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We report on the performance of an end-to-end Bayesian analysis pipeline fordetecting and characterizing galactic binary signals in simulated LISA data.Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM)algorithm, which has been optimized to search for tens of thousands ofoverlapping signals across the LISA band. The BAM algorithm employs Bayesianmodel selection to determine the number of resolvable sources, and providesposterior distribution functions for all the model parameters. The BAMalgorithm performed almost flawlessly on all the Round 1 Mock LISA DataChallenge data sets, including those with many highly overlapping sources. Theonly misses were later traced to a coding error that affected high frequencysources. In addition to the BAM algorithm we also successfully tested a GeneticAlgorithm (GA), but only on data sets with isolated signals as the GA has yetto be optimized to handle large numbers of overlapping signals.

Extracting galactic binary signals from the first round of Mock LISA Data Challenges