DINAMO: Dynamic and INterpretable anomaly MOnitoring for large-scale particle physics experiments

Abstract 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 struggle with frequent changes in operational conditions. We present D ynamic and IN terpretable A nomaly MO nitoring: a novel, interpretable, robust, and scalable DQM framework designed to automate anomaly detection in time-dependent settings. Our approach constructs evolving histogram templates with built-in uncertainties, featuring both a statistical variant—extending the classical exponentially weighted moving average—and a machine learning-enhanced version that leverages a transformer encoder for improved adaptability. Experimental validations on synthetic datasets demonstrate the high accuracy, adaptability, and interpretability of these methods. The statistical variant is being commissioned in the LHCb experiment at the large hadron collider, underscoring its real-world impact.