  We have evaluated the data-efficient Bayesian optimization method for the
specific task of injection tuning in a circular accelerator. In this paper, we
describe the implementation of this method at the Karlsruhe Research
Accelerator with up to nine tuning parameters, including the determination of
the associated hyperparameters. We show that the Bayesian optimization method
outperforms manual tuning and the commonly used Nelder-Mead optimization
algorithm both in simulation and experiment. The algorithm was also
successfully used to ease the commissioning phase after the installation of new
injection magnets and is regularly used during accelerator operations. We
demonstrate that the introduction of context variables that include intra-bunch
scattering effects, such as the Touschek effect, further improves the control
and robustness of the injection process.
