Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators

TL;DR

This paper explores machine learning techniques to reduce noise in RF control signals of industrial accelerators, aiming to enhance performance and deployment efficiency in various industrial applications.

Contribution

It introduces novel machine learning algorithms tailored for noise reduction in RF signals of industrial accelerators, with promising simulation results and plans for real-world deployment.

Findings

Machine learning algorithms effectively reduce RF noise in simulations.

Potential for improved performance and cost savings in industrial accelerators.

Framework ready for deployment on actual industrial systems.

Abstract

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.