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

arXiv: https://arxiv.org/abs/1808.00911 · HTML: https://mlsanity.com/paper/1808.00911/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Machine Learning for Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2312.14190/) (2023)
- [Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test](https://mlsanity.com/paper/2303.05413/) (2023)
- [Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter](https://mlsanity.com/paper/2311.04190/) (2023)
- [Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2310.06047/) (2023)
- [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)
- [Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter](https://mlsanity.com/paper/2309.10157/) (2023)
- [Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters](https://mlsanity.com/paper/2509.07451/) (2025)

## Abstract (arXiv)

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.