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URLhttps://doi.org/10.18429/JACoW-IPAC2024-THPR35
TitleOptimizing non-linear kicker injection parameters using machine learning
Authors
  • A. Schuett
    Munich University of Technology
  • C. Knochenhauer
    Technical University of Munich
  • M. McAteer, P. Schnizer
    Helmholtz-Zentrum Berlin für Materialien und Energie GmbH
AbstractSynchrotron light source storage rings aim to maintain a continuous beam current without observable beam motion during injection. One element that paves the way to this target is the non-linear kicker (NLK). The field distribution it generates poses challenges for optimizing the topping-up operation. Within this study, a reinforcement learning agent was developed and trained to optimize the NLK operation parameters. We present the models employed, the optimization process, and the achieved results.
Paperdownload: THPR35.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference15th International Particle Accelerator Conference
Series
LocationNashville, TN
Date19-24 May 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardFulvia Pilat - Oak Ridge National Laboratory Wolfram Fischer - Brookhaven National Laboratory Robert Saethre - Oak Ridge National Laboratory Petr Anisimov - Los Alamos National Laboratory Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A.
Online ISBN978-3-95450-247-9
Online ISSN2673-5490
Received15 May 2024
Revised21 May 2024
Accepted21 May 2024
Issued01 July 2024
DOI10.18429/JACoW-IPAC2024-THPR35
Pages3571-3574