自动化检测高能物理实验中的数据异常，提升监测效率与可解释性。
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments
- 构建动态演化直方图模板，结合统计与Transformer模型自适应变化条件。
- 在合成数据上实现高准确率，统计版已部署于大型强子对撞机LHCb实验。
- 方法兼具可解释性与可扩展性，适合大规模粒子物理数据监控场景。
大型粒子物理实验需依赖数据质量监控（DQM）确保数据采集可靠性，以检测探测器故障并保障数据完整性。传统上该任务依赖人工轮班员，但面对频繁运行条件变化时效率低下。本文提出DINAMO：一种新颖、可解释、鲁棒且可扩展的DQM框架，用于时间相关环境下的异常检测。该方法构建带有内置不确定性的动态演化直方图模板，包含两类版本：一是基于经典指数加权移动平均（EWMA）的统计变体，二是利用Transformer编码器增强适应性的机器学习版本。在合成数据上的实验验证表明，该方法具有高准确率、强适应性和良好可解释性。其中统计变体已正式部署于大型强子对撞机（LHCb）实验中，体现了其实际应用价值。研究代码已开源：https://github.com/ArseniiGav/DINAMO。
原文摘要 · Abstract (English)
Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive task has been handled by human shifters who struggle with frequent changes in operational conditions. We present DINAMO: a novel, interpretable, robust, and scalable DQM framework designed to automate anomaly detection in time-dependent settings. Our approach constructs evolving histogram templates with built-in uncertainties, featuring both a statistical variant - extending the classical Exponentially Weighted Moving Average (EWMA) - and a machine learning (ML)-enhanced version that leverages a transformer encoder for improved adaptability. Experimental validations on synthetic datasets demonstrate the high accuracy, adaptability, and interpretability of these methods. The statistical variant is being commissioned in the LHCb experiment at the Large Hadron Collider, underscoring its real-world impact. The code used in this study is available at https://github.com/ArseniiGav/DINAMO.
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