Generative Models for Fast Calorimeter Simulation

EPJ Web of Conferences. 2019. Vol. 2014. P. 1–8.

Ratnikov F., Ustyuzhanin A.

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.

Keywords: simulation, HEP, GAN