Automating accelerator tuning at GSI/FAIR

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 algorithms. 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 an 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.