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.
Brinkerhoff et al. (Mon,) studied this question.