[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127382-en":3,"doc-seo-127382-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127382,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators","Industrial particle accelerators operate in harsher environments than research machines, increasing noise in RF and electronic systems. Mass production and limited optimization effort for each unit can cause performance shortfalls relative to the underlying hardware. This work applies machine learning to reduce noise in RF signals used for pulse-to-pulse feedback. Algorithms and results are reviewed for simulated RF systems, along with planned next steps toward deployment on industrial accelerator hardware.","arXiv :2409 .03931v1 [physics .acc-ph] 5 Sep 2024  \nPrepared for submission to JINST  \nMachine Learning for Reducing Noise in  \nRF Control Signals at Industrial Accelerators  \nM. Henderson, 􀀰, 1 J. P. Edelen, 􀀰 J. Einstein-Curtis, 􀀰 C. C. Hall, 􀀰  \nJ. A. Diaz Cruz, 􀀱 and A. L. Edelen􀀱  \n􀀰 RadiaSoft LLC, Boulder CO, USA  \n􀀱 SLAC, Menlo Park CA, USA  \nE-mail: [mhenderson@radiasoft.net](mhenderson@radiasoft.net)  \nAbstract: Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here wereview our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.  \nKeywords: machine learning, RF controls, noise reduction  \n1Corresponding author.  \nContents  \n1 Introduction 1  \n2 Data Generation 2  \n3 Overview of Methods 2  \n3.1 Kalman Filter 2  \n3.2 Feed-forward Autoencoder 3  \n3.3 Convolutional Autoencoder 3  \n3.4 Variational Recurrent Autoencoders 3  \n3.5 Noise Reduction Analysis 4  \n4 Results 4  \n4.1 Single Sample Analysis 4  \n4.2 Statistical Comparisons 6  \n5 Conclusions 7  \n1 Introduction  \nMachine learning (ML) has been identified as having the potential for significant impact on the modeling, operation, and control of particle accelerators [1, 2] . For machine diagnostics specifically, there have been numerous efforts to improve measurement capabilities and detect faulty instruments. For example, many developments have focused on improving beam position monitors (BPMs), including the removal of poorly performing BPMs. Work done at the Large Hadron Collider (LHC) identified faulty BPMs prior to application of standard optics correction algorithms [3] . More recently, ML methods have improved optics measurements from beam position monitor data [4] .  \nWhile ML continues to be a popular area of research for accelerator diagnostics, thereis a lack of engineering knowledge when it comes to the application of ML for RF systems. As the demands on industrial accelerators increase, so does their complexity and the need for refined control methods. While ML techniques have the potential to improve accelerator operations generally, they show particular promise for systems operating in industrial environments. The ability to improve signal to noise ratio and extract key characteristics from RF signals would greatly improve the ability of industrial systems to meet growing performance demands.  \nAutoencoders (AEs) are an ML technique with established application to noise removal for diagnostic signals. Variational Autoencoders (VAEs) are especially adept at removing noise due to the enforcement of a smoothness criterion in the model latent-space [5] . This feature of VAEs has seen them be applied to several physical process models, such as those in gravitational wave research [6, 7] and geophysical data [8] . Variational recurrent autoencoders (VRAEs) have the  \nadded advantage of being well-suited to sequential data. Autoencoders have also been applied to simulated ring BPM data to remove both additive white Gaussian (AWG) noise and noise of different colors (i.e., power law spectra) [9] .  \nIn this paper, we evaluate several ML methods for noise removal, including AEs, convolutional AEs (CAEs), and VRAEs, and compare them with a more conventional Kalman filter (KF) ","cbCaib1hjm4PFLhb","https://ap.wps.com/l/cbCaib1hjm4PFLhb","pdf",1166704,1,9,"English","en",105,"# Introduction\n# Data Generation\n# Overview of Methods\n## Kalman Filter\n## Feed-forward Autoencoder\n## Convolutional Autoencoder\n## Variational Recurrent Autoencoders\n## Noise Reduction Analysis\n# Results\n## Single Sample Analysis\n## Statistical Comparisons\n# Conclusions","[{\"question\":\"为什么工业加速器的RF控制信号噪声更难处理？\",\"answer\":\"工业加速器通常处于比研究加速器更脏、更复杂的环境，导致RF和电子系统噪声增加；同时批量制造使得单机优化投入不足，容易出现性能低于硬件潜力的情况。\"},{\"question\":\"本研究主要用什么方法降低脉冲到脉冲反馈中的RF信号噪声？\",\"answer\":\"使用多种机器学习去噪方法，包括自编码器（AE）、卷积自编码器（CAE）与变分循环自编码器（VRAE），并与传统卡尔曼滤波（KF）进行对比。\"},{\"question\":\"数据生成与评估实验是如何组织的？\",\"answer\":\"通过RadiaSoft的RF模拟器生成数据，使用散射矩阵与有效RLC电路模型描述传播与腔体动力学，并加入加性白高斯噪声；随后在不同训练/测试数据集上评估各模型，并给出单样本分析与统计对比结果。\"}]","Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators | PDF",1785938601,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-reducing-noise-in-rf-control-signals-at-industrial-accelerators","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-reducing-noise-in-rf-control-signals-at-industrial-accelerators/127382/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么工业加速器的RF控制信号噪声更难处理？","Question",{"text":75,"@type":76},"工业加速器通常处于比研究加速器更脏、更复杂的环境，导致RF和电子系统噪声增加；同时批量制造使得单机优化投入不足，容易出现性能低于硬件潜力的情况。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本研究主要用什么方法降低脉冲到脉冲反馈中的RF信号噪声？",{"text":80,"@type":76},"使用多种机器学习去噪方法，包括自编码器（AE）、卷积自编码器（CAE）与变分循环自编码器（VRAE），并与传统卡尔曼滤波（KF）进行对比。",{"name":82,"@type":73,"acceptedAnswer":83},"数据生成与评估实验是如何组织的？",{"text":84,"@type":76},"通过RadiaSoft的RF模拟器生成数据，使用散射矩阵与有效RLC电路模型描述传播与腔体动力学，并加入加性白高斯噪声；随后在不同训练/测试数据集上评估各模型，并给出单样本分析与统计对比结果。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]