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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
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2008
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2009
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2010
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J. Gallicchio and M. D. Schwartz, “Quark and Gluon Tagging at the LHC,” Phys. Rev. Lett
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2011
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J. Gallicchio, J. Huth, M. Kagan, M. D. Schwartz, K. Black, and B. Tweedie, “Multivariate discrimination and the Higgs + W/Z search,” JHEP
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2011
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J. Thaler and K. Van Tilburg, “Maximizing Boosted Top Identification by Minimizing N-subjettiness,” JHEP
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Similar

A. Altheimer et al
Original
2012
Cited alongside, same era.
W. J. Waalewijn, “Calculating the Charge of a Jet,” Phys. Rev
Original
2012
Cited alongside, same era.
D. Krohn, M. D. Schwartz, T. Lin, and W. J. Waalewijn, “Jet Charge at the LHC,” Phys. Rev. Lett
Original
2013
Cited alongside, same era.
J. Gallicchio and M. D. Schwartz, “Quark and Gluon Jet Substructure,” JHEP
Original
2013
Cited alongside, same era.
J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, “Jet-Images: Computer Vision Inspired Techniques for Jet Tagging,” JHEP
Original
2015

Then

D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, “Jet Flavor Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev
Original
2016
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, “Deep learning in color: towards automated quark/gluon jet discrimination,” JHEP
Original
2017
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, B. Nachman, and M. D. Schwartz, “Pileup Mitigation with Machine Learning (PUMML),” JHEP
Original
2017
Later among the works it cites.
E. M. Metodiev, B. Nachman, and J. Thaler, “Classification without labels: Learning from mixed samples in high energy physics,” JHEP
Original
2017
Later among the works it cites.
G. Kasieczka, T. Plehn, M. Russell, and T. Schell, “Deep-learning Top Taggers or The End of QCD?,” JHEP

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2018

Jet Charge and Machine Learning

Fraser, Katherine, Schwartz, Matthew D.

Understand

Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider.

For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination.
We explore these methods for the purpose of classifying jets according to their electric charge.
We find that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods.