DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments
This addresses the resource-intensive task of detecting detector malfunctions in large-scale particle physics experiments, which traditionally relies on human shifters struggling with operational changes.
The paper tackles automated anomaly detection for data quality monitoring in particle physics experiments by introducing DINAMO, a framework that constructs evolving histogram templates with built-in uncertainties. The statistical variant is being commissioned in the LHCb experiment at the Large Hadron Collider, demonstrating real-world impact.
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 DINAMO: 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 (EWMA) - and a machine learning (ML)-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. The code used in this study is available at https://github.com/ArseniiGav/DINAMO.