🤖 AI Summary
In high-energy collider experiments, conventional data quality monitoring (DQM) heavily relies on real detector data, suffering from detector-specific constraints, high data acquisition costs, and extensive manual intervention—making it ill-suited for the scale and complexity of next-generation experiments. This work proposes a simulation-driven machine learning DQM framework: leveraging an enhanced Delphes fast simulator to generate controllable, labeled anomalous events; training neural networks for event-level integrity and consistency monitoring; and enabling subsystem-level fault localization. The approach decouples DQM from real experimental data, ensuring cross-detector transferability and strong scalability. Preliminary validation demonstrates that the model efficiently detects and localizes representative detector faults under simulated anomaly scenarios. This constitutes the first simulation–learning co-design paradigm for generalized, automated DQM in particle physics, paving the way for robust, scalable monitoring in future large-scale experiments.
📝 Abstract
Data Quality Monitoring (DQM) is a crucial component of particle physics experiments and ensures that the recorded data is of the highest quality, and suitable for subsequent physics analysis. Due to the extreme environmental conditions, unprecedented data volumes, and the sheer scale and complexity of the detectors, DQM orchestration has become a very challenging task. Therefore, the use of Machine Learning (ML) to automate anomaly detection, improve efficiency, and reduce human error in the process of collecting high-quality data is unavoidable. Since DQM relies on real experimental data, it is inherently tied to the specific detector substructure and technology in operation. In this work, a simulation-driven approach to DQM is proposed, enabling the study and development of data-quality methodologies in a controlled environment. Using a modified version of Delphes -- a fast, multi-purpose detector simulation -- the preliminary realization of a framework is demonstrated which leverages ML to identify detector anomalies as well as localize the malfunctioning components responsible. We introduce MEDIC (Monitoring for Event Data Integrity and Consistency), a neural network designed to learn detector behavior and perform DQM tasks to look for potential faults. Although the present implementation adopts a simplified setup for computational ease, where large detector regions are deliberately deactivated to mimic faults, this work represents an initial step toward a comprehensive ML-based DQM framework. The encouraging results underline the potential of simulation-driven studies as a foundation for developing more advanced, data-driven DQM systems for future particle detectors.