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

TL;DR

This paper introduces GraphSTAD, a semi-supervised spatio-temporal anomaly detection system using graph neural networks for real-time data quality monitoring of the CMS Hadron Calorimeter, demonstrating high accuracy and integration into CERN's data pipeline.

Contribution

The paper presents a novel graph neural network-based system for anomaly detection in high-energy physics detector data, enabling real-time monitoring and fault diagnosis.

Findings

Achieves production-level accuracy in detecting detector faults

Successfully integrated into CMS data quality monitoring system

Outperforms benchmark models in anomaly detection tasks

Abstract

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 .