Neural Networks for Modeling and Control of Particle Accelerators

TL;DR

This paper explores the application of neural networks in particle accelerator control, detailing recent advances, potential integration strategies, and presenting a neural network-based control system with initial experimental results at Fermilab.

Contribution

It introduces a neural network-based control system for resonance control of an RF electron gun, demonstrating its feasibility with initial experimental results.

Findings

Successful implementation of a neural network controller at Fermilab

Initial experimental results show promising control performance

Highlights potential for neural networks in accelerator system management

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.