Tuning particle accelerators with safety constraints using Bayesian optimization

Autor: Johannes Kirschner, Mojmir Mutný, Andreas Krause, Jaime Coello de Portugal, Nicole Hiller, Jochem Snuverink
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Physical Review Accelerators and Beams, Vol 25, Iss 6, p 062802 (2022)
Druh dokumentu: article
ISSN: 2469-9888
DOI: 10.1103/PhysRevAccelBeams.25.062802
Popis: 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.
Databáze: Directory of Open Access Journals