Zusammenfassung
We introduce a new and highly efficient tagger for hadronically decaying top
quarks, based on a deep neural network working with Lorentz vectors and the
Minkowski metric. With its novel machine learning setup and architecture it
allows us to identify boosted top quarks not only from calorimeter towers, but
also including tracking information. We show how the performance of our tagger
compares with QCD-inspired and image-recognition approaches and find that it
significantly increases the performance for strongly boosted top quarks.
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