# Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector

## TL;DR

This paper introduces AutoDQM, a system that uses machine learning to detect anomalies in data from the CMS particle detector at CERN.

## Contribution

The novelty lies in applying beta-binomial and PCA-based anomaly detection for automated data quality monitoring in particle physics.

## Key findings

- AutoDQM identifies bad data at 4–6 times the rate of good data.
- The system is effective for general data quality monitoring in CMS.
- Algorithms were tested on all 2022 proton-proton collision data.

## 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.

The online version contains supplementary material available at 10.1007/s41781-025-00147-2.