Hierarchical multi-stage MCMC follow-up of continuous gravitational wave candidates

TL;DR

This paper presents a hierarchical MCMC-based method for follow-up and parameter estimation of continuous gravitational wave candidates, improving search efficiency and accuracy in noisy data.

Contribution

It introduces a novel hierarchical MCMC approach tailored for continuous gravitational wave candidate follow-up, with tools to manage parameter space effectively.

Findings

Method approaches theoretical optimal performance in simulations

Demonstrates effective parameter estimation for continuous wave sources

Provides a framework for controlling parameter space size

Abstract

Leveraging Markov chain Monte Carlo (MCMC) optimization of the F-statistic, we introduce a method for the hierarchical follow-up of continuous gravitational wave candidates identified by wide-parameter space semi-coherent searches. We demonstrate parameter estimation for continuous wave sources and develop a framework and tools to understand and control the effective size of the parameter space, critical to the success of the method. Monte Carlo tests of simulated signals in noise demonstrate that this method is close to the theoretical optimal performance.