Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

TL;DR

This paper introduces an advanced MCMC-based method for estimating parameters of binary neutron star inspiral signals in gravitational wave data, improving efficiency with novel techniques and demonstrating its application on simulated data.

Contribution

It presents a new MCMC routine with importance resampling and detection probability priors for gravitational wave parameter estimation.

Findings

Efficient parameter estimation for binary inspiral signals.

Implementation of advanced MCMC techniques.

Successful application on simulated interferometric data.

Abstract

Presented is a description of a Markov chain Monte Carlo (MCMC) parameter estimation routine for use with interferometric gravitational radiational data in searches for binary neutron star inspiral signals. Five parameters associated with the inspiral can be estimated, and summary statistics are produced. Advanced MCMC methods were implemented, including importance resampling and prior distributions based on detection probability, in order to increase the efficiency of the code. An example is presented from an application using realistic, albeit fictitious, data.