In this paper, we investigate the usage of advanced algorithms, specifically Bayesian optimization, adapted for optimizing the design and operation of different linear accelerators (LINACs). The aim is to enhance the design efficiency and 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.
Advanced algorithms for linear accelerator design and operation / Ong, Y. K.; Bellan, L.; Pisent, A.; Comunian, M.; Fagotti, E.; Bortolato, D.; Montis, M.; Giacchini, M.; Carletto, O.. - (2024), pp. 484-487. ( 32nd Linear Accelerator Conference (LINAC) Chicago, Illinois, USA ) [10.18429/jacow-linac2024-tupb075].
Advanced algorithms for linear accelerator design and operation
Y. K. Ong
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2024
Abstract
In this paper, we investigate the usage of advanced algorithms, specifically Bayesian optimization, adapted for optimizing the design and operation of different linear accelerators (LINACs). The aim is to enhance the design efficiency and 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.| File | Dimensione | Formato | |
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Ong_Advanced-algorithms_2024.pdf
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