Journal Article

PUBDB-2024-01516

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Bayesian Optimization Algorithms for Accelerator Physics

Roussel, R. (Corresponding author) ; Edelen, A. L. ; Boltz, T. ; Kennedy, D. ; Zhang, Z. ; Ji, F. ; Hunag, X. ; Ratner, D.Extern* ; Santamaria Garcia, A.Extern* ; Xu, C.Extern* ; Kaiser, J.DESY* ; Ferran Pousa, A.DESY* ; Eichler, A.XFEL.EU*DESY* ; Lübsen, J.Extern* ; Isenberg, N. M. ; Gao, Y. ; Kuklev, N. ; Matrinez, J. ; Mustapha, B. ; Kain, V. ; Mayes, C. ; Lin, W. ; Liuzzo, S. M. ; St. John, J. ; Streeter, M. J. V. ; Lehe, R. ; Neiswanger, W.

2024
American Physical Society College Park, MD

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Please use a persistent id in citations: doi:10.1103/PhysRevAccelBeams.27.084801  doi:10.3204/PUBDB-2024-01516

Abstract: Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques toward solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

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Contributing Institute(s):

1. Strahlkontrollen (MSK)
2. Plasma Accelerators (MPA)
3. externe Institute im Bereich Photon Science (KIT)
4. Stanford Linear Accelerator Center (SLAC)

Research Program(s):

1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)
2. InternLabs-0011 - HIR3X - Helmholtz International Laboratory on Reliability, Repetition, Results at the most advanced X-ray Sources (2020_InternLabs-0011) (2020_InternLabs-0011)
3. EURIZON - European network for developing new horizons for RIs (871072) (871072)

Experiment(s):

1. Accelerator Research Experiment at SINBAD

Appears in the scientific report 2024

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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