Diagnostics for Linac Optimization With Machine Learning

TL;DR

This paper discusses the development of machine learning-based diagnostics and optimization techniques to improve the stability and performance of Fermilab's linear accelerator, focusing on beam diagnostics and dynamic parameter tuning.

Contribution

It introduces a framework for ML-driven diagnostics and optimization in linac operation, including preliminary results and future plans.

Findings

Initial diagnostics studies show promising signals for ML input

ML-based optimization can potentially stabilize beam parameters

Framework sets the stage for real-time linac control improvements

Abstract

The Fermilab Linac delivers 400 MeV H- beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To counter fluctuations of Linac output due to various effects to be described below we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As inputs for the ML model, signals from beam diagnostics have to be well understood and reliable. In this paper we discuss the status and plans for ML-based optimization as well as preliminary results of diagnostics studies.