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

arXiv: https://arxiv.org/abs/2311.04190 · HTML: https://mlsanity.com/paper/2311.04190/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)
- [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://mlsanity.com/paper/1902.07987/) (2019)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis](https://mlsanity.com/paper/2209.04635/) (2022)
- [Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider](https://mlsanity.com/paper/1808.00911/) (2018)
- [Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks](https://mlsanity.com/paper/1911.04831/) (2019)
- [HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions](https://mlsanity.com/paper/2106.06167/) (2021)
- [Anomaly Detection on Graph Time Series](https://mlsanity.com/paper/1708.02975/) (2017)
- [The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization](https://mlsanity.com/paper/1709.09883/) (2017)
- [Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets](https://mlsanity.com/paper/1611.06241/) (2016)

## Built on by (most cited first, 4 of 4)

- [Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments](https://mlsanity.com/paper/2405.15508/) (2024)
- [Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection](https://mlsanity.com/paper/2408.16612/) (2024)
- [Lightweight Multi-System Multivariate Interconnection and Divergence Discovery](https://mlsanity.com/paper/2404.08453/) (2024)
- [Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data](https://mlsanity.com/paper/2412.11800/) (2024)

## 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 .