CERN Accelerating science

Article
Report number	arXiv:2209.03183
Title	Towards automatic setup of 18 MeV electron beamline using machine learning
Related title	Automatic setup of 18 MeV electron beamline using machine learning
Author(s)	Velotti, Francesco Maria (CERN) ; Goddard, Brennan (CERN) ; Kain, Verena (CERN) ; Ramjiawan, Rebecca (CERN) ; Della Porta, Giovanni Zevi (CERN) ; Hirlaender, Simon (U. Salzburg (main))
Publication	2023-04-27
Imprint	2022-09-07
Number of pages	18
In:	Mach. Learn. Sci. Tech. 4 (2023) 025016
DOI	10.1088/2632-2153/acce21
Subject category	physics.acc-ph ; Accelerators and Storage Rings
Abstract	To improve the performance-critical stability and brightness of the electron bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN experiment, automation approaches based on unsupervised Machine Learning (ML) were developed and deployed. Numerical optimisers were tested together with different model-free reinforcement learning agents. In order to avoid any bias, reinforcement learning agents have been trained also using a completely unsupervised state encoding using auto-encoders. To aid hyper-parameter selection, a full synthetic model of the beamline was constructed using a variational auto-encoder trained to generate surrogate data from equipment settings. This paper describes the novel approaches based on deep learning and reinforcement learning to aid the automatic setup of a low energy line, as the one used to deliver beam to the AWAKE facility. The results obtained with the different ML approaches, including automatic unsupervised feature extraction from images using computer vision are presented. The prospects for operational deployment and wider applicability are discussed.
Copyright/License	preprint: (License: CC BY 4.0) publication: © 2023-2025 The Author(s) (License: CC-BY-4.0)

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