Reinforcement Learning for Charged Particle Beam Control to Minimize Injection Mismatch in Particle Accelerators
Thilina Balasooriya, Shinjae Yoo, Vincent Schoefer, Huan-Hsin Tseng, Yuan Gao, Weijian Lin, Chanaka De Silva
Abstract
Particle accelerators are composed of various components, and their properties are finely tuned to optimize certain particle beam qualities as they accelerate. In particular, particle colliders like the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) are interested in maximizing luminosity, a measure of the collision rate primarily determined by the beam intensity (number of particles) and its beam size. However, finding and maintaining optimum settings is a time-consuming expert operator activity. This work proposes the use of the Recurrent Proximal Policy Optimization (RPPO), a reinforcement learning algorithm, to find parameters of quadrupole magnet strengths optimizing the beam qualities in the Booster to AGS (Alternating Gradient Synchrotron) section of the RHIC complex.
BibTeX
@inproceedings{icassp2025_reinforcementlea,
title = {Reinforcement Learning for Charged Particle Beam Control to Minimize Injection Mismatch in Particle Accelerators},
author = {Thilina Balasooriya and Shinjae Yoo and Vincent Schoefer and Huan-Hsin Tseng and Yuan Gao and Weijian Lin and Chanaka De Silva},
booktitle = {ICASSP 2025},
year = {2025}
}