Determination of the Half-Beam cone angle for Pierce electron gun design using an artificial neural network

IF 4.4 2区物理与天体物理 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Results in Physics Pub Date : 2025-04-14 DOI:10.1016/j.rinp.2025.108256

R. Panahi , S.A.H. Feghhi , Sh. Sanaye Hajari , M. Khorsandi

引用次数: 0

Abstract

Various electron gun design methods, especially iterative and noniterative, are generally utilized for preliminary electron gun design based on the input design beam parameters. In the electron gun design methods, calculating the half-beam cone angle is crucial, as it significantly influences the determination of the electron gun’s geometry. In this paper, an Artificial Neural Network (ANN) model was proposed for predicting the half-beam cone angle. The ANN model was trained and tested using experimental data obtained from previously documented studies on electron guns. The predicted results of the proposed ANN model were compared with three well-known methods in the electron gun design: iterative method, noniterative method, and modified noniterative method. The comparison indicates that the proposed ANN model achieves better performance than the other methods, with a Main Relative Error (MRE) of less than 5%.

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用人工神经网络确定皮尔斯电子枪半束锥角

基于输入设计光束参数对电子枪进行初步设计，一般采用各种电子枪设计方法，特别是迭代和非迭代方法。在电子枪的设计方法中，半束锥角的计算是至关重要的，它对电子枪几何形状的确定有着重要的影响。本文提出了一种预测半光束锥角的人工神经网络模型。人工神经网络模型的训练和测试使用的实验数据来自先前记录的电子枪研究。将该模型的预测结果与电子枪设计中常用的迭代法、非迭代法和改进非迭代法进行了比较。对比表明，该模型的主要相对误差（MRE）小于5%，取得了较好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Results in Physics MATERIALS SCIENCE, MULTIDISCIPLINARYPHYSIC-PHYSICS, MULTIDISCIPLINARY

CiteScore

8.70

自引率

9.40%

发文量

754

审稿时长

50 days

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