Modelization of an injector with machine learning

Research output: Contribution to report/book/conference proceedingsIn-proceedings paperpeer-review

Authors

  • Angélique Gatera
  • Mathieu Debongnie
  • Frédéric Bouly
  • Maud Baylac
  • Nicolas Chauvin
  • Didier Uriot

Institutes & Expert groups

  • CNRS/IN2P3 - Université Grenoble Alpes - LPSC
  • CEA Saclay - Commissariat à l'énergie atomique
  • CEA Saclay - IRFU-SPhN

Documents & links

Abstract

Modern particle accelerator projects, such as the accelerator for the Multi-purpose hYbrid Research Reactor for High-tech Application (MYRRHA) project driven by the SCK*CEN in Belgium, have very high stability and/or reliability requirements. This means that new strategies for the control systems have to be developed. For that, having faster beam dynamics simulation could prove to be helpful. In this paper, we report the training of neural networks to model key properties of the beam in the MYRRHA injector as well as in IPHI (“Injecteur de Proton à Haute Intensité”). The trained models are shown to be able to reproduce the general behaviours of the machines while requiring a very low computation time.

Details

Original languageEnglish
Title of host publication10th International Particle Accelerator Conference
Subtitle of host publicationIPAC2019, Melbourne, Australia
PublisherJACoW Publishing
Pages3096-3099
Number of pages4
ISBN (Electronic)978-3-95450-208-0
DOIs
Publication statusPublished - 23 May 2019
Event2019 - IPAC: 10th International Particle Accelerator Conference - Melbourne convention & exhibition centre, Melbourne, Australia
Duration: 19 May 201924 May 2019
https://ipac19.org/
https://ipac19.org

Conference

Conference2019 - IPAC
Abbreviated titleIPAC2019
Country/TerritoryAustralia
CityMelbourne
Period2019-05-192019-05-24
Internet address

Keywords

  • Machine Learning, neural network, MINERVA, injector transmission

ID: 6871827