Coherent Bayesian analysis of inspiral signals

TL;DR

This paper introduces a Bayesian parameter estimation method using Markov chain Monte Carlo techniques for analyzing gravitational wave signals from binary inspirals, incorporating data from multiple detectors and estimating key source parameters.

Contribution

It presents a novel Bayesian analysis framework for inspiral signals that efficiently explores parameter space using MCMC, tailored for networked interferometric data.

Findings

Effective parameter estimation demonstrated with simulated data.

Method can handle signals from systems with masses up to 20 solar masses.

Provides insights into source localization and binary parameters.

Abstract

We present in this paper a Bayesian parameter estimation method for the analysis of interferometric gravitational wave observations of an inspiral of binary compact objects using data recorded simultaneously by a network of several interferometers at different sites. We consider neutron star or black hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and 2.5 PN in amplitude. Inference is facilitated using Markov chain Monte Carlo methods that are adapted in order to efficiently explore the particular parameter space. Examples are shown to illustrate how and what information about the different parameters can be derived from the data. This study uses simulated signals and data with noise characteristics that are assumed to be defined by the LIGO and Virgo detectors operating at their design sensitivities. Nine parameters are estimated, including those associated with the binary system, plus its location on the sky. We explain how this technique will be part of a detection pipeline for binary systems of compact objects with masses up to $20 \sunmass$, including cases where the ratio of the individual masses can be extreme.