用合成数据提升高能物理探测器异常检测能力
Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters
- 基于Lorenzetti模拟器生成带异常的合成时间序列
- 在无标签数据下验证多种深度学习模型检测效果
- 适用于不同探测器设计与缺陷类型，通用性强
多变量时间序列中的异常检测对保障物理实验数据质量至关重要。准确识别意外错误或缺陷发生时刻虽关键却困难，原因在于标签稀缺、异常类型未知以及多维间复杂相关性。为解决标注数据稀缺且不可靠的问题，本文采用Lorenzetti模拟器生成注入了量能器异常的合成事件，并评估了多种时间序列异常检测方法的敏感性，包括基于Transformer及其他深度学习模型。该方法具有通用性，可适用于不同探测器设计与缺陷类型。
原文摘要 · Abstract (English)
Anomaly detection in multivariate time series is crucial to ensure the quality of data coming from a physics experiment. Accurately identifying the moments when unexpected errors or defects occur is essential, yet challenging due to scarce labels, unknown anomaly types, and complex correlations across dimensions. To address the scarcity and unreliability of labelled data, we use the Lorenzetti Simulator to generate synthetic events with injected calorimeter anomalies. We then assess the sensitivity of several time series anomaly detection methods, including transformer-based and other deep learning models. The approach employed here is generic and applicable to different detector designs and defects.
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