2021/05/31 by Rodrigo Tenorio, R. Tenorio, D. Keitel +3 · 1 citation
Earth and Planetary Sciences · Physics and Astronomy · #Algorithm #Artificial intelligence #Astrophysics #Bayes factor #Bayes' theorem #Bayesian probability #Computer science #Cosmology and Gravitation Theories #Geophysics and Gravity Measurements #Gravitational wave #LIGO #Marginal likelihood #Markov chain Monte Carlo #Outlier #Physics #Pulsars and Gravitational Waves Research #astro-ph.IM #gr-qc
paper · pdf · doi:10.1103/physrevd.104.084012
published as Phys. Rev. D 104, 084012 (2021) · 23 pages, 15 figures, matches PRD published version
openalex created_date 2021/06/22 · arxiv created 2021/10/01 · openalex publication_date 2021/10/01 · arxiv updated 2021/10/04 · openalex updated_date 2026/08/06
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.