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.
@article{arxiv.2311.12616,
title = {DeepTreeGAN: Fast Generation of High Dimensional Point Clouds},
author = {Moritz Alfons Wilhelm Scham and Dirk Krücker and Benno Käch and Kerstin Borras},
journal= {arXiv preprint arXiv:2311.12616},
year = {2023}
}