| 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 |
| Externí odkaz: |
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