  Accurate simulation of physical processes is crucial for the success of
modern particle physics. However, simulating the development and interaction of
particle showers with calorimeter detectors is a time consuming process and
drives the computing needs of large experiments at the LHC and future
colliders. Recently, generative machine learning models based on deep neural
networks have shown promise in speeding up this task by several orders of
magnitude. We investigate the use of a new architecture -- the Bounded
Information Bottleneck Autoencoder -- for modelling electromagnetic showers in
the central region of the Silicon-Tungsten calorimeter of the proposed
International Large Detector. Combined with a novel second post-processing
network, this approach achieves 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. The results are
validated by comparing to established architectures. 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.
