Generative models for fast calorimeter simulation: the LHCb case
Published in EPJ Web of Conferences, 2019
Authors: V. Chekalina, E. Orlova3 , F. Ratnikov, D. Ulyanov3 , A. Ustyuzhanin, E. Zakharov
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL-LHC) needs, so the experiments are in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 orders of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a sufficient amount of simulated data needed by the next HL-LHC experiments using limited computing resources.