Speaker
Vilius Cepaitis
(University of Geneva)
Description
The ATLAS detector at the LHC has comprehensive data quality monitoring procedures for ensuring high quality physics analysis data. This contribution introduces a long short-term memory (LSTM) autoencoder-based algorithm designed to identify detector anomalies in ATLAS liquid argon calorimeter data. The data is represented as a multidimensional time series, corresponding to statistical moments of energy cluster properties. The model is trained in an unsupervised fashion on good-quality data and is evaluated to detect anomalous intervals of data-taking. The liquid argon noise burst phenomenon is used to validate the approach. The potential of applying such an algorithm to detect arbitrary transient calorimeter detector issues is discussed.
| AI keywords | anomaly detection, data quality monitoring, LSTM, autoencoder, time series |
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Author
Vilius Cepaitis
(University of Geneva)
Co-author
Steven Schramm
(University of Geneva)
