[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127184-en":3,"doc-seo-127184-105":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},127184,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring - Abstract","A real-time semi-supervised anomaly detection system using an autoencoder is developed for the CMS electromagnetic calorimeter online Data Quality Monitoring at the CERN LHC. The method improves detection by leveraging both the time-evolution of anomalies and spatial variations in detector response, enabling efficient anomaly identification with a very low false discovery rate. Validation uses anomalies found in 2018 and 2022 LHC collision data. Initial deployment results in the CMS online DQM workflow during Run 3 show detection of issues missed by existing monitoring.","arXiv :2407 .20278v1 [physics .ins-det] 25 Jul 2024  \nAnomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring  \nAbhirami Harilal1 , ∗ , Kyungmin Park1 , ∗∗ , and Manfred Paulini1 , ∗∗∗ (On behalf of the CMS Collaboration)  \n1Carnegie Mellon University, Pittsburgh, Pennsylvania, USA  \nAbstract. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning has been developed for the online Data Quality Monitoring system of the electromagnetic calorimeter of the CMS detector at the CERN LHC. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. Additionally, the first results from deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.  \n1 Introduction  \nThe central feature of the CMS experiment recording proton-proton collision data produced by the CERN LHC is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter, each composed of a barrel and two endcap sections. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [1] .  \nThe CMS electromagnetic calorimeter provides homogeneous coverage in pseudorapidity |η| \u003C 1.48 in a barrel region (EB) and 1 .48 \u003C |η| \u003C 3.0 in two endcap regions (EE+ and EE−) as shown in Fig. 1. Preshower detectors consisting of two planes of silicon sensors interleaved with three radiation lengths of lead are located in front of each endcap detector. The ECAL consists of 75 848 lead tungstate (PbWO4 ) crystals. The barrel granularity is 360-fold in ϕ and (2×85)-fold in η provided by a total of 61 200 crystals, with each crystal having a dimension of 0.0174×0.0174 in ∆η × ∆ϕ space, while each endcap is divided into two halves, with each comprising 3662 crystals.  \nThe CMS data quality monitoring (DQM) system [2] is a crucial operational tool to record high-quality physics data. Presently, the DQM consists of a software system that produces a set of histograms that are based on a preliminary analysis of a subset of data collected by the CMS  \n∗ e-mail: [aharilal@andrew.cmu.edu](aharilal@andrew.cmu.edu)[ ](aharilal@andrew.cmu.edu)∗∗[e-mail: kyungmip@andrew.cmu.edu](e-mail: kyungmip@andrew.cmu.edu)[ ](e-mail: kyungmip@andrew.cmu.edu)∗∗∗ e-mail: [paulini@andrew.cmu.edu](paulini@andrew.cmu.edu)  \nFigure 1. Schematic view of the ECAL showing the cylindrical barrel closed by the two endcap regions with one half endcap displayed.  \ndetector. Conventional cut-based thresholds are used to define quality flags on these histograms which are monitored continuously by a DQM shifter in the CMS control room who reports on any apparent irregularities observed. While this system has proven to be dependable, the changing running conditions and increasing LHC collision rates, together with aging electronics, bring forth failure modes that are newer and harder to predict.  \nThere are two kinds of histograms present in the ECALDQM: “Occupancy-style” histograms shown at the top of Fig. 2 filled with critical quantities from the real-time detector data and “Quality-style” histograms displayed at the bottom of Fi","cbCaigirCfvPjEiN","https://ap.wps.com/l/cbCaigirCfvPjEiN","pdf",1561185,1,5,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Machine Learning Based Anomaly Detection Strategy","[{\"question\":\"What anomaly detection approach is used for the CMS ECAL online DQM?\",\"answer\":\"A real-time semi-supervised autoencoder-based system is used to learn patterns from certified good data and flag deviations as anomalies.\"},{\"question\":\"How does the proposed method improve anomaly detection performance?\",\"answer\":\"It maximizes performance by exploiting the time-dependent evolution of anomalies and spatial variations in the detector response.\"},{\"question\":\"How was the system validated and where is it deployed?\",\"answer\":\"Performance was validated using anomalies from 2018 and 2022 LHC collision data, and first deployment results are presented for the CMS online DQM workflow at the beginning of Run 3.\"}]","Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring - Abstract | PDF",1785937380,13,{"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},"anomaly-detection-based-on-machine-learning-for-the-cms-electromagnetic-calorimeter-online-data-quality-monitoring-abstract","",{"@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/anomaly-detection-based-on-machine-learning-for-the-cms-electromagnetic-calorimeter-online-data-quality-monitoring-abstract/127184/",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},"What anomaly detection approach is used for the CMS ECAL online DQM?","Question",{"text":75,"@type":76},"A real-time semi-supervised autoencoder-based system is used to learn patterns from certified good data and flag deviations as anomalies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method improve anomaly detection performance?",{"text":80,"@type":76},"It maximizes performance by exploiting the time-dependent evolution of anomalies and spatial variations in the detector response.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the system validated and where is it deployed?",{"text":84,"@type":76},"Performance was validated using anomalies from 2018 and 2022 LHC collision data, and first deployment results are presented for the CMS online DQM workflow at the beginning of Run 3.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"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":21,"slug":137},19,"General","general"]