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.

通过使用半监督机器学习的实时自编码器异常检测系统，可以快速识别、定位和诊断可能影响物理数据质量的 CMS 电磁量能器的各种问题，同时利用时间依赖性演化和探测器响应的空间变化，最大程度提高异常检测性能，该系统在 2018 年和 2022 年的 LHC 碰撞数据中进行了验证，并且在 LHC Run 3 开始阶段将自编码器系统部署在 CMS 在线数据质量监测工作流中的初步结果表明其可以检测到现有系统所错过的问题。

基于自编码器的 CMS 电磁量能器的在线数据质量监测异常检测系统

Autoencoder-based Anomaly Detection System for Online Data Quality  Monitoring of the CMS Electromagnetic Calorimeter

We explore the metric space of jets using public collider data from the CMS
experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at
the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central
jets with transverse momentum above 375 GeV. To validate the performance of the
CMS detector in reconstructing the energy flow of jets, we compare the CMS Open
Data to corresponding simulated data samples for a variety of jet kinematic and
substructure observables. Even without detector unfolding, we find very good
agreement for track-based observables after using charged hadron subtraction to
mitigate the impact of pileup. We perform a range of novel analyses, using the
"energy mover's distance" (EMD) to measure the pairwise difference between jet
energy flows. The EMD allows us to quantify the impact of detector effects,
visualize the metric space of jets, extract correlation dimensions, and
identify the most and least typical jet configurations. To facilitate future
jet studies with CMS Open Data, we make our datasets and analysis code
available, amounting to around two gigabytes of distilled data and one hundred
gigabytes of simulation files.

本文使用 CMS 探测器公开的对撞机数据探索喷注的度量空间，使用 “能流移动距离”（EMD）来衡量喷注能流的成对差异，量化探测器效应，提取相关维度，并确定最典型和最不典型的喷注配置。