Hopfield Network based Control and Diagnostics System for Accelerators

TL;DR

This paper explores using Hopfield neural networks for pattern recognition, control, and diagnostics in accelerator systems, including denoising ion beam images for improved data analysis.

Contribution

It introduces the application of Hopfield networks as autoencoders for image denoising and pattern recognition in accelerator diagnostics, a novel use case.

Findings

Hopfield networks effectively recognize patterns in accelerator data.

The system successfully denoises ion beam images from CCD systems.

Potential for AI-based control and diagnostics in accelerators.

Abstract

A recurrent artificial neural network known as Hopfield network is used for pattern storage. Here we have applied this associative memory type network for pattern recognition for predictive controls and diagnostics in accelerator based systems. This system will be usefull for control systems and data acquisition system based on artificial intelligence at accelerator facilities. In this publication we have discussed the role of Hopfield network as pattern recognition and auto encoder for denoising the images of ion beams obtained using CCD based optical systems.