Journal
JOURNAL OF HIGH ENERGY PHYSICS
Volume -, Issue 5, Pages -Publisher
SPRINGER
DOI: 10.1007/JHEP05(2017)006
Keywords
Jet substructure; QCD; Hadron-Hadron scattering (experiments); Top physics
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Funding
- International Max Planck Research School for Precision Test of Fundamental Symmetries
- DFG Research Training Group Particle Physics Beyond the Standard Model
- European Union Marie Curie Research Training Network MCnetITN [PITN-GA-2012-315877]
- Swiss National Supercomputing Centre (CSCS) [D61]
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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.
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