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URLhttps://doi.org/10.18429/JACoW-IPAC2023-WEPA026
TitleActive deep learning for nonlinear optics design of a vertical FFA accelerator
Authors
  • A. Oeftiger
    GSI Helmholtzzentrum für Schwerionenforschung GmbH
  • A. Santamaria Garcia
    Karlsruhe Institute of Technology
  • J. Lagrange
    Science and Technology Facilities Council
  • S. Hirlaender
    University of Salzburg
AbstractVertical Fixed-Field Alternating Gradient (vFFA) accelerators exhibit particle orbits which move vertically during acceleration. This recently rediscovered circular accelerator type has several advantages over conventional ring accelerators, such as zero momentum compaction factor. At the same time, inherently non-planar orbits and a unique transverse coupling make controlling the beam dynamics a complex task. In general, betatron tune adjustment is crucial to avoid resonances, particularly when space charge effects are present. Due to highly nonlinear magnetic fields in the vFFA, it remains a challenging task to determine an optimal lattice design in terms of maximising the dynamic aperture. This contribution describes a deep learning based algorithm which strongly improves on regular grid scans and random search to find an optimal lattice: a surrogate model is built iteratively from simulations with varying lattice parameters to predict the dynamic aperture. The training of the model follows an active learning paradigm, which thus considerably reduces the number of samples needed from the computationally expensive simulations.
Paperdownload: WEPA026.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference14th International Particle Accelerator Conference
Series
LocationVenice, Italy
Date07-12 May 2023
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardRalph Assmann - Deutsches Elektronen-Synchrotron DESY Peter McIntosh - Science and Technology Facilities Council (STFC/DL/ASTeC) Giovanni Bisoffi - Istituto Nazionale di Fisica Nucleare (INFN/LNL) Alessandro Fabris - Elettra-Sincrotrone Trieste S.C.p.A. Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A. Giulia Vinicola - Istituto Nazionale di Fisica Nucleare (INFN/LNF)
Online ISBN978-3-95450-231-8
Online ISSN2673-5490
Received03 May 2023
Revised08 May 2023
Accepted19 June 2023
Issued26 September 2023
DOI10.18429/JACoW-IPAC2023-WEPA026
Pages2709-2712