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

We report on the performance of an end-to-end Bayesian analysis pipeline for detecting 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 of overlapping signals across the LISA band. The BAM algorithm employs Bayesian model selection to determine the number of resolvable sources, and provides posterior density functions for all the model parameters. The BAM algorithm performed almost flawlessly on all the round 1 Mock LISA Data Challenge data sets, including those with many highly overlapping sources. Some misses were later traced to a particular flaw in the coding that affected high frequency sources. In addition to the BAM algorithm we also successfully tested a genetic algorithm (GA), but only on data sets with isolated signals as the GA has yet to be optimized to handle large numbers of overlapping signals