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Bayesian optimization has been shown to be a powerful tool for solving blackbox problems during online accelerator optimization. The major advantage ofBayesian based optimization techniques is the ability to include priorinformation about the problem to speed up optimization, even if thatinformation is not perfectly correlated with experimental measurements. Inparallel, neural network surrogate system models of accelerator facilities areincreasingly being made available, but at present they are not widely used inonline optimization. In this work, we demonstrate the use of an approximateneural network surrogate model as a prior mean for Gaussian processes used inBayesian optimization in a realistic setting. We show that the initialperformance of Bayesian optimization is improved by using neural networksurrogate models, even when surrogate models make erroneous predictions.Finally, we quantify requirements on surrogate prediction accuracy to achieveoptimization performance when solving problems in high dimensional inputspaces.

Neural Network Prior Mean for Particle Accelerator Injector Tuning