AI Driven Optimal Control Systems in Nuclear Physics Experiments

Advancing experimental capabilities in spin physics demands innovative integration of mechanical, nuclear, and computational systems. In this talk, I present a data acquisition scheme designed to unify and monitor several key experimental subsystems—including cryogenics, vacuum, and target RF and microwave electronics—with the goal of creating training data sets to build an AI driven optimal control framework. This interdisciplinary approach is being developed and tested on the University of Virginia Spin Group’s solid state polarized target infrastructure, which serves as a real-world test bench. By combining classical target design principles with modern control theory and AI-driven automation, this work aims to reduce operator intervention, increase system efficiency, and enable intelligent fault detection. The resulting architecture serves as a foundation for future smart instrumentation in high-precision nuclear physics experiments.