Modelization of an injector with machine learning

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

    Research outputpeer-review


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


    Conference2019 - IPAC
    Abbreviated titleIPAC2019
    Internet address

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