Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors

TL;DR

This paper introduces a Markov chain Monte Carlo method for coherent parameter estimation of binary inspiral signals using multiple gravitational wave detectors, improving analysis accuracy for detector networks.

Contribution

It presents a novel MCMC routine that jointly analyzes data from multiple interferometers to estimate binary system parameters, including sky location and masses.

Findings

Effective parameter estimation demonstrated with simulated LIGO and Virgo data.

Routine can be integrated into gravitational wave detection pipelines.

Uses advanced MCMC techniques like importance resampling and parallel tempering.

Abstract

Presented in this paper is a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using data from multiple detectors. The MCMC technique uses data from several interferometers and infers all nine of the parameters (ignoring spin) associated with the binary system, including the distance to the source, the masses, and the location on the sky. The Metropolis-algorithm utilises advanced MCMC techniques, such as importance resampling and parallel tempering. The data is compared with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. Our routine could be implemented as part of an inspiral detection pipeline for a world wide network of detectors. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors operating at their design sensitivity.