  In High Energy Physics, detailed and time-consuming simulations are used for
particle interactions with detectors. To bypass these simulations with a
generative model, the generation of large point clouds in a short time is
required, while the complex dependencies between the particles must be
correctly modelled. Particle showers are inherently tree-based processes, as
each particle is produced by the decay or detector interaction of a particle of
the previous generation. In this work, we present a novel Graph Neural Network
model (DeepTreeGAN) that is able to generate such point clouds in a tree-based
manner. We show that this model can reproduce complex distributions, and we
evaluate its performance on the public JetNet dataset.
