Automatic setup of 18 MeV electron beamline using machine learning

To improve the performance-critical stability and brightness of the electron bunch at injection into the proton-driven plasma wakefield at AWAKE, automation approaches based on unsupervised Machine Learning (ML) were developed and deployed. Numerical optimisers were tested together with different model-free reinforcement learning (RL) agents. 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 introduces the AWAKE electron beamline and describes the results obtained with the different ML approaches, including automatic unsupervised feature extraction from images using computer vision. The prospects for operational deployment and wider applicability are discussed.