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@ARTICLE{Kaiser:617676,
      author       = {Kaiser, Jan and Lauscher, Anne and Eichler, Annika},
      title        = {{L}arge language models for human-machine collaborative
                      particle accelerator tuning through natural language},
      journal      = {Science advances},
      volume       = {11},
      number       = {1},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {PUBDB-2024-06977},
      pages        = {eadr4173},
      year         = {2025},
      abstract     = {Autonomous tuning of particle accelerators is an active and
                      challenging research field with the goal of enabling
                      advanced accelerator technologies and cutting-edge
                      high-impact applications, such as physics discovery, cancer
                      research, and material sciences. A challenge with autonomous
                      accelerator tuning remains that the most capable algorithms
                      require experts in optimization and machine learning to
                      implement them for every new tuning task. Here, we propose
                      the use of large language models (LLMs) to tune particle
                      accelerators. We demonstrate on a proof-of-principle example
                      the ability of LLMs to tune an accelerator subsystem based
                      on only a natural language prompt from the operator, and
                      compare their performance to state-of-the-art optimization
                      algorithms, such as Bayesian optimization and reinforcement
                      learning–trained optimization. In doing so, we also show
                      how LLMs can perform numerical optimization of a nonlinear
                      real-world objective. Ultimately, this work represents
                      another complex task that LLMs can solve and promises to
                      help accelerate the deployment of autonomous tuning
                      algorithms to day-to-day particle accelerator operations.},
      cin          = {MSK},
      ddc          = {500},
      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 $(2020_InternLabs-0011)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_InternLabs-0011$},
      experiment   = {EXP:(DE-H253)ARES-20200101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2405.08888},
      howpublished = {arXiv:2405.08888},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2405.08888;\%\%$},
      pubmed       = {pmid:39742494},
      UT           = {WOS:001386432700013},
      doi          = {10.1126/sciadv.adr4173},
      url          = {https://bib-pubdb1.desy.de/record/617676},
}