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arXiv · 2203.13968

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

Johannes Kirschner·Mojmir Mutný·Andreas Krause·Jaime Coello de Portugal·Nicole Hiller·Jochem Snuverink
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Abstract

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.

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Johannes Kirschner, Mojmir Mutný, Andreas Krause, Jaime Coello de Portugal, Nicole Hiller, Jochem Snuverink. 2022-06-30. Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization. https://doi.org/10.1103/physrevaccelbeams.25.062802

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