Year of Graduation
GAN as an Approach for Fast Simulation of Showers Produced in Calorimeters for Experiments on Large Hadron Collider at CERN
Simulation is a key component of high energy physics. Historically simulation in particle physics relies on the Monte Carlo methods which require a tremendous amount of computation. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is 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 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.