利用机器学习的加速器光束相空间层析成像技术考虑光束线组件的变化

Andrzej Wolski, Diego Botelho, David Dunning, Amelia E. Pollard
{"title":"利用机器学习的加速器光束相空间层析成像技术考虑光束线组件的变化","authors":"Andrzej Wolski, Diego Botelho, David Dunning, Amelia E. Pollard","doi":"arxiv-2405.10028","DOIUrl":null,"url":null,"abstract":"We describe a technique for reconstruction of the four-dimensional transverse\nphase space of a beam in an accelerator beamline, taking into account the\npresence of unknown errors on the strengths of magnets used in the data\ncollection. Use of machine learning allows rapid reconstruction of the\nphase-space distribution while at the same time providing estimates of the\nmagnet errors. The technique is demonstrated using experimental data from\nCLARA, an accelerator test facility at Daresbury Laboratory.","PeriodicalId":501318,"journal":{"name":"arXiv - PHYS - Accelerator Physics","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accelerator beam phase space tomography using machine learning to account for variations in beamline components\",\"authors\":\"Andrzej Wolski, Diego Botelho, David Dunning, Amelia E. Pollard\",\"doi\":\"arxiv-2405.10028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a technique for reconstruction of the four-dimensional transverse\\nphase space of a beam in an accelerator beamline, taking into account the\\npresence of unknown errors on the strengths of magnets used in the data\\ncollection. Use of machine learning allows rapid reconstruction of the\\nphase-space distribution while at the same time providing estimates of the\\nmagnet errors. The technique is demonstrated using experimental data from\\nCLARA, an accelerator test facility at Daresbury Laboratory.\",\"PeriodicalId\":501318,\"journal\":{\"name\":\"arXiv - PHYS - Accelerator Physics\",\"volume\":\"3 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Accelerator Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2405.10028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Accelerator Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.10028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

我们描述了一种用于重建加速器光束线中光束的四维横向相位空间的技术,其中考虑到了数据采集中使用的磁铁强度存在的未知误差。利用机器学习可以快速重建相空间分布,同时提供磁体误差的估计值。该技术利用达斯伯里实验室的加速器测试设备CLARA 的实验数据进行了演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Accelerator beam phase space tomography using machine learning to account for variations in beamline components
We describe a technique for reconstruction of the four-dimensional transverse phase space of a beam in an accelerator beamline, taking into account the presence of unknown errors on the strengths of magnets used in the data collection. Use of machine learning allows rapid reconstruction of the phase-space distribution while at the same time providing estimates of the magnet errors. The technique is demonstrated using experimental data from CLARA, an accelerator test facility at Daresbury Laboratory.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Semi-analytical algorithms to study longitudinal beam instabilities in double rf systems Exploring the Potential of Resonance Islands and Bent Crystals for a Novel Slow Extraction from Circular Hadron Accelerators Space Charge and Future Light Sources Beam Dynamics simulations for ERDC project -- SRF linac for industrial use Realizing Steady-State Microbunching with Optical Stochastic Crystallization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1