TY  - JOUR
AU  - Roussel, Ryan
AU  - Edelen, Auralee L.
AU  - Boltz, Tobias
AU  - Kennedy, Dylan
AU  - Zhang, Zhe
AU  - Ji, Fuhao
AU  - Hunag, Xiaobiao
AU  - Ratner, Daniel
AU  - Santamaria Garcia, Andrea
AU  - Xu, Chenran
AU  - Kaiser, Jan
AU  - Ferran Pousa, Angel
AU  - Eichler, Annika
AU  - Lübsen, Jannis
AU  - Isenberg, Natalie M.
AU  - Gao, Yuan
AU  - Kuklev, Nikita
AU  - Matrinez, Jose
AU  - Mustapha, Brahim
AU  - Kain, Verena
AU  - Mayes, Christopher
AU  - Lin, Wejian
AU  - Liuzzo, Simone Maria
AU  - St. John, Jason
AU  - Streeter, Metthew J. V.
AU  - Lehe, Remi
AU  - Neiswanger, Willie
TI  - Bayesian Optimization Algorithms for Accelerator Physics
JO  - Physical review accelerators and beams
VL  - 27
IS  - 8
SN  - 2469-9888
CY  - College Park, MD
PB  - American Physical Society
M1  - PUBDB-2024-01516
SP  - 084801
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001293205800001
DO  - DOI:10.1103/PhysRevAccelBeams.27.084801
UR  - https://bib-pubdb1.desy.de/record/605574
ER  -