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

The project aims at applying recent progress in Machine Learning techniques to the automation of quality assessment allowing the check of large volumes of data in real-time and improving the ability to detect unexpected features. The test implementation was presented to a panel of Physicist and Computer scientists; I was awarded the 2nd position for my work.

I was selected as a summer research student under the Openlab Program to collaborate with Dr. Gianluca Cerminara and Adrian Alan Pol of the Compact Muon Solenoid group at CERN

The project aims at applying recent progress in Machine Learning techniques to the automation of quality assessment allowing the check of large volumes of data in real-time and improving the ability to detect unexpected features. A test implementation using an unsupervised machine learning model (autoencoder) focused on the data of one of the CMS muon detectors (drift tubes) has been developed and bench-marked on real and fake data.

• Openlab Presentation of the project
• Report