Title: Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters,
Abstract: High-precision modeling of subatomic particle interactions is critical for
many fields within the physical sciences, such as nuclear physics and high
energy particle physics. Most simulation pipelines in the sciences are
computationally intensive -- in a variety of scientific fields, Generative
Adversarial Networks have been suggested as a solution to speed up the forward
component of simulation, with promising results. An important component of any
simulation system for the sciences is the ability to condition on any number of
physically meaningful latent characteristics that can effect the forward
generation procedure. We introduce an auxiliary task to the training of a
Generative Adversarial Network on particle showers in a multi-layer
electromagnetic calorimeter, which allows our model to learn an attribute-aware
conditioning mechanism.