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Deep-learning top taggers or the end of QCD?

  • Gregor Kasieczka
  • Tilman Plehn
  • Michael Russell
  • Torben Schell
Open Access
Regular Article - Experimental Physics

Abstract

Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.

Keywords

Jet substructure QCD Hadron-Hadron scattering (experiments) Top physics 

Notes

Open Access

This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Gregor Kasieczka
    • 1
  • Tilman Plehn
    • 2
  • Michael Russell
    • 3
  • Torben Schell
    • 2
  1. 1.Institute for Particle PhysicsETH ZürichZürichSwitzerland
  2. 2.Institut für Theoretische PhysikUniversität HeidelbergHeidelbergGermany
  3. 3.School of Physics and AstronomyUniversity of GlasgowGlasgowUnited Kingdom

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