CMS develops new AI algorithm to detect anomalies

<!DOCTYPE html> <html lang="en" dir="ltr" prefix="content: http://purl.org/rss/1.0/modules/content/ dc: http://purl.org/dc/terms/ foaf: http://xmlns.com/foaf/0.1/ og: http://ogp.me/ns# rdfs: http://www.w3.org/2000/01/rdf-schema# schema: http://schema.org/ sioc: http://rdfs.org/sioc/ns# sioct: http://rdfs.org/sioc/types# skos: http://www.w3.org/2004/02/skos/core# xsd: http://www.w3.org/2001/XMLSchema# "> <head> <meta charset="utf-8" /> <script>var _paq = _paq || [];(function(){var u=(("https:" == document.location.protocol) ? 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To do this, data quality monitoring systems are in place for the various subdetectors of an experiment and they play an important role in checking the accuracy of the data. One such subdetector is the CMS electromagnetic calorimeter (ECAL), a crucial component of the CMS detector. The ECAL measures the energy of particles, mainly electrons and photons, produced in collisions at the LHC, allowing physicists to reconstruct particle decays. Ensuring the accuracy and reliability of data recorded in the ECAL is paramount for the successful operation of the experiment. During Run 3 of the LHC, which is currently ongoing, CMS researchers have developed and deployed an innovative machine-learning technique to enhance the current data quality monitoring system of the ECAL. Detailed in a recent publication, this new approach promises to make the detection of data anomalies more accurate and efficient. Such real-time capability is essential in the fast-paced LHC environment for quick detection and correction of detector issues, which in turn improves the overall quality of the data. The new system was deployed in the barrel of the ECAL in 2022 and in the endcaps in 2023. The traditional CMS data quality monitoring system consists of conventional software that relies on a combination of predefined rules, thresholds and manual inspections to alert the team in the control room to potential detector issues. This approach involves setting specific criteria for what constitutes normal data behaviour and flagging deviations. While effective, these methods can potentially miss subtle or unexpected anomalies that don't fit predefined patterns. In contrast, the new machine-learning-based system is able to detect these anomalies, complementing the traditional data quality monitoring system. It is trained to recognise the normal detector behaviour from existing good data and to detect any deviations. The cornerstone of this approach is an autoencoder-based anomaly detection system. Autoencoders, a specialised type of neural network, are designed for unsupervised learning tasks. The system, fed with ECAL data in the form of 2D images, is also adept at spotting anomalies that evolve over time thanks to novel correction strategies. This aspect is crucial for recognising patterns that may not be immediately apparent but develop gradually. The novel autoencoder-based system not only boosts the performance of the CMS detector but also serves as a model for real-time anomaly detection across various fields, highlighting the transformative potential of artificial intelligence. For example, industries that manage large-scale, high-speed data streams, such as the finance, cybersecurity and healthcare industries, could benefit from similar machine-learning-based systems for anomaly detection, enhancing their operational efficiency and reliability. CMS is just one of many experiments at CERN that is improving its performance using AI, automation and machine learning. Read more about this here. Read more: CMS briefing Paper" /> <meta property="og:image" content="https://cds.cern.ch/images//file?size=medium" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:description" content="In the quest to uncover the fundamental particles and forces of nature, one of the critical challenges facing high-energy experiments at the Large Hadron Collider (LHC) is ensuring the quality of the vast amounts of data collected. To do this, data quality monitoring systems are in place for the various subdetectors of an experiment and they play an important role in checking the accuracy of the data. One such subdetector is the CMS electromagnetic calorimeter (ECAL), a crucial component of the CMS detector. The ECAL measures the energy of particles, mainly electrons and photons, produced in collisions at the LHC, allowing physicists to reconstruct particle decays. Ensuring the accuracy and reliability of data recorded in the ECAL is paramount for the successful operation of the experiment. During Run 3 of the LHC, which is currently ongoing, CMS researchers have developed and deployed an innovative machine-learning technique to enhance the current data quality monitoring system of the ECAL. Detailed in a recent publication, this new approach promises to make the detection of data anomalies more accurate and efficient. Such real-time capability is essential in the fast-paced LHC environment for quick detection and correction of detector issues, which in turn improves the overall quality of the data. The new system was deployed in the barrel of the ECAL in 2022 and in the endcaps in 2023. The traditional CMS data quality monitoring system consists of conventional software that relies on a combination of predefined rules, thresholds and manual inspections to alert the team in the control room to potential detector issues. This approach involves setting specific criteria for what constitutes normal data behaviour and flagging deviations. While effective, these methods can potentially miss subtle or unexpected anomalies that don't fit predefined patterns. In contrast, the new machine-learning-based system is able to detect these anomalies, complementing the traditional data quality monitoring system. It is trained to recognise the normal detector behaviour from existing good data and to detect any deviations. The cornerstone of this approach is an autoencoder-based anomaly detection system. Autoencoders, a specialised type of neural network, are designed for unsupervised learning tasks. The system, fed with ECAL data in the form of 2D images, is also adept at spotting anomalies that evolve over time thanks to novel correction strategies. This aspect is crucial for recognising patterns that may not be immediately apparent but develop gradually. The novel autoencoder-based system not only boosts the performance of the CMS detector but also serves as a model for real-time anomaly