Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
This work addresses the repetitive and safety-critical tuning process for particle accelerators, offering a practical solution for facilities like PSI, though it is incremental as it adapts an existing method.
The paper tackled the problem of automating the tuning of particle accelerator parameters while respecting safety constraints, and demonstrated a safe Bayesian optimization variant that successfully tuned up to 16 parameters under 224 constraints on two real-world accelerators.
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most methods do not account for safety-critical constraints in each iteration, such as loss signals or step-size limitations. One notable exception is safe Bayesian optimization, which is a data-driven tuning approach for global optimization with noisy feedback. We propose and evaluate a step-size limited variant of safe Bayesian optimization on two research facilities of the Paul Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) the High-Intensity Proton Accelerator (HIPA). We report promising experimental results on both machines, tuning up to 16 parameters subject to 224 constraints.