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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.
Tuning particle accelerators with safety constraints using Bayesian optimization J. Kirschner, M. Mutny, A. Krause, J. C. de Portugal, N. Hiller, J. SnuverinkIn Physical Review Accelerators and Beams, volume 25, 2022
Bibtex Entry:
	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}}