Bayesian Framework to Follow-up Continuous Gravitational Wave Candidates from Deep Surveys

TL;DR

This paper introduces a Bayesian framework for efficiently and automatically following up on candidates from deep all-sky searches for continuous gravitational waves, reducing human effort and improving sensitivity.

Contribution

It proposes a novel Bayesian-based follow-up method that automates and accelerates candidate verification in gravitational wave surveys.

Findings

Enables rapid, automated follow-up of gravitational wave candidates.

Reduces human labor in candidate verification process.

Improves the sensitivity and efficiency of continuous gravitational wave searches.

Abstract

Broad all-sky searches for continuous gravitational waves have high computational costs and require hierarchical pipelines. The sensitivity of these approaches is set by the initial search and by the number of candidates from that stage that can be followed up. The current follow-up schemes for the deepest surveys require careful tuning and set-up, have a significant human-labor cost and this impacts the number of follow-ups that can be afforded. Here we present and demonstrate a new follow-up framework based on Bayesian parameter estimation for the rapid, highly automated follow-up of candidates produced by the early stages of deep, wide-parameter space searches for continuous waves.