SciPost Submission Page
Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters
by Laura Boggia, Bogdan Malaescu
Submission summary
| Authors (as registered SciPost users): | Laura Boggia |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.07451v3 (pdf) |
| Date submitted: | Dec. 4, 2025, 3:49 p.m. |
| Submitted by: | Laura Boggia |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
ChatGPT (GPT-5 and GPT-4, free version) for suggestions regarding language for writing of the article.
Abstract
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.
Author comments upon resubmission
Thank you for your recommandations and please accept my apologies for the late resubmission.
I hope this new version addressed all the issues raised in the last comments (namely the format and coherence of the references).