Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider

TL;DR

This paper presents the application of deep neural networks, including classifiers and autoencoders, to improve anomaly detection and data quality monitoring in the CMS muon detectors at CERN's LHC, aiming to automate and enhance data integrity assessment.

Contribution

It introduces a neural network-based approach for anomaly detection in LHC collision data, including both supervised classifiers and semi-supervised autoencoders, advancing automation in data quality monitoring.

Findings

High detection efficiency for known anomalies

Autoencoders extend detection to unforeseen failures

Potential for automating data quality assessment

Abstract

Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the data collected by the CMS muon detectors. We use deep neural networks to analyze LHC collision data, represented as images organized geographically. We train a classifier capable of detecting the known anomalous behaviors with unprecedented efficiency and explore the usage of convolutional autoencoders to extend anomaly detection capabilities to unforeseen failure modes. A generalization of this strategy could pave the way to the automation of the data quality assessment process for present and future high-energy physics experiments.