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.
@article{arxiv.2203.13968,
title = {Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization},
author = {Johannes Kirschner and Mojmir Mutný and Andreas Krause and Jaime Coello de Portugal and Nicole Hiller and Jochem Snuverink},
journal= {arXiv preprint arXiv:2203.13968},
year = {2022}
}