Inference on inspiral signals using LISA MLDC data

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dc.creatorRöver, Christian
dc.creatorStroeer, Alexander
dc.creatorBloomer, Ed
dc.creatorChristensen, Nelson
dc.creatorClark, James
dc.creatorHendry, Martin
dc.creatorMessenger, Chris
dc.creatorMeyer, Renate
dc.creatorPitkin, Matt
dc.creatorToher, Jennifer
dc.creatorUmstätter, Richard
dc.creatorVecchio, Alberto
dc.creatorVeitch, John
dc.creatorWoan, Graham
dc.date2007-07-26
dc.date2007-08-05
dc.date.accessioned2026-07-07T10:56:55Z
dc.date.available2026-07-07T10:56:55Z
dc.descriptionIn this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.
dc.descriptionAccepted for publication in Classical and Quantum Gravity, GWDAW-11 special issue
dc.identifierhttps://arxiv.org/abs/0707.3969
dc.identifierhttp://arxiv.org/abs/0707.3969
dc.identifierClass.Quant.Grav.24:S521-S528,2007
dc.identifierdoi:10.1088/0264-9381/24/19/S15
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/186580
dc.subjectGeneral Relativity and Quantum Cosmology
dc.titleInference on inspiral signals using LISA MLDC data
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