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Machine learning assisted non-destructive beam profile monitoring

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Title
Machine learning assisted non-destructive beam profile monitoring
Author(s)
Zhanibek Omarov; Selcuk Haciomeroglu
Publication Date
2022-03
Journal
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, v.1026
Publisher
Elsevier B.V.
Abstract
© 2021 Elsevier B.V.We present a non-destructive beam profile monitoring concept that utilizes numerical optimization tools, namely genetic algorithm with a gradient descent-like minimization. The signal picked up by a button BPM includes information about the transverse profile content of the beam. A genetic algorithm is used to transform an arbitrary Gaussian beam in such a way that it eventually reconstructs the transverse position and the shape of the original beam to match the signal on the BPM electrodes. A case study for the developed algorithm is proton EDM experiment where conventional beam profile measurements are not possible. This method allows visualization of fairly distorted beams with non-Gaussian distributions as well.
URI
https://pr.ibs.re.kr/handle/8788114/11190
DOI
10.1016/j.nima.2021.166132
ISSN
0168-9002
Appears in Collections:
Center for Axion and Precision Physics Research(액시온 및 극한상호작용 연구단) > 1. Journal Papers (저널논문)
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