Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector

EPJ Research Infrastructures 2026 Open access

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Authors: Andrew Brinkerhoff, Chosila Sutantawibul, Indara Suarez, Robert White, Caio Daumann, Jonathan Guiang, Chad Freer, Samuel May, Bennett Marsh, Darin Acosta, Alex Aubuchon, Emanuela Barberis
+15 more Aaron Bundock, Claudio Campagnari, Evan Collins, Preston Epps, J. Erdmann, Henning U Flaecher, Junshen Huang, Vivan Nguyen, Ryan Nie, Sudarshan Paramesvaran, John Rotter, Kaitlin Salyer, Siddhesh Sawant, Tanvi Sheokand, Darien Wood
DOI: 10.1007/s41781-025-00147-2
Cited by: 1
References: 18

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 and principal component analysis 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. Supplementary Information: The online version contains supplementary material available at 10.1007/s41781-025-00147-2.

Fields of Study & Subjects

Particle physics theoretical and experimental studies Particle Detector Development and Performance Anomaly Detection Techniques and Applications

Publication details

Journal / source
EPJ Research Infrastructures
Publisher
Springer Science and Business Media LLC
Volume
10
Issue
1
Pages
4–4
Publication date
9 February 2026
Language
EN