  The Generative Adversarial Network (GAN) is a powerful and flexible tool that
can generate high-fidelity synthesized data by learning. It has seen many
applications in simulating events in High Energy Physics (HEP), including
simulating detector responses and physics events. However, training GANs is
notoriously hard and optimizing their hyperparameters even more so. It normally
requires many trial-and-error training attempts to force a stable training and
reach a reasonable fidelity. Significant tuning work has to be done to achieve
the accuracy required by physics analyses. This work uses the physics-agnostic
and high-performance-computer-friendly hyperparameter optimization tool HYPPO
to optimize and examine the sensitivities of the hyperparameters of a GAN for
two independent HEP datasets. This work provides the first insights into
efficiently tuning GANs for Large Hadron Collider data. We show that given
proper hyperparameter tuning, we can find GANs that provide high-quality
approximations of the desired quantities. We also provide guidelines for how to
go about GAN architecture tuning using the analysis tools in HYPPO.
