A new framework to follow up candidates from continuous gravitational-wave searches

TL;DR

This paper introduces a flexible new framework using MCMC and nested sampling to improve follow-up analyses of continuous gravitational-wave candidates, expanding parameter space coverage and efficiency.

Contribution

It presents a novel, adaptable framework that surpasses previous tools by enabling diverse sampling algorithms and handling correlated priors for gravitational-wave candidate follow-up.

Findings

MCMC and nested sampling recover maximum posterior points in larger parameter spaces.

The framework effectively includes sources in binary systems.

It demonstrates improved computational efficiency and flexibility.

Abstract

Searches for continuous gravitational waves from unknown neutron stars are limited in sensitivity due to their high computational cost. For this reason, developing new methods or improving existing ones can increase the probability of making a detection. In this paper we present a new framework that uses MCMC or nested sampling methods to follow-up candidates of continuous gravitational-wave searches. This framework aims to go beyond the capabilities of PYFSTAT (which is limited to the PTEMCEE sampler), by allowing a flexible choice of sampling algorithm (using BILBY as a wrapper) and multi-dimensional correlated prior distributions. We show that MCMC and nested sampling methods can recover the maximum posterior point for much bigger parameter-space regions than previously thought (including for sources in binary systems), and we present tests that examine the capabilities of the new framework: a comparison between the DYNESTY, NESSAI, and PTEMCEE samplers, the usage of correlated priors, and its improved computational cost.