Inference on inspiral signals using LISA MLDC data

Röver, Christian and Stroeer, Alexander and Bloomer, Ed and Christensen, Nelson and Clark, James and Hendry, Martin and Messenger, Chris and Meyer, Renate and Pitkin, Matthew and Toher, Jennifer and Umstätter, Richard and Vecchio, Alberto and Veitch, John and Woan, Graham (2007) Inference on inspiral signals using LISA MLDC data. Classical and Quantum Gravity, 24 (19). S521-S527. ISSN 0264-9381

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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.

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Journal Article
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Classical and Quantum Gravity
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30 Jul 2019 13:45
Last Modified:
22 Mar 2022 02:23