THPM031

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URL	https://doi.org/10.18429/JACoW-IPAC2025-THPM031
Title	Beam Energy Forecasting using Machine Learning at the CLEAR accelerator
Authors	A. Gilardi University of Naples Federico II A. Malyzhenkov, A. Petersson, A. Aksoy, D. Gamba, E. Granados, G. Tangari, L. Bonnard, L. Wroe, O. Franek, R. Corsini, V. Rieker, W. Farabolini, S. Mazzoni European Organization for Nuclear Research A. Mostacci Sapienza University of Rome A. Pollastro Naples University Federico II and INFN D. Filippetto Istituto Nazionale di Fisica Nucleare K. Sjobak University of Oslo M. Carranza-García Universidad de Sevilla P. Korysko University of Oxford
Abstract	In particle accelerators, accurate and stable beam parameters are crucial for experimental success. Traditional methods for measuring parameters like beam energy often rely on invasive techniques that disrupt experiments. This paper presents a novel, non-invasive machine learning-based approach to predict beam energy using parasitic measurements, enabling real-time estimation without interference. The method employs a predictive model optimized for one-step-ahead forecasting and uses time-series data decomposition to handle complex beam energy dynamics. Recursive prediction strategies allow the model to anticipate future variations autonomously. Preliminary results from experiments at the CLEAR accelerator demonstrate the model’s ability to capture both slow trends and rapid energy shifts, adapting to diverse experimental needs. These findings showcase the potential of machine learning to measure beam energy, offering a real-time, non-destructive alternative to conventional methods. This approach promises significant advancements in accelerator-based applications, especially where destructive techniques are impractical.
Paper	download: THPM031.pdf
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Conference	16th International Particle Accelerator Conference
Series
Location	Taipei, Taiwan
Date	01-06 Jun 2025
Publisher	JACoW Publishing, Geneva, Switzerland
Editorial Board	* Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center
Online ISBN	978-3-95450-248-6
Online ISSN	2673-5490
Received	28 May 2025
Revised	03 June 2025
Accepted	06 June 2025
Issued	05 November 2025
DOI	10.18429/JACoW-IPAC2025-THPM031
Pages	2747-2750
Copyright	Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.