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.
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References
G. Perez, Top quark theory and the new physics searches frontier, Phys. Scripta T158 (2013) 014008 [INSPIRE].
K. Agashe, A. Belyaev, T. Krupovnickas, G. Perez and J. Virzi, LHC Signals from Warped Extra Dimensions, Phys. Rev. D 77 (2008) 015003 [hep-ph/0612015] [INSPIRE].
B. Lillie, L. Randall and L.-T. Wang, The Bulk RS KK-gluon at the LHC, JHEP 09 (2007) 074 [hep-ph/0701166] [INSPIRE].
M. Perelstein and A. Spray, Four boosted tops from a Regge gluon, JHEP 09 (2011) 008 [arXiv:1106.2171] [INSPIRE].
T. Plehn, M. Spannowsky, M. Takeuchi and D. Zerwas, Stop Reconstruction with Tagged Tops, JHEP 10 (2010) 078 [arXiv:1006.2833] [INSPIRE].
J. Berger, M. Perelstein, M. Saelim and A. Spray, Boosted Tops from Gluino Decays, arXiv:1111.6594 [INSPIRE].
A. Azatov, M. Salvarezza, M. Son and M. Spannowsky, Boosting Top Partner Searches in Composite Higgs Models, Phys. Rev. D 89 (2014) 075001 [arXiv:1308.6601] [INSPIRE].
T. Flacke, J.H. Kim, S.J. Lee and S.H. Lim, Constraints on composite quark partners from Higgs searches, JHEP 05 (2014) 123 [arXiv:1312.5316] [INSPIRE].
M. Backović, G. Perez, T. Flacke and S.J. Lee, LHC Top Partner Searches Beyond the 2 TeV Mass Region, arXiv:1409.0409 [INSPIRE].
M. Backović, T. Flacke, J.H. Kim and S.J. Lee, Boosted Event Topologies from TeV Scale Light Quark Composite Partners, JHEP 04 (2015) 082 [arXiv:1410.8131] [INSPIRE].
B. Gripaios, T. Müller, M.A. Parker and D. Sutherland, Search Strategies for Top Partners in Composite Higgs models, JHEP 08 (2014) 171 [arXiv:1406.5957] [INSPIRE].
J. Reuter and M. Tonini, Top Partner Discovery in the T → tZ channel at the LHC, JHEP 01 (2015) 088 [arXiv:1409.6962] [INSPIRE].
T. Plehn and M. Spannowsky, Top Tagging, J. Phys. G 39 (2012) 083001 [arXiv:1112.4441] [INSPIRE].
A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks, J. Phys. G 39 (2012) 063001 [arXiv:1201.0008] [INSPIRE].
M. Jankowiak and A.J. Larkoski, Jet Substructure Without Trees, JHEP 06 (2011) 057 [arXiv:1104.1646] [INSPIRE].
J.D. Bjorken and S.J. Brodsky, Statistical Model for electron-Positron Annihilation Into Hadrons, Phys. Rev. D 1 (1970) 1416 [INSPIRE].
J. Thaler and L.-T. Wang, Strategies to Identify Boosted Tops, JHEP 07 (2008) 092 [arXiv:0806.0023] [INSPIRE].
L.G. Almeida, S.J. Lee, G. Perez, G.F. Sterman, I. Sung and J. Virzi, Substructure of high-p T Jets at the LHC, Phys. Rev. D 79 (2009) 074017 [arXiv:0807.0234] [INSPIRE].
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 (2008) 242001 [arXiv:0802.2470] [INSPIRE].
D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE].
S.D. Ellis, C.K. Vermilion and J.R. Walsh, Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches, Phys. Rev. D 81 (2010) 094023 [arXiv:0912.0033] [INSPIRE].
T. Plehn, G.P. Salam and M. Spannowsky, Fat Jets for a Light Higgs, Phys. Rev. Lett. 104 (2010) 111801 [arXiv:0910.5472] [INSPIRE].
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 (2008) 142001 [arXiv:0806.0848] [INSPIRE].
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness, JHEP 03 (2011) 015 [arXiv:1011.2268] [INSPIRE].
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness, JHEP 02 (2012) 093 [arXiv:1108.2701] [INSPIRE].
L.G. Almeida, O. Erdogan, J. Juknevich, S.J. Lee, G. Perez and G. Sterman, Three-particle templates for a boosted Higgs boson, Phys. Rev. D 85 (2012) 114046 [arXiv:1112.1957] [INSPIRE].
L.G. Almeida, S.J. Lee, G. Perez, G. Sterman and I. Sung, Template Overlap Method for Massive Jets, Phys. Rev. D 82 (2010) 054034 [arXiv:1006.2035] [INSPIRE].
M. Backovic, J. Juknevich and G. Perez, Boosting the Standard Model Higgs Signal with the Template Overlap Method, JHEP 07 (2013) 114 [arXiv:1212.2977] [INSPIRE].
