Speaker
Description
Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive task has been handled by human shifters who may struggle with frequent changes in operational conditions. Instead, to simplify and automate the shifters' work, we present DINAMO: a dynamic and interpretable anomaly detection framework for large-scale particle physics experiments in time-varying settings [1]. Our approach constructs evolving histogram templates with built-in uncertainties, featuring both a statistical variant - extending the classical Exponentially Weighted Moving Average (EWMA) - and a machine learning (ML)-enhanced version that leverages a transformer encoder for improved adaptability and accuracy.
Both approaches are studied using comprehensive synthetic datasets that emulate key features of real particle physics detectors. Validations on a large number of such datasets demonstrate the high accuracy, adaptability, and interpretability of these methods, with the statistical variant being commissioned in the LHCb experiment at the Large Hadron Collider, underscoring its real-world impact.
[1] A. Gavrikov, J. García Pardiñas, and A. Garfagnini, DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments (2025). Link: https://arxiv.org/abs/2501.19237
| AI keywords | Anomaly Detection; Interpretability; Online Learning; Transformer |
|---|
