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URLhttps://doi.org/10.18429/JACoW-NAPAC2025-MOP050
TitleImprove beam brightness with bayesian optimization at the AGS booster injection at BNL
Authors
  • W. Lin, K. Brown, L. Hajdu, P. Adams, V. Schoefer, X. Gu
    Brookhaven National Laboratory
  • E. Hamwi
    Cornell University
  • G. Hoffstaetter
    Cornell University (CLASSE)
AbstractAlternating Gradient Synchrotron (AGS) and its Booster serve as part of the injector compound for RHIC and the future EIC at Brookhaven National Laboratory. Injection and early acceleration processes set maximum beam brightness for the collider rings. Such processes have many control parameters and are traditionally optimized empirically by operators. In an effort to streamline the injection processes with machine learning (ML) techniques, we develop and test a Bayesian Optimization (BO) algorithm to automatically tune the Linac to Booster (LtB) transfer line magnets to maximize beam brightness after injection into the Booster. We present experimental results that demonstrate BO can be applied to optimize Booster injection efficiency.
Paperdownload: MOP050.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
Received06 August 2025
Revised10 August 2025
Accepted10 August 2025
Issued28 January 2026
DOI10.18429/JACoW-NAPAC2025-MOP050
Pages157-159