CSNS残余气体电离型束流剖面测量畸变校正

刘孟宇; 孙纪磊; 徐智虹; 杨涛; 聂小军; 黄蔚玲; 杨仁俊; 康玲; 刘华昌

doi:10.11884/HPLPB202537.240271

CSNS残余气体电离型束流剖面测量畸变校正

doi: 10.11884/HPLPB202537.240271

刘孟宇^{1, 2, 3,},
孙纪磊^{1, 2, 3},
徐智虹^{1, 3},
杨涛^{1, 3},
聂小军^{1, 3},
黄蔚玲^{1, 2, 3, ,},
杨仁俊^{1, 2, 3, ,},
康玲^{1, 2, 3},
刘华昌^{1, 2, 3}

1.
中国科学院高能物理研究所，北京 100049
2.
中国科学院大学，北京 100049
3.
散裂中子源科学中心，广东东莞 523803

基金项目: 广东省自然科学基金(2021A1515010269); 国家自然科学基金项目(12275294)

详细信息

作者简介:
刘孟宇，liumy@ihep.ac.cn

通讯作者:
黄蔚玲，huangwei@ihep.ac.cn

杨仁俊，yangrenjun@ihep.ac.cn。

中图分类号: TL506
计量
- 文章访问数: 44
- HTML全文浏览量: 31
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2024-08-21
- 修回日期: 2025-02-17
- 录用日期: 2025-02-17
- 网络出版日期: 2025-04-07

Ddistortion correction of CSNS Ionization Profile Monitor measurement

Liu Mengyu^{1, 2, 3
,},
Sun Jilei^{1, 2, 3},
Xu Zhihong^{1, 3},
Yang Tao^{1, 3},
Nie Xiaojun^{1, 3},
Huang Weiling^{1, 2, 3
, ,},
Yang Renjun^{1, 2, 3
, ,},
Kang Ling^{1, 2, 3},
Liu HuangChang^{1, 2, 3}

1.
Institute of High Energy Physics, CAS, Beijing 100049, China
2.
University of China Academy of Sciences, CAS, Beijing 100049, China
3.
Spallation Neutron Source Science Center, Dongguan 523803, China

摘要

摘要: ，残余气体电离型束流剖面探测器（IPM）可以实时提供高流强质子加速器调试和稳定运行所需的关键束流分布信息, 中国散裂中子源（CSNS）直线加速器IPM装置采用紧凑型结构设计，通过离子模式收集并由光学成像系统实现束流横向一维分布测量。电极板开孔处的蜂窝网格结构阻挡部分离子或电子进入微通道板，造成成像阴影并引入束流分布畸变，利用离线算法进行校正。利用偏微分修复和机器学习算法对CSNS直线加速器IPM蜂窝网格造成的成像阴影和束流分布畸变进行了校正处理，采用无监督机器学习方法DIP校正后的束流尺寸与理论预期偏差低于10%并保持较好信噪比。
- 残余气体电离型束流剖面探测器 /
- 机器学习 /
- 图像校正
Abstract: The ionization profile monitor (IPM) can provide critical beam distribution information required for real-time debugging and stable operation of high-current proton accelerators. The IPM system of the China Spallation Neutron Source (CSNS) Linac adopts a compact structural design. It collects data in ion mode and performs one-dimensional transverse beam distribution measurement through an optical imaging system. However, the honeycomb mesh structure at the electrode plate apertures blocks some ions or electrons from entering the microchannel plate, causing imaging shadows and introducing beam distribution distortion. Offline numerical algorithms must be used for correction. In this paper, partial differential equation (PDE) restoration and machine learning algorithms are used to correct the imaging shadows and beam distribution distortion caused by the honeycomb mesh of the IPM in the CSNS linac. The unsupervised machine learning method DIP (Deep Image Prior) was employed, and the corrected beam size deviates from the theoretical expectation by less than 10%, while maintaining a good signal-to-noise ratio.
- ionization profile monitor /
- machine learning /
- image correction

HTML全文

图 1 CSNS-LRBT段IPM及其工作原理

Figure 1. CSNS LR- IPM and its principle

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图 2 残余气体为氢气H₂及氦气He时电子阻止本领与入射粒子能量的关系

Figure 2. The relationship between residual gas stopping power and incident particle energy when the residual gas is hydrogen H₂ and helium He

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图 3 引导场对剖面畸变的矫正曲线及对束流的影响

Figure 3. Correction curve of profile distortion by guidance field and its influence on beam loss

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图 4 LR-IPM验证装置下极板上的匀场栅网

Figure 4. Honey comb on the cathode plate of LR-IPM in CSNS

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图 5 屏蔽附近Ex分布及对测量结果的影响

Figure 5. Ex distribution near the honey comb and its impact on measurement results

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图 6 安装电磁屏蔽后IPM获取的图像及一维投影

Figure 6. The image obtained by the IPM after installing the electromagnetic shielding (left) and the integration results (right)

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图 7 DIP图像处理流程

Figure 7. DIP image processing workflow

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图 8 不同算法的信噪比及修复效果对比

Figure 8. Comparison of PSNR and repair effects of different algorithms

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