https://doi.org/10.1051/epjconf/202429509010
DeepTreeGAN: Fast Generation of High Dimensional Point Clouds
1 Deutsches Elektronen-Synchrotron DESY, Germany
2 Jülich Supercomputing Centre, Institute for Advanced Simulation, Germany
3 RWTH Aachen University, III. Physikalisches Institut A, Germany
4 Universität Hamburg, Institut für Experimentalphysik, Germany
* e-mail: moritz.scham@desy.de
Published online: 6 May 2024
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
