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The Laser Interferometer Space Antenna (LISA) is a planned space-basedgravitational wave telescope with the goal of measuring gravitational waves inthe milli-Hertz frequency band, which is dominated by millions of Galacticbinaries. While some of these binaries produce signals that are loud enough tostand out and be extracted, most of them blur into a confusion foreground.Current methods for analyzing the full frequency band recorded by LISA toextract as many Galactic binaries as possible and to obtain Bayesian posteriordistributions for each of the signals are computationally expensive. Weintroduce a new approach to accelerate the extraction of the best fittingsolutions for Galactic binaries across the entire frequency band from data withmultiple overlapping signals. Furthermore, we use these best fitting solutionsto omit the burn-in stage of a Markov chain Monte Carlo method and to take fulladvantage of GPU-accelerated signal simulation, allowing us to computeposterior distributions in 2 seconds per signal on a laptop-grade GPU.

Accelerating global parameter estimation of gravitational waves from Galactic binaries using a genetic algorithm and GPUs