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Tuning machine parameters of particle accelerators is a repetitive andtime-consuming task that is challenging to automate. While many off-the-shelfoptimization algorithms are available, in practice their use is limited becausemost methods do not account for safety-critical constraints in each iteration,such as loss signals or step-size limitations. One notable exception is safeBayesian optimization, which is a data-driven tuning approach for globaloptimization with noisy feedback. We propose and evaluate a step-size limitedvariant of safe Bayesian optimization on two research facilities of the PaulScherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) theHigh-Intensity Proton Accelerator (HIPA). We report promising experimentalresults on both machines, tuning up to 16 parameters subject to 224constraints.

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization