan; Nitz, Alexander; Raymond, Vivien
PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals Journal Article
In: Publications of the Astronomical Society of the Pacific, vol. 131, no. 996, pp. 024503, 2019.
Abstract | Links | BibTeX | Tags: Data Analysis, gravitational waves - methods, statistical
@article{1538-3873-131-996-024503,
title = {PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals},
author = {Christoper M. Biwer and Collin Capano and Soumi De and Miriam Cabero and Duncan Brown and Alexander Nitz and Vivien Raymond},
url = {http://stacks.iop.org/1538-3873/131/i=996/a=024503},
year = {2019},
date = {2019-01-01},
journal = {Publications of the Astronomical Society of the Pacific},
volume = {131},
number = {996},
pages = {024503},
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.},
keywords = {Data Analysis, gravitational waves - methods, statistical},
pubstate = {published},
tppubtype = {article}
}
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.
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1.
Biwer, Christoper M.; Capano, Collin; De, Soumi; Cabero, Miriam; Brown, Duncan; Nitz, Alexander; Raymond, Vivien
PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals Journal Article
In: Publications of the Astronomical Society of the Pacific, vol. 131, no. 996, pp. 024503, 2019.
@article{1538-3873-131-996-024503,
title = {PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals},
author = {Christoper M. Biwer and Collin Capano and Soumi De and Miriam Cabero and Duncan Brown and Alexander Nitz and Vivien Raymond},
url = {http://stacks.iop.org/1538-3873/131/i=996/a=024503},
year = {2019},
date = {2019-01-01},
journal = {Publications of the Astronomical Society of the Pacific},
volume = {131},
number = {996},
pages = {024503},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.