Author: Biedron, S.
Paper Title Page
TUPAL077 2D-3D PIC Code Benchmarking/Anchoring Comparisons For a Novel RFQ/RFI LINAC Design 1194
  • S.J. Smith, S. Biedron, A. M. N. Elfrgani, E. Schamiloglu
    University of New Mexico, Albuquerque, USA
  • M.S. Curtin, B. Hartman, T. Pressnall, D.A. Swenson
    Ion Linac Systems, Inc., Albuquerque, USA
  • K. Kaneta
    CICS, Tokyo, Japan
  Funding: *The study at the University of New Mexico was supported in part by DARPA Grant N66001-16-1-4042 and gift to the University of New Mexico Foundation by ILS.
In this study, comparisons are made between several particle dynamics codes (namely CST Particle Studio, GPT, and upgraded PARMILA codes) in order to benchmark them. The structure used for the simulations is a novel 200 MHz, 2.5 MeV, CW RFQ/RFI LINAC designed by Ion Linac Systems (ILS). The structure design and parameters are provided, simulation techniques are explained, and results from all three code families are presented. These results are then compared with each other, identifying similarities and differences. Numerous parameters for comparison are used, including the transmission efficiency, Q-factor, E-field on axis, and beam properties. Preliminary anchoring between modeling and simulation performance predictions and experimental measurements will be provided.
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Results and Discussion of Recent Applications of Neural Network-Based Approaches to the Modeling and Control of Particle Accelerators  
  • A.L. Edelen
    CSU, Fort Collins, Colorado, USA
  • S. Biedron
    University of New Mexico, Albuquerque, USA
  • D.L. Bowring, B.E. Chase, D.R. Edstrom, J. Steimel
    Fermilab, Batavia, Illinois, USA
  • J.P. Edelen
    RadiaSoft LLC, Boulder, Colorado, USA
  • P.J.M. van der Slot
    Mesa+, Enschede, The Netherlands
  Here we highlight several examples from our work in applying neural network-based modeling and control techniques to particle accelerator systems, through a combination of simulation and experimental studies. We also discuss where the specific approaches used fit into the state of the art in deep learning for control, including limitations of the present state of the art (for example in efficiently dealing with noisy, time-varying, many-parameter systems, like those found in accelerators). We will also briefly clarify some of the terminology/taxonomy of artificial intelligence, and describe how the neural network approaches used here relate to other classes of algorithms that are familiar to the accelerator community. The particle accelerator applications discussed include resonant frequency control of Fermilab's PIP-II RFQ, fast switching between beam parameters in a compact THz FEL, modeling of the FAST low energy beamline at Fermilab, temperature control for the FAST RF gun, and trajectory control for the Jefferson Laboratory FEL.  
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IEEE/NPSS PAST Award Talk: Sandra Biedron  
  • S. Biedron
    UNM, Albuquerque, New Mexico, USA
  IEEE/NPSS PAST Award Talk: Sandra Biedron  
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