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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
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2008
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T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1,
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M. Cacciari, G. P. Salam and G. Soyez, The Anti- k t k_{t} jet clustering algorithm,
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L. G. Almeida, S. J. Lee, G. Perez, G. F. Sterman, I. Sung and J. Virzi, Substructure of high- p T p_{T} Jets at the LHC,
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2009
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T. Plehn, G. P. Salam and M. Spannowsky, Fat Jets for a Light Higgs,
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I. Moult, L. Necib and J. Thaler, New Angles on Energy Correlation Functions,
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J. Dolen, P. Harris, S. Marzani, S. Rappoccio and N. Tran, Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
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J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, JHEP 1711
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2020

Jet tagging made easy

Aguilar-Saavedra, J. A., Zaldivar, B.

Understand

We develop taggers for multi-pronged jets that are simple functions of jet substructure (so-called `subjettiness') variables.

These taggers can be approximately decorrelated from the jet mass in a quite simple way.
Specifically, we use a Logistic Regression Design (LoRD) which, even being one of the simplest machine learning classifiers, shows a performance which surpasses that of simple variables used by the ATLAS and CMS Collaborations and is not far from more complex models based on neural networks.
Contrary to the latter, our method allows for an easy implementation of tagging tasks by providing a simple and interpretable analytical formula with already optimised parameters.