Inference on inspiral signals using LISA MLDC data

C Rover, A Stroeer, E Bloomer, N Christensen, J Clark, M Hendry, C Messenger, R Meyer, M Pitkin, J Toher, R Umstaetter, Alberto Vecchio, John Veitch, G Woan

Research output: Contribution to journalArticle

10 Citations (Scopus)


In this paper, we describe a Bayesian inference framework for the 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 nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.
Original languageEnglish
Pages (from-to)S521-S527
JournalClassical and Quantum Gravity
Issue number19
Early online date19 Sept 2007
Publication statusPublished - 7 Oct 2007


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