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<p>Automated accelerator tuning at GSI and FAIR is achieved using the Geoff framework. Geoff provides real-time optimization of beam parameters and experimental setups, supporting fast deployment and control room integration. It significantly improves performance, reducing SIS18 synchrotron injection losses from 45% to 12% and speeding up fragment separator setup using a classification algorithm. Advanced optimization strategies, such as multi-objective and multi-fidelity Bayesian optimization, were applied to SIS18 injection tuning, while model-predictive control implemented via model-driven reinforcement learning allows fast, constraint-aware adaptation. Using dedicated ion-source setups of the PUMA experiment at TU Darmstadt, automated control and optimization algorithms were used to optimize a hot-cathode electron source and a multi-reflection time-of-flight mass spectrometer, demonstrating the feasibility of real-time tuning of ion sources and particle traps. Geoff’s modular design facilitates easy integration of classical and machine-learning-based algorithms, bridging traditional accelerator operations with modern data-driven optimization.</p>

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Automating Accelerator Tuning at GSI/FAIR

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Sabrina Appel,
  • Hendrik Alsmeier,
  • Martin Bajzek,
  • Oliver Boine-Frankenheim,
  • Lisa Dingeldein,
  • Rolf Findeisen,
  • Benjamin Halilovic,
  • Simon Hirlaender,
  • Sebastian Hirt,
  • Daniel Kallendorf,
  • Erika Kazantseva,
  • Olha Kazinova,
  • Maximilian Kraeft,
  • Eric Lenz,
  • Penny Madysa,
  • Alexandre Obertelli,
  • Maik Pfefferkorn,
  • Stephane Pietri,
  • Christoph Reinwald,
  • Moritz Schlaich,
  • Stefan Sorge,
  • Helmut Weick,
  • Frank Wienholtz

摘要

Automated accelerator tuning at GSI and FAIR is achieved using the Geoff framework. Geoff provides real-time optimization of beam parameters and experimental setups, supporting fast deployment and control room integration. It significantly improves performance, reducing SIS18 synchrotron injection losses from 45% to 12% and speeding up fragment separator setup using a classification algorithm. Advanced optimization strategies, such as multi-objective and multi-fidelity Bayesian optimization, were applied to SIS18 injection tuning, while model-predictive control implemented via model-driven reinforcement learning allows fast, constraint-aware adaptation. Using dedicated ion-source setups of the PUMA experiment at TU Darmstadt, automated control and optimization algorithms were used to optimize a hot-cathode electron source and a multi-reflection time-of-flight mass spectrometer, demonstrating the feasibility of real-time tuning of ion sources and particle traps. Geoff’s modular design facilitates easy integration of classical and machine-learning-based algorithms, bridging traditional accelerator operations with modern data-driven optimization.