PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals
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...
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| Veröffentlicht in: | Publications of the Astronomical Society of the Pacific 2019-02, Vol.131 (996), p.24503 |
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| container_issue | 996 |
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| container_title | Publications of the Astronomical Society of the Pacific |
| container_volume | 131 |
| creator | Biwer, C. M. Capano, Collin D. De, Soumi Cabero, Miriam Brown, Duncan A. Nitz, Alexander H. Raymond, V. |
| description | 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. |
| doi_str_mv | 10.1088/1538-3873/aaef0b |
| format | Article |
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| source | Jstor Complete Legacy; IOP Publishing Journals; Institute of Physics (IOP) Journals - HEAL-Link; Alma/SFX Local Collection |
| subjects | Astronomy ASTRONOMY AND ASTROPHYSICS Bayesian analysis Black holes Coalescence Gravitational waves methods: data analysis methods: statistical Parameter estimation |
| title | PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals |
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