Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data

TL;DR

This paper introduces a Bayesian MCMC approach, using the Gibbs sampler, to estimate parameters of binary systems from gravitational wave data, demonstrating its effectiveness in noisy environments.

Contribution

It applies the Gibbs sampler MCMC method to gravitational wave data analysis for the first time, showing its potential for parameter estimation in noisy signals.

Findings

Successful detection and analysis of signals within noisy data

Demonstration of Gibbs sampler effectiveness for gravitational wave parameter estimation

Potential for improved Bayesian inference in gravitational wave astronomy

Abstract

We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate the potential that MCMC techniques may hold for the computation of posterior distributions of parameters of the binary system that created the gravity radiation signal. We describe the use of the Gibbs sampler method, and present examples whereby signals are detected and analyzed from within noisy data.