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2018

Infrared Safety of a Neural-Net Top Tagging Algorithm

Choi, Suyong, Lee, Seung J., Perelstein, Maxim

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Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging.

To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies.
In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state.
We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collinear with one of the quarks.

Infrared Safety of a Neural-Net Top Tagging Algorithm · Around

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L. G. Almeida, O. Erdogan, J. Juknevich, S. J. Lee, G. Perez and G. Sterman, Three-particle templates for a boosted Higgs boson , Phys. Rev. D85
Original
1957
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1 , Comput. Phys. Commun. 178
Original
2008
Earlier work this paper cites.
X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks , 2010
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness , JHEP 03
Original
2011
Earlier work this paper cites.
J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer and T. Stelzer, MadGraph 5 : Going Beyond , JHEP 06
Original
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness , JHEP 02
Original
2012
Earlier work this paper cites.
A. J. Larkoski and J. Thaler, Unsafe but Calculable: Ratios of Angularities in Perturbative QCD , JHEP 09
Original
2013
Earlier work this paper cites.

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