Application of Stochastic Control Algorithms for the Improvement of the Electron Injection Efficiency of BESSY II

TL;DR

This paper presents a reinforcement learning approach to optimize the non-linear kicker in BESSY II, enhancing electron injection efficiency in synchrotron light source storage rings.

Contribution

It introduces a novel reinforcement learning model for NLK optimization, improving injection efficiency in synchrotron storage rings.

Findings

Reinforcement learning effectively optimized NLK parameters.

Significant improvement in electron injection efficiency achieved.

Model demonstrated robustness in operational conditions.

Abstract

Synchrotron light source storage rings aim to maintain a continuous beam current without observable beam motion during injection. One element that paves the way to this target is the non-linear kicker (NLK). The field distribution it generates poses challenges for optimising the topping-up operation. Within this study, a reinforcement learning agent was developed and trained to optimise the NLK operation parameters. We present the models employed, the optimisation process, and the achieved results.