The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online Data Quality Monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. 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.

翻译：CMS探测器是用于探测大型强子对撞机产生的高能对撞的通用装置。CMS电磁量能器的在线数据质量监测是一项至关重要的运行工具，它使探测器专家能够快速识别、定位和诊断可能影响物理数据质量的各种探测器问题。本文提出了一种采用半监督机器学习的实时自编码器异常检测系统，用于检测CMS电磁量能器数据中的异常。我们引入了一种新方法，通过利用异常的时间演化特性以及探测器响应的空间变化，最大化异常检测性能。该基于自编码器的系统能够高效检测异常，同时保持极低的误报率。系统性能已通过2018年和2022年LHC对撞数据中发现的异常得到验证。此外，本文首次展示了在LHC第三轮运行初期将该自编码器系统部署于CMS在线数据质量监测工作流的结果，表明其能够检测出现有系统遗漏的问题。