# Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters

arXiv: https://arxiv.org/abs/2509.07451 · HTML: https://mlsanity.com/paper/2509.07451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider](https://mlsanity.com/paper/1808.00911/) (2018)
- [DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems](https://mlsanity.com/paper/2405.07749/) (2024)
- [Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2506.20574/) (2025)

## Abstract (arXiv)

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.