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@ARTICLE{Kaiser:600286,
      author       = {Kaiser, Jan and Xu, Chenran and Eichler, Annika and
                      Santamaria Garcia, Andrea},
      title        = {{B}ridging the gap between machine learning and particle
                      accelerator physics with high-speed, differentiable
                      simulations},
      journal      = {Physical review accelerators and beams},
      volume       = {27},
      number       = {5},
      issn         = {2469-9888},
      address      = {College Park, MD},
      publisher    = {American Physical Society},
      reportid     = {PUBDB-2023-07854, arXiv:2401.05815},
      pages        = {054601},
      year         = {2024},
      note         = {Phys. Rev. Accel. Beams 27 (2024) 054601. 16 pages, 9
                      figures, 3 tables},
      abstract     = {Machine learning has emerged as a powerful solution to the
                      modern challenges in accelerator physics. However, the
                      limited availability of beam time, the computational cost of
                      simulations, and the high dimensionality of optimization
                      problems pose significant challenges in generating the
                      required data for training state-of-the-art machine learning
                      models. In this work, we introduce cheetah, a pytorch-based
                      high-speed differentiable linear beam dynamics code. cheetah
                      enables the fast collection of large datasets by reducing
                      computation times by multiple orders of magnitude and
                      facilitates efficient gradient-based optimization for
                      accelerator tuning and system identification. This positions
                      cheetah as a user-friendly, readily extensible tool that
                      integrates seamlessly with widely adopted machine learning
                      tools. We showcase the utility of cheetah through five
                      examples, including reinforcement learning training,
                      gradient-based beamline tuning, gradient-based system
                      identification, physics-informed Bayesian optimization
                      priors, and modular neural network surrogate modeling of
                      space charge effects. The use of such a high-speed
                      differentiable simulation code will simplify the development
                      of machine learning-based methods for particle accelerators
                      and fast-track their integration into everyday operations of
                      accelerator facilities.},
      keywords     = {space charge (INSPIRE) / accelerator (INSPIRE) / machine
                      learning (INSPIRE) / optimization (INSPIRE) / reinforcement
                      learning (INSPIRE) / modular (INSPIRE) / Bayesian (INSPIRE)
                      / neural network (INSPIRE)},
      cin          = {MSK},
      ddc          = {530},
      cid          = {I:(DE-H253)MSK-20120731},
      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 $(2020_InternLabs-0011)$ / ZT-I-PF-5-6 -
                      Autonomous Accelerator (AA) $(2020_ZT-I-PF-5-6)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_InternLabs-0011$ /
                      $G:(DE-HGF)2020_ZT-I-PF-5-6$},
      experiment   = {EXP:(DE-H253)ARES-20200101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2401.05815},
      howpublished = {arXiv:2401.05815},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2401.05815;\%\%$},
      UT           = {WOS:001237562300003},
      doi          = {10.1103/PhysRevAccelBeams.27.054601},
      url          = {https://bib-pubdb1.desy.de/record/600286},
}