Generative Models for Fast Calorimeter Simulation.LHCb case
This addresses the urgent need for fast simulation techniques in the High Luminosity Large Hadron Collider experiments, enabling physicists to generate sufficient simulated data with limited computing resources.
The paper tackles the computational bottleneck of Monte Carlo simulation in high energy physics by introducing a Deep Learning framework based on Generative Adversarial Networks, achieving a speedup of 5 orders of magnitude with reasonable accuracy.
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) 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.