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