首页|New Findings from University of Hamburg in the Area of Machine Learning Publishe d (Refining fast simulation using machine learning)
New Findings from University of Hamburg in the Area of Machine Learning Publishe d (Refining fast simulation using machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from the University of Hambur g by NewsRx journalists, research stated, “At the CMS experiment, a growing reli ance on the fast Monte Carlo application (FastSim) will accompany the high lumin osity and detector granularity expected in Phase 2.” The news editors obtained a quote from the research from University of Hamburg: “The FastSim chain is roughly 10 times faster than the application based on the Geant4 detector simulation and full reconstruction referred to as FullSim. Howev er, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide p ost-hoc corrections to samples produced by the FastSim chain. The results show c onsiderably improved agreement with the FullSim output and an improvement in cor relations among output observables and external parameters.”
University of HamburgCyborgsEmerging TechnologiesMachine Learning
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