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MCNET-17-07
arXiv:1701.08784
JHEP 1705 (2017) 006

by: Kasieczka, Gregor (Zurich, ETH) et al.

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.
Link: 
http://inspirehep.net/record/1511436
Publ date: 
Wednesday, February 1, 2017 - 04:57