  Bayesian optimization has been shown to be a powerful tool for solving black
box problems during online accelerator optimization. The major advantage of
Bayesian based optimization techniques is the ability to include prior
information about the problem to speed up optimization, even if that
information is not perfectly correlated with experimental measurements. In
parallel, neural network surrogate system models of accelerator facilities are
increasingly being made available, but at present they are not widely used in
online optimization. In this work, we demonstrate the use of an approximate
neural network surrogate model as a prior mean for Gaussian processes used in
Bayesian optimization in a realistic setting. We show that the initial
performance of Bayesian optimization is improved by using neural network
surrogate models, even when surrogate models make erroneous predictions.
Finally, we quantify requirements on surrogate prediction accuracy to achieve
optimization performance when solving problems in high dimensional input
spaces.
