Speaker
Description
In this talk, we investigate the use of Generative Adversarial Networks (GANs) and a new architecture -- the Bounded Information Bottleneck Autoencoder (Bib-AE) -- for modeling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. An accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full GEANT4 simulation for a high-granularity calorimeter with 27k simulated channels have been achieved. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy. Furthermore, a detailed investigation of the latent space encoded by Bib-AE has been carried out.
