PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals

TL;DR

PyCBC Inference is a new Python toolkit extension that performs Bayesian parameter estimation for gravitational wave signals from binary mergers, providing accurate and unbiased results validated against real LIGO-Virgo data.

Contribution

The paper introduces new Bayesian inference modules in PyCBC, enhancing gravitational wave data analysis with open-source, validated tools for binary merger parameter estimation.

Findings

Unbiased parameter estimates for simulated binary black hole populations

Good agreement with LIGO-Virgo published results

Open-source toolkit facilitates gravitational wave data analysis

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 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.