  Given the increasing data collection capabilities and limited computing
resources of future collider experiments, interest in using generative neural
networks for the fast simulation of collider events is growing. In our previous
study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for
generating photon showers in a high-granularity calorimeter showed a high
accuracy modeling of various global differential shower distributions. In this
work, we investigate how the BIB-AE encodes this physics information in its
latent space. Our understanding of this encoding allows us to propose methods
to optimize the generation performance further, for example, by altering latent
space sampling or by suggesting specific changes to hyperparameters. In
particular, we improve the modeling of the shower shape along the particle
incident axis.
