6th Inter-experiment Machine Learning Workshop

DeepTreeGANv2: Iterative Pooling of Point Clouds - Poster

31 Jan 2024, 17:10

5m

61/1-201 - Pas perdus - Not a meeting room - (CERN)

Poster (from contributed talk) 3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model Poster Session

Speaker

Mr Moritz Scham (Deutsches Elektronen-Synchrotron (DE))

Description

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 and CaloChallange datasets.
Two papers under review are available here:
https://cernbox.cern.ch/s/WoNDFxdOiKpg0fG

Presentation materials

poster.pdf