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 PSI: (a) the SwissFEL and (b) HIPA. We report promising experimental results on both machines, tuning up to 16 parameters subject to 224 constraints.

@article{kirschner22tuning,
	author = {Johannes Kirschner and Mojmir Mutny and Andreas Krause and Jaime Coello de Portugal and Nicolle Hiller and Jochem Snuverink},
	doi = {10.1103/PhysRevAccelBeams.25.062802},
	journal = {Physical Review Accelerators and Beams},
	month = {June},
	number = {6},
	title = {Tuning particle accelerators with safety constraints using Bayesian optimization},
	volume = {25},
	year = {2022}}