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@ARTICLE{Roussel:605574,
      author       = {Roussel, Ryan and Edelen, Auralee L. and Boltz, Tobias and
                      Kennedy, Dylan and Zhang, Zhe and Ji, Fuhao and Hunag,
                      Xiaobiao and Ratner, Daniel and Santamaria Garcia, Andrea
                      and Xu, Chenran and Kaiser, Jan and Ferran Pousa, Angel and
                      Eichler, Annika and Lübsen, Jannis and Isenberg, Natalie M.
                      and Gao, Yuan and Kuklev, Nikita and Matrinez, Jose and
                      Mustapha, Brahim and Kain, Verena and Mayes, Christopher and
                      Lin, Wejian and Liuzzo, Simone Maria and St. John, Jason and
                      Streeter, Metthew J. V. and Lehe, Remi and Neiswanger,
                      Willie},
      title        = {{B}ayesian {O}ptimization {A}lgorithms for {A}ccelerator
                      {P}hysics},
      journal      = {Physical review accelerators and beams},
      volume       = {27},
      number       = {8},
      issn         = {2469-9888},
      address      = {College Park, MD},
      publisher    = {American Physical Society},
      reportid     = {PUBDB-2024-01516},
      pages        = {084801},
      year         = {2024},
      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.},
      cin          = {MSK / MPA / KIT / SLAC},
      ddc          = {530},
      cid          = {I:(DE-H253)MSK-20120731 / I:(DE-H253)MPA-20200816 /
                      I:(DE-H253)KIT-20130928 / I:(DE-H253)SLAC-20170401},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      InternLabs-0011 - HIR3X - Helmholtz International Laboratory
                      on Reliability, Repetition, Results at the most advanced
                      X-ray Sources $(2020_InternLabs-0011)$ / EURIZON - European
                      network for developing new horizons for RIs (871072)},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_InternLabs-0011$ /
                      G:(EU-Grant)871072},
      experiment   = {EXP:(DE-H253)ARES-20200101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001293205800001},
      doi          = {10.1103/PhysRevAccelBeams.27.084801},
      url          = {https://bib-pubdb1.desy.de/record/605574},
}