  The Linac Coherent Light Source changes configurations multiple times per
day, necessitating fast tuning strategies to reduce setup time for successive
experiments. To this end, we employ a Bayesian approach to transport optics
tuning to optimize groups of quadrupole magnets. We use a Gaussian process to
provide a probabilistic model of the machine response with respect to control
parameters from a modest number of samples. Subsequent samples are selected
during optimization using a statistical test combining the model prediction and
uncertainty. The model parameters are fit from archived scans, and correlations
between devices are added from a simple beam transport model. The result is a
sample-efficient optimization routine, which we show significantly outperforms
existing optimizers.
