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<p>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.</p>

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Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月9日

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  • Andrew Brinkerhoff,
  • Chosila Sutantawibul,
  • Indara Suarez,
  • Robert White,
  • Caio Daumann,
  • Jonathan Guiang,
  • Chad Freer,
  • Samuel May,
  • Bennett Marsh,
  • Darin Acosta,
  • Alex Aubuchon,
  • Emanuela Barberis,
  • Aaron Bundock,
  • Claudio Campagnari,
  • Evan Collins,
  • Preston Epps,
  • Johannes Erdmann,
  • Henning Flaecher,
  • Junshen Huang,
  • Vivan Nguyen,
  • Ryan Nie,
  • Sudarshan Paramesvaran,
  • John Rotter,
  • Kaitlin Salyer,
  • Siddhesh Sawant,
  • Tanvi Sheokand,
  • Darien Wood

摘要

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.