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Main Authors: Brinkerhoff, Andrew, Sutantawibul, Chosila, White, Robert, Daumann, Caio, Freer, Chad, Suarez, Indara, May, Samuel, Nguyen, Vivan, Guiang, Jonathan, Marsh, Bennett, Acosta, Darin, Aubuchon, Alex, Barberis, Emanuela, Bundock, Aaron, Collins, Evan, Epps, Preston, Erdmann, Johannes, Flaecher, Henning, Huang, Junshen, Nie, Ryan, Paramesvaran, Sudarshan, Rotter, John, Salyer, Kaitlin, Sawant, Siddhesh, Sheokand, Tanvi, Wood, Darien
Format: Preprint
Published: 2025
Subjects:
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
Instrumentation and Detectors
Online Access:https://arxiv.org/abs/2501.13789
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Table of Contents:
  • 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, principal component analysis, and neural network autoencoder image evaluation 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.

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