Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations

Understand

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid.

• This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations.
• Deep Learning Generative Adversarial Networks are among the most promising alternatives.
• Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes.