2018/05/08 by G. Ashton, Gregory Ashton, Reinhard Prix +3 · 1 citation
Earth and Planetary Sciences · Physics and Astronomy · #Algorithm #Artificial intelligence #Astronomy #Computer science #Detector #Geophysics and Gravity Measurements #Glitch #Gravitational wave #Identification (biology) #Inference #Mechanics #Neutron star #Nuclear Physics and Applications #Optics #Physics #Pulsars and Gravitational Waves Research #Rotational symmetry #SIGNAL (programming language) #astro-ph.HE #gr-qc
paper · pdf · doi:10.1103/physrevd.98.063011
published as Phys. Rev. D 98, 063011 (2018) · 9 pages, 6 figures, 2 tables
arxiv created 2018/05/08 · openalex created_date 2018/05/17 · openalex publication_date 2018/09/20 · arxiv updated 2018/09/26 · openalex updated_date 2026/08/05
Isolated nonaxisymmetric rotating neutron stars producing continuous-gravitational-wave signals may undergo occasional spin-up events known as glitches. If unmodeled by a search, these glitches can result in continuous wave signals being missed or misidentified as detector artifacts. We outline a semicoherent glitch-robust search method that allows identification of continuous wave signal candidates that contain glitches and inferences about the model parameters. We demonstrate how this can be applied to the follow-up of candidates found by wide-parameter space searches. We find that a Markov chain Monte Carlo method outperforms a grid-based method in speed and accuracy.