Playing tag with ANN: boosted top identification with pattern recognition

Almeida, Leandro (Institut de Biologie de l’ École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197, 46 rue d’Ulm, 75005, Paris, France) ; Backović, Mihailo (Center for Cosmology, Particle Physics and Phenomenology - CP3, Universite Catholique de Louvain, Louvain-la-neuve, Belgium) ; Cliche, Mathieu (Laboratory for Elementary Particle Physics, Cornell University, Ithaca, NY, 14853, United States) ; Lee, Seung (Department of Physics, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, South Korea) (School of Physics, Korea Institute for Advanced Study, Seoul, 130-722, South Korea) ; Perelstein, Maxim (Laboratory for Elementary Particle Physics, Cornell University, Ithaca, NY, 14853, United States)

23 July 2015

Abstract: 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 p T 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.


Published in: JHEP 1507 (2015) 086
Published by: Springer/SISSA
DOI: 10.1007/JHEP07(2015)086
arXiv: 1501.05968
License: CC-BY-4.0



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