{"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/anomaly-detection-for-automated-data-quality","title":"Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector","arxiv_id":"2501.13789","date":"2025-01-23","proceeding":null,"authors":["Andrew Brinkerhoff","Chosila Sutantawibul","Robert White","Caio Daumann","Chad Freer","Indara Suarez","Samuel May","Vivan Nguyen","Jonathan Guiang","Bennett Marsh","Darin Acosta","Alex Aubuchon","Emanuela Barberis","Aaron Bundock","Evan Collins","Preston Epps","Johannes Erdmann","Henning Flaecher","Junshen Huang","Ryan Nie","Sudarshan Paramesvaran","John Rotter","Kaitlin Salyer","Siddhesh Sawant","Tanvi Sheokand","Darien Wood"],"abstract":"Successful operation of large particle detectors like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider requires rapid, in-depth assessment of data quality. We introduce the ``AutoDQM'' system for Automated Data Quality Monitoring using advanced statistical techniques and unsupervised machine learning. Anomaly detection algorithms based on the beta-binomial probability function, principal component analysis, and neural network autoencoder image evaluation are tested on the full set of proton-proton collision data collected by CMS in 2022. AutoDQM identifies anomalous ``bad'' data affected by significant detector malfunction at a rate 4 -- 6 times higher than ``good'' data, demonstrating its effectiveness as a general data quality monitoring tool.","url_abs":"https://arxiv.org/abs/2501.13789v1","url_pdf":"https://arxiv.org/pdf/2501.13789v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"anomaly-detection-for-automated-data-quality","repo_url":"https://github.com/autodqm/autodqm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}