Neural Networks for Modeling and Control of Particle Accelerators

Abstract

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

21 pp

References in corpus (2)

• Deep Learning in Neural Networks: An Overview
• On the difficulty of training Recurrent Neural Networks

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• Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities
• Resonant Frequency Control For the PIP-II Injector Test RFQ: Control Framework and Initial Results
• Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster