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.

arXiv comment21 pp

References in corpus (7)

• Deep Learning in Neural Networks: An Overview
• On the difficulty of training Recurrent Neural Networks
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Data Mining and Machine Learning in Astronomy
• Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
• SKYNET: an efficient and robust neural network training tool for machine learning in astronomy
• Online optimization of storage ring nonlinear beam dynamics

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• Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex
• Physics-informed neural network method for modelling beam-wall interactions
• Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
• Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
• Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
• Neural Network Model Of The PXIE RFQ Cooling System and Resonant Frequency Response
• Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning
• Report from the A.I. For Nuclear Physics Workshop
• 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
• Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning
• Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements