Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets

Stroeer, A. et al. (2007) Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets. Classical and Quantum Gravity, 24, S541-S549. (doi:10.1088/0264-9381/24/19/S17)

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Abstract

We report on the analysis of selected single source data sets from the first round of the mock LISA data challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pipeline consisting of a grid-based coherent pre-processing unit for signal detection and an automatic Markov Chain Monte Carlo (MCMC) post-processing unit for signal evaluation. We demonstrate that signal detection with our coherent approach is secure and accurate, and is increased in accuracy and supplemented with additional information on the signal parameters by our Markov Chain Monte Carlo approach. We also demonstrate that the Markov Chain Monte Carlo routine is additionally able to determine accurately the noise level in the frequency window of interest.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Messenger, Dr Christopher and Veitch, Dr John and Hendry, Professor Martin and Woan, Professor Graham and Pitkin, Dr Matthew
Authors: Stroeer, A., Veitch, J., Roever, C., Bloomer, E., Clark, J., Christensen, N., Hendry, M., Messenger, C., Meyer, R., Pitkin, M., Toher, J., Umstaetter, R., Vecchio, A., and Woan, G.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Classical and Quantum Gravity
Publisher:Institute of Physics
ISSN:0264-9381
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