[2511.18172] MEDIC: a network for monitoring data quality in collider experiments

High Energy Physics - Experiment arXiv:2511.18172 (hep-ex) [Submitted on 22 Nov 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:MEDIC: a network for monitoring data quality in collider experiments Authors:Juvenal Bassa, Arghya Chattopadhyay, Sudhir Malik, Mario Escabi Rivera View a PDF of the paper titled MEDIC: a network for monitoring data quality in collider experiments, by Juvenal Bassa and 2 other authors View PDF HTML (experimental) 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 anom...