Speaker
Description
Aging of the stripper foil and unexpected machine shutdowns are the primary causes for reduction of the injected intensity from CERN’s linac3 into the Low Energy Ion Ring (LEIR). As a result, the set of optimal control parameters that maximizes beam intensity in the ring tends to drift, requiring daily adjustments to the machine control settings. In this paper, several data-driven methods such as Bayesian Optimization (BO) and Reinforcement Learning (RL) are compared for the design of an autonomous controller of linac3 and LEIR parameters to maximize beam intensity before RF capture. Through the evaluation of both black-box and stateful approaches to solving this high-dimensional maximization problem, we aim to design an optimal controller that meets both performance and sample efficiency constraints.
