  Recently, Generative Adversarial Networks (GANs) trained on samples of
traditionally simulated collider events have been proposed as a way of
generating larger simulated datasets at a reduced computational cost. In this
paper we point out that data generated by a GAN cannot statistically be better
than the data it was trained on, and critically examine the applicability of
GANs in various situations, including a) for replacing the entire Monte Carlo
pipeline or parts of it, and b) to produce datasets for usage in highly
sensitive analyses or sub-optimal ones. We present our arguments using
information theoretic demonstrations, a toy example, as well as in the form of
a formal statement, and identify some potential valid uses of GANs in collider
simulations.
