Bayesian optimization experiment for trajectory alignment at the low energy RHIC electron cooling system

Abstract

The low energy RHIC electron cooling (LEReC) system is the world’s first electron cooler utilizing radio frequency (rf) accelerated electron bunches, and a nonmagnetized electron beam. It is also the first electron cooler applied directly to colliding hadron beams. The unique approach to cooling makes beam dynamics in LEReC very different from the conventional electron coolers. Numerous LEReC parameters can affect the cooling rate. One of the most critical factors is the alignment of the electron and ion trajectories in the cooling section. In this work, we apply Bayesian optimization to check and if needed to optimize the trajectories’ alignment. Experimental results are presented and it is demonstrated that machine learning (ML) methods can be applied to perform the control tasks effectively in the RHIC controls system.