Three Dimensional Energy Parametrized Generative Adversarial Networks for Electromagnetic Shower Simulation

High Energy Physics (HEP) simulations are traditionally based on the Monte Carlo approach and generally rely on time consuming calculations. The present work investigates the use of Generative Adversarial Networks (GANs) as a fast alternative. Our approach treats the energy deposited by a particle inside a calorimeter detector as a three-dimensional image. True three-dimensional convolutions can be employed to capture the spatio-temporal correlation of shower energy depositions. Three-dimensional images are generated, conditioned on the energy of the incoming particle and validated against Monte Carlo simulation. The results show an agreement to full Mote Carlo simulations well within 10% thus proving that GAN can be used as a fast alternative for simulation of HEP detector response.