Multi-objective optimization of CEBAF heat load management and trip rates using AI/ML

Abstract

The goal of this project is to investigate the use of RL to simultaneously optimize the cryogenic heat load and trip rate of the CEBAF linacs and make it generic to solve multi-objective problems that will enable its application at other Department of Energy (DOE) facilities with similar challenges. The existing approach optimizes the gradient distribution to minimize the trip rate and does not take into account the total heat load. A secondary objective of the project is the develop a framework to correct cavity quality curves (Q-curves) or RF-heat to Gradient relation curves for individual cavities. These curves are currently obtained using historical measurements and may have changed their behavior due to various events. Correction in these curves will allow us to accurately approximate the total RF heat generated by the linacs which is critical for the optimization.