  At present, a variety of image-based diagnostics are used in particle
accelerator systems. Often times, these are viewed by a human operator who then
makes appropriate adjustments to the machine. Given recent advances in using
convolutional neural networks (CNNs) for image processing, it should be
possible to use image diagnostics directly in control routines (NN-based or
otherwise). This is especially appealing for non-intercepting diagnostics that
could run continuously during beam operation. Here, we show results of a first
step toward implementing such a controller: our trained CNN can predict
multiple simulated downstream beam parameters at the Fermilab Accelerator
Science and Technology (FAST) facility's low energy beamline using simulated
virtual cathode laser images, gun phases, and solenoid strengths.
