Dresden 2020 – scientific programme
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AKBP: Arbeitskreis Beschleunigerphysik
AKBP 7: Diagnostics, Control and Instrumentation
AKBP 7.6: Talk
Tuesday, March 17, 2020, 15:45–16:00, MOL 213
Bayesian Optimization of Injection Efficiency at KARA using Gaussian Processes — •Chenran Xu1, Tobias Boltz1, Akira Mochihashi2, and Anke-Susanne Müller1,2 — 1LAS, KIT, Karlsruhe — 2IBPT, KIT, Karlsruhe
The injection at the KIT storage ring KARA is tuned by many parameters, such as the strength of various magnets and the RF frequency. The tuning process is currently performed manually by machine operators, which is time consuming and often gets stuck in local optima. This is exactly the domain for Bayesian optimization, a technique to optimize noisy black box functions. Using Gaussian processes (GPs) for regression, we obtain a probabilistic model which allows the integration of prior knowledge about the physical process. The model can be queried during the optimization procedure in order to efficiently explore the given parameter space, leading to comparably fast convergence. In this contribution, we demonstrate the implementation of Bayesian optimization to automate and optimize the injection process.