Jet classification
This project Apply deep neural networks common in computer vision applications to distinguish different sources of jets using “jet images”
Physics with jets essential for the success of the LHC physics program. Jet clustering combines calorimeter deposits or tracks in an attempt to relate observations with theoretical predictions
Large effort in both Experiment and Theory communities to improve/extend jet tools
Major role in these developments: Advanced ML techniques Started with jet flavour tagging, showing impressive improvement in performance
ML-based application extend to other important jet physics tasks such as jet calibration, improvement of jet energy and mass reconstruction
These efforts pays off yielding substantial improvements in physics analyses
