Physics-aware modelling of an accelerated particle cloud - Institut de Physique Nucléaire d'Orsay Access content directly
Conference Papers Year : 2023

Physics-aware modelling of an accelerated particle cloud

Abstract

Particle accelerator simulators, pivotal for acceleration optimization, are computationally heavy; surrogate, machine learning-based models are thus trained to facilitate the accelerator fine-tuning. While these current models are efficient, they do not allow for simulating the beam at the individual particle-level. This paper adapts point cloud deep learning methods, developed for computer vision, to model particle beams.
Fichier principal
Vignette du fichier
ML4PS.pdf (729.75 Ko) Télécharger le fichier
Origin Files produced by the author(s)
licence
Public Domain

Dates and versions

hal-04396175 , version 1 (16-01-2024)

Licence

Public Domain

Identifiers

  • HAL Id : hal-04396175 , version 1

Cite

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag. Physics-aware modelling of an accelerated particle cloud. MLPS 2023 - Machine Learning and the Physical Sciences Workshop 23023 - At the 37th conference on Neural Information Processing Systems (NeurIPS), Dec 2023, New Orleans, United States. ⟨hal-04396175⟩
93 View
49 Download

Share

Gmail Mastodon Facebook X LinkedIn More