The online Data Quality Monitoring system (DQM) of the CMS electromagnetic
calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to
quickly identify, localize, and diagnose a broad range of detector issues that
would otherwise hinder physics-quality data taking. Although the existing ECAL
DQM system has been continuously updated to respond to new problems, it remains
one step behind newer and unforeseen issues. Using unsupervised deep learning,
a real-time autoencoder-based anomaly detection system is developed that is
able to detect ECAL anomalies unseen in past data. After accounting for spatial
variations in the response of the ECAL and the temporal evolution of anomalies,
the new system is able to efficiently detect anomalies while maintaining an
estimated false discovery rate between $10^{-2}$ to $10^{-4}$, beating existing
benchmarks by about two orders of magnitude. The real-world performance of the
system is validated using anomalies found in 2018 and 2022 LHC collision data.
Additionally, first results from deploying the autoencoder-based system in the
CMS online DQM workflow for the ECAL barrel during Run 3 of the LHC are
presented, showing its promising performance in detecting obscure issues that
could have been missed in the existing DQM system.

使用无监督深度学习开发了一个实时自编码器异常检测系统，能够检测先前数据中未见的 ECAL 异常，通过考虑 ECAL 响应的空间变化和异常的时间演变，该系统能够高效地检测异常，估计的错误发现率在 $10^{-2}$ 到 $10^{-4}$ 之间，比现有基准提高了两个数量级。在 2018 年和 2022 年的 LHC 碰撞数据中验证了系统的真实性能，并首次在 LHC Run 3 期间在 ECAL 桶部署了基于自编码器的系统，展示其在检测现有 DQM 系统可能忽视的隐晦问题中表现出的良好性能。