The Spallation Neutron Source uses a high-power accelerator and target to produce neutrons to explore the nature of materials and energy. Running the facility at the cutting edge of technology does lead to occasional interruptions in the scientific program. We present results from a three year project aimed at exploring Machine Learning methods to improve accelerator and target reliability. Various application areas ranging from reducing beam trips, surrogate modeling of high-power targets, to improving on cryogenic system behavior will be discussed as well as lessons learned. Finally, we present our plans for the continuation of the project, including a continual learning framework necessary to integrate Machine Learning with Operations.

We propose and demonstrate a time-resolved, two-dimensional temperature monitoring technique for nanocrystalline diamond stripper foils exposed to high-intensity hydrogen ion (H-) beams at the Spallation Neutron Source (SNS) accumulator ring which is independent of foil emissivity. The technique utilizes a two-color imaging pyrometer in the shortwave infrared (SWIR) spectral band to measure thermal radiation from stripper foils located 40 meters away from the measurement site. This work presents a unique optical design, optical calibration of the system using a high-temperature blackbody source, preliminary temperature measurement results from two stripper foils (new and used) under various H‒ production beam conditions with average powers up to 1.7 MW and energy of 1.0 GeV. This technique can be utilized to understand the thermal behavior of charge strippers under high-intensity particle beams, providing crucial feedback to operations to control foil temperature and ensure foil lifetime.