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

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

  • Alexander Stroeer
  • John Veitch
  • Christian Röver
  • Ed Bloomer
  • James Clark
  • Nelson Christensen
  • Martin Hendry
  • Chris Messenger
  • Renate Meyer
  • Matthew Pitkin
  • Jennifer Toher
  • Richard Umstätter
  • Alberto Vecchio
  • Graham Woan
Publication date1/10/2007
Number of pages9
<mark>Original language</mark>English


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.