https://doi.org/10.1007/s41781-025-00147-2
Research
Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
1
Baylor University,
Waco, USA
2
Boston University, Boston
, USA
3
INFN Sezione di Torino, Turin
, Italy
4
Massachusetts Institute of Technology,
Cambridge, USA
5
Northeastern University,
Boston, USA
6
Panjab University,
Chandigarh, India
7
Rice University,
Houston, USA
8
RWTH Aachen University III. Physikalisches Institut A,
Aachen, Germany
9
University of Bristol,
Bristol, England
10
University of California Santa Barbara,
Santa Barbara, USA
11
University of Florida,
Gainesville, USA
a
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Received:
11
April
2025
Accepted:
30
September
2025
Published online:
9
February
2026
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.
Key words: Particle Physics / Anomoly Detection / PCA / Data Quality Monitoring
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s41781-025-00147-2.
© The Author(s) 2026
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