Journal Article

GSI-2025-00601

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Injection optimization at particle accelerators via reinforcement learning: From simulation to real-world application

Awal, A. A. H. A. (Corresponding author)GSI*RWTH* ; Hetzel, J. H. (Corresponding author)GSI* ; Gebel, R. H.GSI* ; Pretz, J.RWTH*FZJ*

2025
American Physical Society College Park, MD

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Please use a persistent id in citations: doi:10.1103/PhysRevAccelBeams.28.034601  doi:10.15120/GSI-2025-00601

Abstract: Optimizing the injection process in particle accelerators is crucial for enhancing beam quality and operational efficiency. This paper presents a framework for utilizing reinforcement learning (RL) to optimize the injection process at accelerator facilities. By framing the optimization challenge as an RL problem, we developed an agent capable of dynamically aligning the beam’s transverse space with desired targets. Our methodology leverages the soft actor-critic algorithm, enhanced with domain randomization and dense neural networks, to train the agent in simulated environments with varying dynamics promoting it to learn a generalized robust policy. The agent was evaluated in live runs at the cooler synchrotron COSY and it has successfully optimized the beam cross section reaching human operator level but in notably less time. An empirical study further validated the importance of each architecture component in achieving a robust and generalized optimization strategy. The results demonstrate the potential of RL in automating and improving optimization tasks at particle acceleration facilities.

Note: "Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI."

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Contributing Institute(s):

1. HESR (HES)

Research Program(s):

1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)
2. 6G12 - FAIR (GSI) (POF4-6G12) (POF4-6G12)

Experiment(s):

1. External experiment at external facility/ no experiment at GSI (other)

Appears in the scientific report 2025

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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