Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring
Abstract
A real-time autoencoder-based anomaly detection system using semi-supervised machine learning has been developed for the online Data Quality Monitoring system of the electromagnetic calorimeter of the CMS detector at the CERN LHC. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. Additionally, the first results from deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.
Cite
@article{arxiv.2407.20278,
title = {Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring},
author = {Abhirami Harilal and Kyungmin Park and Manfred Paulini},
journal= {arXiv preprint arXiv:2407.20278},
year = {2024}
}
Comments
Submitted to the Proceedings of the 20th International Conference on Calorimetry in Particle Physics, CALOR 2024, Tsukuba, Japan, May 20-24, 2024