Anomaly Detection using Machine Learning for Data Quality Monitoring in the CMS Experiment


This talk is about our project that aimed at applying recent progress in Machine Learning techniques (unsupervised machine learning method - autoencoder) to the automation of quality assessment. In our project, we concentrated on analyzing the occupancy of the drift tube chambers. The aim was to check large volumes of data in real-time and improve the ability to detect unexpected features.