Playing tag with ANN: Boosted top identification with pattern recognition

Leandro G. Almeida, Mihailo Backović, Mathieu Cliche, Seung J. Lee, Maxim Perelstein

Research output: Contribution to journalArticlepeer-review

119 Citations (Scopus)


Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image” of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with pT in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Original languageEnglish
Article number86
JournalJournal of High Energy Physics
Issue number7
Publication statusPublished - 2015 Jul 27

Bibliographical note

Publisher Copyright:
© 2015, The Author(s).


  • Jets

ASJC Scopus subject areas

  • Nuclear and High Energy Physics


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