detection across various fields, highlighting the transformative potential of artificial intelligence. For example, industries that manage large-scale, high-speed data streams, such as the finance, cybersecurity and healthcare industries, could benefit from similar machine-learning-based systems for anomaly detection, enhancing their operational efficiency and reliability. CMS is just one of many experiments at CERN that is improving its performance using AI, automation and machine learning. Read more about this here. Read more: CMS briefing Paper" /> <meta name="twitter:site" content="@cern" /> <meta name="twitter:title" content="CMS develops new AI algorithm to detect anomalies" /> <meta name="twitter:image" content="https://cds.cern.ch/images//file?size=medium" /> <meta name="Generator" content="Drupal 10 (https://www.drupal.org)" /> <meta name="MobileOptimized" content="width" /> <meta name="HandheldFriendly" content="true" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <link rel="icon" href="/sites/default/themes/custom/cernpublic/favicon.ico" type="image/vnd.microsoft.icon" /> <link rel="alternate" hreflang="en" href="https://home.cern/news/news/experiments/cms-develops-new-ai-algorithm-detect-anomalies" /> <link rel="alternate" hreflang="fr" href="https://home.cern/fr/news/news/experiments/cms-develops-new-ai-algorithm-detect-anomalies" /> <title>CMS develops new AI algorithm to detect anomalies | CERN</title> <link rel="stylesheet" media="all" 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To do this, data quality monitoring systems are in place for the various subdetectors of an experiment and they play an important role in checking the accuracy of the data.</p> <p class="Body" style="border:none">One such subdetector is the <a href="https://home.cern/science/experiments/cms">CMS</a> electromagnetic calorimeter (ECAL), a crucial component of the CMS <a href="https://cms.cern/detector">detector</a>. The ECAL measures the energy of particles, mainly electrons and photons, produced in collisions at the LHC, allowing physicists to reconstruct particle decays. Ensuring the accuracy and reliability of data recorded in the ECAL is paramount for the successful operation of the experiment.</p> <p class="Body" style="border:none">During Run 3 of the LHC, which is currently ongoing, CMS researchers have developed and deployed an innovative machine-learning technique to enhance the current data quality monitoring system of the ECAL. Detailed in a recent publication, this new approach promises to make the detection of data anomalies more accurate and efficient. Such real-time capability is essential in the fast-paced LHC environment for quick detection and correction of detector issues, which in turn improves the overall quality of the data. The new system was <a href="https://cms.cern/detector/measuring-energy/energy-electrons-and-photons-ecal">deployed</a> in the barrel of the ECAL in 2022 and in the endcaps in 2023.</p> <p>The traditional CMS data quality monitoring system consists of conventional software that relies on a combination of predefined rules, thresholds and manual inspections to alert the team in the control room to potential detector issues. This approach involves setting specific criteria for what constitutes normal data behaviour and flagging deviations. While effective, these methods can potentially miss subtle or unexpected anomalies that don't fit predefined patterns.</p> <p>In contrast, the new machine-learning-based system is able to detect these anomalies, complementing the traditional data quality monitoring system. It is trained to recognise the normal detector behaviour from existing good data and to detect any deviations. The cornerstone of this approach is an autoencoder-based anomaly detection system. Autoencoders, a specialised type of neural network, are designed for unsupervised learning tasks.</p> <figure role="group" class="align-center"> <img alt="On the left, an image showing lots of red, yellow, green and blue coloured pixels. On the right, an image with one red region amongst green" data-entity-type="file" data-entity-uuid="2a3bf4c9-c5d0-409a-af0e-7489941427aa" height="auto" src="/sites/default/files/inline-images/ndinmore/CMS-ecal.png" width="2719" loading="lazy"> <figcaption>An image from ECAL data with anomalous regions (left) which, when passed through the machine-learning system, produces the easily identifiable colour map on the right, showing anomalous regions in red and good regions in green. (Image: CMS experiment)<br>  </figcaption> </figure> <p class="Body" style="border:none">The system, fed with ECAL data in the form of 2D images, is also adept at spotting anomalies that evolve over time thanks to novel correction strategies. This aspect is crucial for recognising patterns that may not be immediately apparent but develop gradually.</p> <p>The novel autoencoder-based system not only boosts the performance of the CMS detector but also serves as a model for real-time anomaly detection across various fields, highlighting the transformative potential of artificial intelligence. For example, industries that manage large-scale, high-speed data streams, such as the finance, cybersecurity and healthcare industries, could benefit from similar machine-learning-based systems for anomaly detection, enhancing their operational efficiency and reliability.</p> <p>CMS is just one of many experiments at CERN that is improving its performance using AI, automation and machine learning. Read more about this <a href="https://home.cern/tags/machine-learning">here</a>.</p> <hr> <p>Read more:</p> <ul> <li><a href="https://cmsexperiment.web.cern.ch/news/harnessing-power-ai-monitor-data-quality-cms">CMS briefing</a></li> <li><a href="https://link.springer.com/article/10.1007/s41781-024-00118-z">Paper</a></li> </ul> </div> <div class="news-node-full-content-tags "> <a href="/tags/machine-learning" hreflang="en">machine learning</a><a href="/tags/cms" hreflang="en">CMS</a> </div> </div> </div> </div> </div> <div class="sidebar-right col-md-3 col-sm-12 col-xs-12"> <div class="region region-right"> <section class="views-element-container block block-views block-views-blocknews-microviews-block-12 clearfix" id="block-views-block-news-microviews-block-12"> <div class="form-group"> <div class="vertical-boxes view view-news-microviews view-id-news_microviews view-display-id-block_12 js-view-dom-id-74638cf4c9b4cbb61c510c05f2860b2acf8e00e618268bcd04126d64e1371fcf 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