  In this paper, we show that a hybrid approach to generative modeling via
combining the decoder from an autoencoder together with an explicit generative
model for the latent space is a promising method for producing images of
particle trajectories in a liquid argon time projection chamber (LArTPC).
LArTPCs are a type of particle physics detector used by several current and
future experiments focused on studies of the neutrino. We implement a
Vector-Quantized Variational Autoencoder (VQ-VAE) and PixelCNN which produces
images with LArTPC-like features and introduce a method to evaluate the quality
of the images using a semantic segmentation that identifies important
physics-based features.
