Journal
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
Volume 131, Issue 996, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1538-3873/aaef0b
Keywords
gravitational waves; methods: data analysis; methods: statistical
Categories
Funding
- NSF [PHY-1404395, PHY-1707954, PHY-1607169, OAC-1541396]
- Inaugural Kathy '73 and Stan '72 Walters Endowed Fund for Science Research graduate fellowship at Syracuse University
- Max Planck Gesellschaft
- Australian Research Council
- STFC [ST/N005430/1] Funding Source: UKRI
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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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