MEDIC: a network for monitoring data quality in collider experiments

Researchers have developed MEDIC, a neural network framework for Data Quality Monitoring (DQM) in particle physics experiments that uses machine learning to automatically detect detector anomalies and identify malfunctioning components. The simulation-driven approach using modified Delphes detector simulation represents an initial step toward comprehensive ML-based DQM systems for future particle detectors.

• →MEDIC is a neural network designed to monitor data quality and detect faults in particle physics detector systems.
• →The framework uses a simulation-driven approach with modified Delphes detector simulation for controlled testing environments.
• →Machine learning automation aims to reduce human error and improve efficiency in data quality monitoring processes.
• →The current implementation uses simplified setups where detector regions are deliberately deactivated to simulate faults.
• →Results show potential for developing more advanced data-driven DQM systems for future particle detection experiments.