Artificial intelligence algorithms used to tune particle accelerators

Accelerators — machines that speed up particles such as protons — are useful in nuclear and high-energy physics as well as materials science, dynamic imaging and even isotope production for cancer therapy. A Los Alamos National Laboratory-led project presents a machine learning algorithm that harnesses artificial intelligence capabilities to help tune accelerators, making continuous adjustments that keep the beam precise and useful for scientific discovery.

“The complexity and time variation of the machinery means that over extended usage, the characteristics of an accelerator’s particle beam change,” said Alexander Scheinker, research and development engineer at Los Alamos and the project’s lead. “Factors like vibrations and temperature changes can cause problems for accelerators, which have thousands of components, and even the best accelerator technicians can struggle to identify and address issues or return them to optimum parameters quickly. It is a high-dimensional optimization problem that must be repeated again and again as the systems drift with time. Turning these machines on after an outage or retuning between different experiments can take weeks.”


An accelerator that can be effectively tuned in real time can provide higher currents to experiments and is more likely to stay running, offering more beam time for science experiments, and is also more likely to ensure precise results. In a collaboration with Lawrence Berkeley National Laboratory, the approach developed by Scheinker couples adaptive feedback control algorithms, deep convolutional neural networks and physics-based models in one large feedback loop to make better, noninvasive predictions that enable autonomous control of compact accelerators.

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