Inference on inspiral signals using LISA MLDC data

Rover, C. et al. (2007) Inference on inspiral signals using LISA MLDC data. Classical and Quantum Gravity, 24(19), S521-S527. (doi: 10.1088/0264-9381/24/19/S15)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Woan, Professor Graham and Messenger, Dr Christopher and Veitch, Dr John and Hendry, Professor Martin
Authors: Rover, C., Stroeer, A., Bloomer, E., Christensen, N., Clark, J., Hendry, M., Messenger, C., Meyer, R., Pitkin, M., Toher, J., Umstaetter, R., Vecchio, A., Veitch, J., and Woan, G.
College/School:College of Science and Engineering > School of Physics and Astronomy
Research Centre:College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research
Journal Name:Classical and Quantum Gravity
Publisher:Institute of Physics
ISSN (Online):1361-6382

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
459312Investigations in Gravitational Radiation.Sheila RowanScience & Technologies Facilities Council (STFC)ST/I001085/1Physics and Astronomy