mlsanityData to 7 Oct 2026

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

arXiv 2311.04190 ↗First posted 7 Nov 2023cs.LG cs.AIcited by 4

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Abstract (arXiv)

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system. Code: https://github.com/muleina/CMS_HCAL_ML_OnlineDQM .

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 72 references that are in the map. First posted on arXiv 7 Nov 2023.

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19 of 72 references.

References · 19

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  2. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  3. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
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  7. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
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7 more
  1. 2022A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysiscited by 10
  2. 2018Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collidercited by 8
  3. 2019Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networkscited by 5
  4. 2021HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactionscited by 3
  5. 2017Anomaly Detection on Graph Time Seriescited by 2
  6. 2017The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantizationcited by 2
  7. 2016Using LSTM recurrent neural networks for monitoring the LHC superconducting magnetscited by 1

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Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experimentscited by 2
  2. 2024Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detectioncited by 2
  3. 2024Lightweight Multi-System Multivariate Interconnection and Divergence Discoverycited by 1
  4. 2024Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Datacited by 0