2008/07/28 by John Veitch, Alberto Vecchio · 50 citations
Computer Science · Physics and Astronomy · #Bayesian inference #Bayesian probability #Binary number #Coalescence (physics) #Gaussian Processes and Bayesian Inference #Gravitational wave #Model selection #Probabilistic logic #Pulsars and Gravitational Waves Research #Selection (genetic algorithm) #Statistical Mechanics and Entropy #Waveform #gr-qc
paper · pdf · doi:10.1088/0264-9381/25/18/184010
published in Classical and Quantum Gravity 25(18), 184010 (IOP Publishing) · From the Proceedings of the 11th Gravitational Wave Data Analysis Workshop 11 pages, 4 pages
arxiv created 2008/07/28 · openalex publication_date 2008/09/02 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Bayesian model selection provides a powerful and mathematically transparent framework to tackle hypothesis testing, such as detection tests of gravitational waves emitted during the coalescence of binary systems using ground-based laser interferometers. Although its implementation is computationally intensive, we have developed an efficient probabilistic algorithm based on a technique known as nested sampling that makes Bayesian model selection applicable to follow-up studies of candidate signals produced by on-going searches of inspiralling compact binaries. We discuss the performance of this approach, in terms of 'false alarm rate' and 'detection probability' of restricted second post-Newtonian inspiral waveforms from non-spinning compact objects in binary systems. The results confirm that this approach is a viable tool for detection tests in current searches for gravitational wave signals.