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V. Rentala, W. Shepherd and T. M. P. Tait, Tagging Boosted Ws with Wavelets , JHEP 1408
Original
1929
Earlier work this paper cites.
Y. L. Dokshitzer, G. D. Leder, S. Moretti and B. R. Webber, Better Jet Clustering Algorithms , JHEP 9708
1997
Earlier work this paper cites.
J. M. Butterworth, A. R. Davison, M. Rubin and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC , Phys. Rev. Lett. 100
Original
2008
Earlier work this paper cites.
W. Skiba and D. Tucker-Smith, Using jet mass to discover vector quarks at the LHC , Phys. Rev. D 75
Original
2008
Earlier work this paper cites.
D. E. Kaplan, K. Rehermann, M. D. Schwartz and B. Tweedie, Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks , Phys. Rev. Lett. 101
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm , JHEP 0804
Original
2008
Earlier work this paper cites.
L. G. Almeida, S. J. Lee, G. Perez, I. Sung and J. Virzi, Top Jets at the LHC , Phys. Rev. D 79
Original
2009
Earlier work this paper cites.
G. Louppe, M. Kagan, K. Cranmer, Learning to Pivot with Adversarial Networks , Phys. Rev. D 80
Original
2009
Earlier work this paper cites.
T. Plehn, G. P. Salam and M. Spannowsky, Fat Jets for a Light Higgs , Phys. Rev. Lett. 104
Original
2010
Earlier work this paper cites.
T. Plehn, M. Spannowsky, M. Takeuchi, and D. Zerwas, Stop Reconstruction with Tagged Tops , JHEP 1010
Original
2010
Earlier work this paper cites.

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Then

A. J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop , JHEP 1405
Original
2014
Later among the works it cites.
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging , JHEP 1502
Original
2015
Later among the works it cites.
L. G. Almeida, M. Backovic, M. Cliche, S. J. Lee and M. Perelstein, Playing Tag with ANN: Boosted Top Identification with Pattern Recognition , JHEP 1507
Original
2015
Later among the works it cites.
see e. g. Y. LeCun, Y. Bengio, and G. Hinton, Geoffrey, Deep learning , Nature 7553
2015
Later among the works it cites.
G. Kasieczka, T. Plehn, T. Schell, T. Strebler and G. P. Salam, Resonance Searches with an Updated Top Tagger , JHEP 1506
Original

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2017

Deep-learning Top Taggers or The End of QCD?

Kasieczka, Gregor, Plehn, Tilman, Russell, Michael et al.

Understand

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.