SciPost Submission Page
Autoencoder-based time series anomaly detection for ATLAS Liquid Argon calorimeter data quality monitoring
by Vilius Cepaitis, Steven Schramm
Submission summary
| Authors (as registered SciPost users): | Vilius Cepaitis |
| Submission information | |
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| Preprint Link: | scipost_202509_00060v1 (pdf) |
| Date submitted: | Sept. 30, 2025, 11:07 a.m. |
| Submitted by: | Vilius Cepaitis |
| 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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| Approach: | Experimental |
Abstract
The ATLAS experiment at the LHC employs comprehensive data quality monitoring procedures to ensure high-quality physics data. This contribution presents a long short-term memory autoencoder-based algorithm for detecting anomalies in ATLAS Liquid Argon calorimeter data, represented as multidimensional time series of statistical moments of energy cluster properties. Trained on good-quality data, the model identifies anomalous intervals. Validation is performed using a known short-term issue of noise bursts, and the potential for broader application to transient calorimeter issues is discussed.
Current status:
Voting in preparation