{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detector-monitoring-with-artificial-neural","title":"Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider","arxiv_id":"1808.00911","date":"2018-07-27","proceeding":null,"authors":["Adrian Alan Pol","Gianluca Cerminara","Cecile Germain","Maurizio Pierini","Agrima Seth"],"abstract":"Reliable data quality monitoring is a key asset in delivering collision data\nsuitable for physics analysis in any modern large-scale High Energy Physics\nexperiment. This paper focuses on the use of artificial neural networks for\nsupervised and semi-supervised problems related to the identification of\nanomalies in the data collected by the CMS muon detectors. We use deep neural\nnetworks to analyze LHC collision data, represented as images organized\ngeographically. We train a classifier capable of detecting the known anomalous\nbehaviors with unprecedented efficiency and explore the usage of convolutional\nautoencoders to extend anomaly detection capabilities to unforeseen failure\nmodes. A generalization of this strategy could pave the way to the automation\nof the data quality assessment process for present and future high-energy\nphysics experiments.","url_abs":"http://arxiv.org/abs/1808.00911v1","url_pdf":"http://arxiv.org/pdf/1808.00911v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detector-monitoring-with-artificial-neural","repo_url":"https://github.com/MantasPtr/CERN-CMS-DQM-DT-visualization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.00911","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}