Author: Bowring, D.L.
Paper Title Page
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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