LINAC2024 - Proceedings
Chicago, IL, USA
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URLhttps://doi.org/10.18429/JACoW-LINAC2024-TUPB075
TitleAdvanced algorithms for linear accelerator design and operation
Authors
  • Y. Ong, L. Bellan, A. Pisent, M. Comunian, E. Fagotti, D. Bortolato, M. Montis, M. Giacchini, O. Carletto
    Istituto Nazionale di Fisica Nucleare
AbstractIn this paper, we investigate the usage of advanced algorithms adapted for optimizing the design and operation of different linear accelerators (LINACs), notably the superconducting linac ALPI at INFN-LNL and the ANTHEM BNCT facility to be constructed at Caserta, Italy. Utilizing various intelligent algorithms and machine learning techniques such as Bayesian optimization, genetic algorithms, particle swarm optimization, and surrogate modeling with artificial neural networks, we aim to enhance the design efficiency, operational reliability and adaptability of linear accelerators. Through simulations and case studies, we demonstrate the effectiveness and practical implications of these algorithms for optimizing LINAC performances across diverse applications.
Paperdownload: TUPB075.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference32nd Linear Accelerator Conference (LINAC2024)
Series
LocationChicago, IL, USA
Date25-30 Aug 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardKelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL)
Online ISBN978-3-95450-219-6
Online ISSN2226-0366
Received14 August 2024
Revised27 August 2024
Accepted28 August 2024
Issued23 October 2024
DOI10.18429/JACoW-LINAC2024-TUPB075
Pages484-487
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.