Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring

Physics experiments is a crucial and demanding task to deliver high-quality data used for physics analysis. At the Compact Muon Solenoid experiment operating at the CERN Large Hadron Collider, the current quality assessment paradigm, is based on the scrutiny of a large number of statistical tests. However, the ever increasing detector complexity and the volume of monitoring data call for a growing paradigm shift. Here, Machine Learning techniques promise a breakthrough. This dissertation deals with the problem of automating Data Quality Monitoring scrutiny with Machine Learning Anomaly.