Weekly Group Seminars

Anomaly Detection for DQM: automation and ML techniques

Name: Anomaly Detection for DQM: automation and ML techniques
Start: 2026-05-27T16:00:00+01:00
End: 2026-05-27T17:00:00+01:00
Location: No location set

by Dr Rob White

Wednesday 27 May 2026, 16:00 → 17:00 Europe/London

Berry Lecture Room (3.21)

Description

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 the CMS Level 1 Trigger 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.

Join Zoom Meeting
https://cern.zoom.us/j/66923142456?pwd=pVCSHwJ6Mo5SbbNbceRuagSOVPR823.1
Meeting ID: 669 2314 2456
Passcode: 367220