Diagnostics for Linac Optimization With Machine Learning

2022/09/06 by R. Sharankova, M. Mwaniki, Sharankova, R. +5
Engineering · #Accelerator Physics (physics.acc-ph) #FOS: Physical sciences #Particle Accelerators and Free-Electron Lasers #Particle accelerators and beam dynamics #Superconducting Materials and Applications

paper · pdf · doi:10.48550/arxiv.2209.02526

openalex publication_date 2022/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

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.

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