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At present, a variety of image-based diagnostics are used in particleaccelerator systems. Often times, these are viewed by a human operator who thenmakes appropriate adjustments to the machine. Given recent advances in usingconvolutional neural networks (CNNs) for image processing, it should bepossible to use image diagnostics directly in control routines (NN-based orotherwise). This is especially appealing for non-intercepting diagnostics thatcould run continuously during beam operation. Here, we show results of a firststep toward implementing such a controller: our trained CNN can predictmultiple simulated downstream beam parameters at the Fermilab AcceleratorScience and Technology (FAST) facility's low energy beamline using simulatedvirtual cathode laser images, gun phases, and solenoid strengths.

First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks