Automatic setup of 18 MeV electron beamline using machine learning

TL;DR

This paper presents novel deep learning and reinforcement learning methods for automating the setup of an 18 MeV electron beamline, enhancing stability and brightness for plasma wakefield experiments at CERN.

Contribution

It introduces unsupervised reinforcement learning and auto-encoder based models for bias-free beamline setup automation, with a synthetic model aiding hyper-parameter tuning.

Findings

Successful deployment of ML-based automation approaches

Effective unsupervised feature extraction from images

Potential for operational deployment at CERN

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.