PyCBC Inference：一个基于Python的紧凑二进制聚结信号参数估计工具包
PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals
摘要：
我们在开源PyCBC引力波天文工具包中引入了新的模块，这些模块实现了用于紧凑对象二进制合并的贝叶斯推断。我们审查的贝叶斯推断方法实现和描述的模块的结构。我们表明，PyCBC推理模块产生无偏估计的参数的模拟人口的二进制黑洞合并。我们表明，后验参数分布使用我们的新代码同意与发表的估计，在第一个LIGO-处女座观测运行的二进制黑洞。
英文摘要：
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 posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.
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