Speaker
Qu Huilin
(UCSB)
Description
dentification of boosted top quarks from their hadronic decays can play an important role in searches for new physics at the LHC. We present DeepBoostedJet, a new approach for boosted jet identification using particle-flow jets at CMS. One dimensional convolutional neural networks are utilized to classify a jet directly from its reconstructed constituent particles. The new method shows significant improvement in performance compared to alternative multivariate methods using jet-level observables.
Primary authors
Dr
Markus Stoye
(CERN)
Qu Huilin
(UCSB)
