Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters
This addresses the challenge of scarce and unreliable labeled data for anomaly detection in physics experiments, though it appears incremental as it applies existing simulation and detection methods to a specific domain.
The paper tackled the problem of anomaly detection in multivariate time series from high-energy physics calorimeters by generating synthetic data with injected anomalies using the Lorenzetti Simulator, and found that transformer-based and other deep learning models could be effectively assessed for sensitivity to these anomalies.
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.