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@INPROCEEDINGS{SantamariaGarcia:358340,
      author       = {Santamaria Garcia, Andrea and Lagrange, Jean-Baptiste and
                      Hirlaender, Simon and Oeftiger, Adrian},
      title        = {{A}ctive deep learning for nonlinear optics design of a
                      vertical {FFA} accelerator},
      address      = {Geneva, Switzerland},
      publisher    = {JACoW Publishing},
      reportid     = {GSI-2025-00524},
      pages        = {2709 - 2712},
      year         = {2023},
      note         = {Published by JACoW Publishing under the terms of the
                      Creative Commons Attribution 4.0 license.},
      abstract     = {Vertical 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.},
      month         = {May},
      date          = {2023-05-07},
      organization  = {14th International Particle
                       Accelerator Conference, Venice (Italy),
                       7 May 2023 - 12 May 2023},
      keywords     = {Accelerator Physics (Other) /
                      mc5-beam-dynamics-and-em-fields - MC5: Beam Dynamics and EM
                      Fields (Other) / mc5-d13-machine-learning - MC5.D13: Machine
                      Learning (Other)},
      cin          = {APH},
      cid          = {I:(DE-Ds200)APH-20060809OR090},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      experiment   = {$EXP:(DE-Ds200)no_experiment-20200803$},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.18429/JACOW-IPAC2023-WEPA026},
      url          = {https://repository.gsi.de/record/358340},
}