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URLhttps://doi.org/10.18429/JACoW-HIAT2025-WEB04
TitleALPI-PIAVE performance at INFN-LNL with advanced optimization algorithms
Authors
  • Y. Ong, L. Bellan, A. Pisent, M. Comunian, E. Fagotti, D. Bortolato, M. Montis
    Istituto Nazionale di Fisica Nucleare
AbstractThe ALPI-PIAVE heavy-ion accelerator complex at INFN-LNL requires precise tuning to achieve optimal performance, particularly given its complex beam dynamics and high-dimensional parameter space. Although the facility is designed for 60% transmission, actual transmission rates are significantly lower, around 15–20%, highlighting the need for improved optimization strategies. In this work, we compare advanced optimization techniques, specifically Bayesian Optimization (BO) and Trust Region Bayesian Optimization (TuRBO), to enhance the efficiency, reliability, and adaptability of ALPI-PIAVE operations. Since Bayesian optimization algorithms are sensitive to accelerator parameter drifts, we integrated Particle Swarm Optimization (PSO) to improve robustness. Additionally, we conducted controlled instability tests to evaluate performance under dynamic conditions. The results of these experiments and their implications for real-time accelerator tuning are presented in this paper.
Paperdownload: WEB04.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference16th International Conference on Heavy Ion Accelerator Technology
Series
LocationEast Lansing, MI, USA
Date22-27 Jun 2025
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardLing Wang (FRIB/MSU), Kelly Jaje (ANL), Michelle Wier (FRIB/MSU), Kent Wootton (ANL), Tong Zhang (FRIB/MSU)
Online ISBN978-3-95450-260-8
Online ISSN2673-5547
Received22 September 2025
Revised22 September 2025
Accepted22 September 2025
Issued13 October 2025
DOI10.18429/JACoW-HIAT2025-WEB04
Pages211-214