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Poster
in
Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

DeepTreeGANv2: Iterative Pooling of Point Clouds

Moritz A.W. Scham · Dirk Kr├╝cker · Kerstin Borras


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.

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