Application of a hierarchical MCMC follow-up to Advanced LIGO continuous gravitational-wave candidates

TL;DR

This paper introduces a hierarchical MCMC follow-up method for continuous gravitational-wave candidates, demonstrating its effectiveness on real LIGO data and distinguishing astrophysical signals from instrumental artifacts.

Contribution

It presents the first application of hierarchical MCMC for continuous gravitational-wave candidate follow-up, including a novel Bayes factor for stage comparison and validation on real data.

Findings

Most outliers are inconsistent with astrophysical signals.

Four outliers are identified as instrumental artifacts.

One outlier's origin remains uncertain, possibly instrumental.

Abstract

We present the first application of a hierarchical Markov Chain Monte Carlo (MCMC) follow-up on continuous gravitational-wave candidates from real-data searches. The follow-up uses an MCMC sampler to draw parameter-space points from the posterior distribution, constructed using the matched-filter as a log-likelihood. As outliers are narrowed down, coherence time increases, imposing more restrictive phase-evolution templates. We introduce a novel Bayes factor to compare results from different stages: The signal hypothesis is derived from first principles, while the noise hypothesis uses extreme value theory to derive a background model. The effectiveness of our proposal is evaluated on fake Gaussian data and applied to a set of 30 outliers produced by different continuous wave searches on O2 Advanced LIGO data. The results of our analysis suggest all but five outliers are inconsistent with an astrophysical origin under the standard continuous wave signal model. We successfully ascribe four of the surviving outliers to instrumental artifacts and a strong hardware injection present in the data. The behavior of the fifth outlier suggests an instrumental origin as well, but we could not relate it to any known instrumental cause.