NAPAC2025 - Proceedings
Sacramento, CA, USA
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URLhttps://doi.org/10.18429/JACoW-NAPAC2025-MOP039
TitleFast adaptive neural control of resonant extraction at Fermilab
Authors
  • A. Whitbeck, A. Narayanan, J. St. John, J. Berlioz, J. MItrevski, K. Danison-Fieldhouse, K. Hazelwood, M. Khan, N. Tran
    Fermi National Accelerator Laboratory
  • J. Ji, M. Walter
    Toyota Technological Institute at Chicago
AbstractWe present the development of a machine learning (ML) regulation system for third-order resonant beam extraction in the Mu2e experiment at Fermilab. Classical and ML-based controllers have been optimized using semi-analytic simulations and evaluated in terms of regulation performance and training efficiency. We compare several controller architectures and discuss the integration of neural control into an adaptive framework. In addition, we report progress on implementing low-latency, edge-based inference to enable deployment in hardware-constrained environments. This work demonstrates the feasibility and potential advantages of ML-based control for regulating complex, non-stationary systems, with applications extending beyond resonant extraction.
Paperdownload: MOP039.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference6th North American Particle Accel. Conf. (NAPAC2025)
Series
LocationSacramento, CA, USA
Date10-15 Aug 2025
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardTor O. Raubenheimer (SLAC), Roark A. Marsh (LLNL), Eric Prebys (UC Davis), Ling Wang (MSU), Petr M. Anisimov (LANL), Kip Bishofberger (LANL), Gustavo Bruno (ANL), Zhichu Chen (SARI), Jan Chrin (PSI), Kelly Jaje (ANL), Jaeyu Lee (POSTECH), Magdalena Montes-Loera (SLAC), Mary Saethre (PHENOTYPE), Tasha Summers (SLAC), Kent Wootton (ANL)
Online ISBN978-3-95450-261-5
Online ISSN2673-7000
Received08 August 2025
Revised14 August 2025
Accepted14 August 2025
Issued28 January 2026
DOI10.18429/JACoW-NAPAC2025-MOP039
Pages131-134
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