Bayesian optimization of injection efficiency at KARA using Gaussian processes

The injection at the KIT storage ring KARA (Karlsruhe Research Accelerator) 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 can get stuck in local optima. To address this, Bayesian optimisation is applied, i.e. a technique for optimising 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 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.

Chenran Xu acknowledges the support by the DFG-funded Doctoral School "Karlsruhe School of Elementary and Astroparticle Physics: Science and Technology".