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Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure.
A Parametrization of the Properties of Quark Jets
R. D. Field and R. P. Feynman · 1978
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Towards a standard jet definition
D. Yu. Grigoriev, E. Jankowski, and F. V. Tkachov · 2003
Earlier work this paper cites.
Optimal jet finder
D. Yu. Grigoriev, E. Jankowski, and F. V. Tkachov · 2003
Earlier work this paper cites.
Deterministic annealing as a jet clustering algorithm in hadronic collisions
L. Angelini, G. Nardulli, L. Nitti, M. Pellicoro, D. Perrino, and S. Stramaglia · 2004
Earlier work this paper cites.
The Anti-k(t) jet clustering algorithm
Matteo Cacciari, Gavin P. Salam, and Gregory Soyez · 2008
Earlier work this paper cites.
FFTJet: A Package for Multiresolution Particle Jet Reconstruction in the Fourier Domain
I. Volobouev · 2009
Earlier work this paper cites.
Towards Jetography
Gavin P. Salam · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Multivariate discrimination and the Higgs + W/Z search
Jason Gallicchio, John Huth, Michael Kagan, Matthew D. Schwartz, Kevin Black, and Brock Tweedie · 2011
Earlier work this paper cites.
FastJet User Manual
Matteo Cacciari, Gavin P. Salam, and Gregory Soyez · 2012
Earlier work this paper cites.
Jet Charge at the LHC
David Krohn, Matthew D. Schwartz, Tongyan Lin, and Wouter J. Waalewijn · 2013
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DELPHES 3, A modular framework for fast simulation of a generic collider experiment
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Towards an Understanding of the Correlations in Jet Substructure
D. Adams et al · 2015
Cited alongside, same era.
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
Josh Cogan, Michael Kagan, Emanuel Strauss, and Ariel Schwarztman · 2015
Cited alongside, same era.
Playing Tag with ANN: Boosted Top Identification with Pattern Recognition
Leandro G. Almeida, Mihailo Backović, Mathieu Cliche, Seung J. Lee, and Maxim Perelstein · 2015
Cited alongside, same era.
An Introduction to PYTHIA 8.2
Torbjörn Sjöstrand, Stefan Ask, Jesper R. Christiansen, Richard Corke, Nishita Desai, Philip Ilten, Stephen Mrenna, Stefan Prestel, Christine O. Rasmussen, and Peter Z. Skands · 2015
Cited alongside, same era.
Jet Constituents for Deep Neural Network Based Top Quark Tagging
Jannicke Pearkes, Wojciech Fedorko, Alison Lister, and Colin Gay · 2017
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Deep-learning Top Taggers or The End of QCD?
Gregor Kasieczka, Tilman Plehn, Michael Russell, and Torben Schell · 2017
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Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
James Barnard, Edmund Noel Dawe, Matthew J. Dolan, and Nina Rajcic · 2017
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Deep learning in color: towards automated quark/gluon jet discrimination
Patrick T. Komiske, Eric M. Metodiev, and Matthew D. Schwartz · 2017
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Heavy flavor identification at CMS with deep neural networks
CMS Collaboration · 2017
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QCD-Aware Recursive Neural Networks for Jet Physics
Gilles Louppe, Kyunghyun Cho, Cyril Becot, and Kyle Cranmer · 2017
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Pierre Baldi, Kevin Bauer, Clara Eng, Peter Sadowski, and Daniel Whiteson · 2016
Cited alongside, same era.
Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
Daniel Guest, Julian Collado, Pierre Baldi, Shih-Chieh Hsu, Gregor Urban, and Daniel Whiteson · 2016
Cited alongside, same era.
Jet-images — deep learning edition
Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, and Ariel Schwartzman · 2016
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Measurement of the charged-particle multiplicity inside jets from s = 8 \sqrt{s}=8 TeV p p pp collisions with the ATLAS detector
Georges Aad et al · 2016
Cited alongside, same era.
Measurement of jet charge in dijet events from s \sqrt{s} =8 TeV pp collisions with the ATLAS detector
Georges Aad et al · 2016
Cited alongside, same era.
Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
Andrew J. Larkoski, Ian Moult, and Benjamin Nachman · 2017
Cited alongside, same era.
Closest in time.
Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector
ATLAS Collaboration · 2017
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New Developments for Jet Substructure Reconstruction in CMS
CMS Collaboration · 2017
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Systematics of quark/gluon tagging
Philippe Gras, Stefan Höche, Deepak Kar, Andrew Larkoski, Leif Lönnblad, Simon Plätzer, Andrzej Siódmok, Peter Skands, Gregory Soyez, and Jesse Thaler · 2017
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How Much Information is in a Jet?
Kaustuv Datta and Andrew Larkoski · 2017
Closest in time.
Particle-flow reconstruction and global event description with the CMS detector
Albert M Sirunyan et al · 2017
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Measurements of jet charge with dijet events in pp collisions at s = 8 \sqrt{s}=8 TeV
Albert M Sirunyan et al · 2017
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