English

DeepTreeGANv2: Iterative Pooling of Point Clouds

Data Analysis, Statistics and Probability 2024-01-03 v2 Machine Learning High Energy Physics - Experiment

Abstract

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 significant extension to DeepTreeGAN, featuring a critic, that is able to aggregate such point clouds iteratively in a tree-based manner. We show that this model can reproduce complex distributions, and we evaluate its performance on the public JetNet 150 dataset.

Keywords

Cite

@article{arxiv.2312.00042,
  title  = {DeepTreeGANv2: Iterative Pooling of Point Clouds},
  author = {Moritz Alfons Wilhelm Scham and Dirk Krücker and Kerstin Borras},
  journal= {arXiv preprint arXiv:2312.00042},
  year   = {2024}
}

Comments

arXiv admin note: substantial text overlap with arXiv:2311.12616

R2 v1 2026-06-28T13:37:32.414Z