Data Grid Replica Optimisation and Using Artificial Neural Networks for Higgs Discovery at the LHC
نویسنده
چکیده
The next generation of international scientific projects poses problems for current computation and data handling techniques, and Grid computing is seen as a viable solution. Possible data handling strategies should be tested before choosing which is deployed on a real Grid; to this end, the Grid simulator OptorSim is presented. A selection of results for some job scheduling and data replication algorithms is given and in particular, the effects of background network traffic are examined. These show that the strategy chosen can significantly affect both resource usage and job turnaround time on the Grid. An analysis of the channel for Higgs discovery at the LHC, using artificial neural networks, is also presented, showing improved sensitivity from previous cuts-based analyses.
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