تقرير
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
العنوان: | Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization |
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المؤلفون: | Kirschner, Johannes, Mutný, Mojmir, Krause, Andreas, de Portugal, Jaime Coello, Hiller, Nicole, Snuverink, Jochem |
سنة النشر: | 2022 |
مصطلحات موضوعية: | Physics - Accelerator Physics, Computer Science - Machine Learning |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
DOI: | 10.1103/PhysRevAccelBeams.25.062802 |
URL الوصول: | http://arxiv.org/abs/2203.13968 |
رقم الأكسشن: | edsarx.2203.13968 |
قاعدة البيانات: | arXiv |
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