Evolving N-body simulations to determine the origin and structure of the milky way galaxy’s halo using volunteer computing

Abstract—This work describes research done by the Milky-Way@Home project to use N-Body simulations to model the formation of the Milky Way Galaxy’s halo. While there have been previous efforts to use N-Body simulations to perform astronomical modeling, to our knowledge this is the first to use evolutionary algorithms to discover the initial parameters to the N-Body simulations so that they accurately model astronomical data. Performing a single 32,000 body simulation can take up to 200 hours on a typical processor, with an average of 15 hours. As optimizing the input parameters to these N-Body simulations typically takes at least 30,000 or more simulations, this work is made possible by utilizing the computing power of the 35,000 volunteered hosts at the MilkyWay@Home project, which are currently providing around 800 teraFLOPS. An open-source framework for generic distributed optimization (FGDO) is used to perform the evolutionary algorithms in conjunction the Berkeley Open Infrastructure for Network Computing (BOINC) which is used by many other volunteer computing projects, such as SETI@Home. The architecture of FGDO is described and results are presented which show that the evolutionary algorithms used correctly discover the initial parameters to N-Body simulations which match test data and find initial parameters which produce N-body simulations that accurately model tidal debris in the Milky Way Galaxy’s halo