Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors

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dc.creatorRöver, Christian
dc.creatorMeyer, Renate
dc.creatorChristensen, Nelson
dc.date2006-09-28
dc.date2007-07-26
dc.date.accessioned2026-07-07T10:24:31Z
dc.date.available2026-07-07T10:24:31Z
dc.descriptionPresented in this paper is a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using data from multiple detectors. The MCMC technique uses data from several interferometers and infers all nine of the parameters (ignoring spin) associated with the binary system, including the distance to the source, the masses, and the location on the sky. The Metropolis-algorithm utilises advanced MCMC techniques, such as importance resampling and parallel tempering. The data is compared with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. Our routine could be implemented as part of an inspiral detection pipeline for a world wide network of detectors. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors operating at their design sensitivity.
dc.description10 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/gr-qc/0609131
dc.identifierhttp://arxiv.org/abs/gr-qc/0609131
dc.identifierPhys.Rev.D75:062004,2007
dc.identifierdoi:10.1103/PhysRevD.75.062004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/176218
dc.subjectGeneral Relativity and Quantum Cosmology
dc.titleCoherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors
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