  We introduce HAWCgen, a set of deep generative neural network models, which
are designed to supplement, or in some cases replace, parts of the simulation
pipeline for the High Altitude Water Cherenkov (HAWC) observatory. We show that
simple deep generative models replicate sampling of the reconstruction at a
near arbitrary speedup compared to the current simulation. Furthermore, we show
that generative models can offer a replacement to the detector simulation at a
comparable rate and quality to current methods. This work was done as part of
an undergraduate summer intern project at NVIDIA during the month of June,
2018.
