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We report on the analysis of selected single source data sets from the firstround of the Mock LISA Data Challenges (MLDC) for white dwarf binaries. Weimplemented an end-to-end pipeline consisting of a grid-based coherentpre-processing unit for signal detection, and an automatic Markov Chain MonteCarlo post-processing unit for signal evaluation. We demonstrate that signaldetection with our coherent approach is secure and accurate, and is increasedin accuracy and supplemented with additional information on the signalparameters by our Markov Chain Monte Carlo approach. We also demonstrate thatthe Markov Chain Monte Carlo routine is additionally able to determineaccurately the noise level in the frequency window of interest.

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