Deep-learned top tagging with a Lorentz layer
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 towe...
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| Main Authors: | , , , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
27 Jul 2017
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| In: |
Arxiv
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| Online Access: | Verlag, kostenfrei, Volltext: http://arxiv.org/abs/1707.08966
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| Author Notes: | Anja Butter, Gregor Kasieczka, Tilman Plehn, and Michael Russell |
| Summary: | 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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| Item Description: | Identifizierung der Ressource nach: Last revised 23 Apr 2018 Gesehen am 01.12.2020 |
| Physical Description: | Online Resource |