In this video from PASC18, Maurizio Pierini from CERN presents: Generative Models for Application-Specific Fast Simulation of LHC Collision Events.
“We investigate the possibility of using generative models (e.g., GANs and variational autoencoders) as analysis-specific data augmentation tools to increase the size of the simulation data used by the LHC experiments. With the LHC entering its high-luminosity phase in 2025, the projected computing resources will not be able to sustain the demand for simulated events. Generative models are already investigated as the mean to speed up the centralized simulation process. Here we propose to investigate a different strategy: training deep networks to generate small-dimension ntuples of numbers (physics quantities such as reconstructed particle energy and direction), learning the distribution of these quantities from a sample of simulated data. In one step, one would then be able to generate the outcome of the full processing workflow (generation + simulation + reconstruction + selection).
Co-Author(s): Dominick Olivito, Bobak Hashemi, Nick Amin (UC San Diego)
Thanks to Rich Brueckner from insideHPC Media Publications for recording the video.