M. Backovic, O. Gabizon, J. Juknevich, G. Perez and Y. Soreq, Measuring boosted tops in semi-leptonic \( t\overline{t} \) events for the standard model and beyond, JHEP 04 (2014) 176 [arXiv:1311.2962] [INSPIRE].
D0 collaboration, V.M. Abazov et al., A precision measurement of the mass of the top quark, Nature 429 (2004) 638 [hep-ex/0406031] [INSPIRE].
P. Artoisenet, V. Lemaitre, F. Maltoni and O. Mattelaer, Automation of the matrix element reweighting method, JHEP 12 (2010) 068 [arXiv:1007.3300] [INSPIRE].
D.E. Soper and M. Spannowsky, Finding physics signals with shower deconstruction, Phys. Rev. D 84 (2011) 074002 [arXiv:1102.3480] [INSPIRE].
D.E. Soper and M. Spannowsky, Finding top quarks with shower deconstruction, Phys. Rev. D 87 (2013) 054012 [arXiv:1211.3140] [INSPIRE].
A.J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop, JHEP 05 (2014) 146 [arXiv:1402.2657] [INSPIRE].
ATLAS collaboration, A search for \( t\overline{t} \) resonances in lepton+jets events with highly boosted top quarks collected in pp collisions at \( \sqrt{s}=7 \) TeV with the ATLAS detector, JHEP 09 (2012) 041 [arXiv:1207.2409] [INSPIRE].
ATLAS collaboration, Search for resonances decaying into top-quark pairs using fully hadronic decays in pp collisions with ATLAS at \( \sqrt{s}=7 \) TeV, JHEP 01 (2013) 116 [arXiv:1211.2202] [INSPIRE].
CMS collaboration, A Cambridge-Aachen (C-A) based Jet Algorithm for boosted top-jet tagging, CMS-PAS-JME-09-001 (2009).
CMS collaboration, Jet Substructure Algorithms, CMS-PAS-JME-10-013 (2011).
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118 [arXiv:1407.5675] [INSPIRE].
I.M. Dremin, G.K. Eyyubova, V.L. Korotkikh and L.I. Sarycheva, Two-dimensional discrete wavelet analysis of multiparticle event topology in heavy ion collisions, Indian J. Phys. 85 (2011) 39 [arXiv:0711.1657] [INSPIRE].
I. Volobouev, FFTJet: A Package for Multiresolution Particle Jet Reconstruction in the Fourier Domain, arXiv:0907.0270 [INSPIRE].
V. Rentala, W. Shepherd and T.M.P. Tait, Tagging Boosted Ws with Wavelets, JHEP 08 (2014) 042 [arXiv:1404.1929] [INSPIRE].
J.W. Monk, Wavelet Analysis: Event De-noising, Shower Evolution and Jet Substructure Without Jets, arXiv:1405.5008 [INSPIRE].
F. Maltoni and T. Stelzer, MadEvent: Automatic event generation with MadGraph, JHEP 02 (2003) 027 [hep-ph/0208156] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, The anti-k t jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
C.M. Bishop, Neural Networks for Pattern Recognition, fist edition, Oxford University Press, Oxford U.K. (1996).
P. Werbos, Beyond regression: new tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, U.S.A. (1975).
S. Marsland, Machine Learning: An Algorithmic Perspective, first edition, Chapman & Hall/CRC, (2009).
Y. Freund and R.E. Schapire, A short introduction to boosting, in In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, (1999), pp. 1401-1406.
A.J. Larkoski and J. Thaler, Unsafe but Calculable: Ratios of Angularities in Perturbative QCD, JHEP 09 (2013) 137 [arXiv:1307.1699] [INSPIRE].
A. Hook, E. Izaguirre, M. Lisanti and J.G. Wacker, High Multiplicity Searches at the LHC Using Jet Masses, Phys. Rev. D 85 (2012) 055029 [arXiv:1202.0558] [INSPIRE].
T. Cohen, E. Izaguirre, M. Lisanti and H.K. Lou, Jet Substructure by Accident, JHEP 03 (2013) 161 [arXiv:1212.1456] [INSPIRE].
M. Backović and J. Juknevich, TemplateTagger v1.0.0: A Template Matching Tool for Jet Substructure, Comput. Phys. Commun. 185 (2014) 1322 [arXiv:1212.2978] [INSPIRE].
ATLAS collaboration, Search for \( t\overline{t} \) resonances in the lepton plus jets final state with ATLAS using 4.7 fb−1 of pp collisions at \( \sqrt{s}=7 \) TeV, Phys. Rev. D 88 (2013) 012004 [arXiv:1305.2756] [INSPIRE].
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Almeida, L.G., Backović, M., Cliche, M. et al. Playing tag with ANN: boosted top identification with pattern recognition. J. High Energ. Phys. 2015, 86 (2015). https://doi.org/10.1007/JHEP07(2015)086
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DOI: https://doi.org/10.1007/JHEP07(2015)086