Fetching the paper…
Reading the bibliography…
We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distinguishing power of these two separate techniques applied to top quark decays.
Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition ,
J. S. Bridle, · 1990
Earlier work this paper cites.
Successive combination jet algorithm for hadron collisions ,
S. D. Ellis and D. E. Soper, · 1993
Earlier work this paper cites.
Searches for new particles using cone and cluster jet algorithms: A Comparative study ,
M. H. Seymour, · 1994
Earlier work this paper cites.
Run II jet physics ,
G. C. Blazey et al. , · 2000
Earlier work this paper cites.
The HepMC C++ Monte Carlo event record for High Energy Physics ,
M. Dobbs and J. B. Hansen, · 2001
Earlier work this paper cites.
A Standard format for Les Houches event files ,
J. Alwall et al. , · 2006
Earlier work this paper cites.
Jet substructure as a new Higgs search channel at the LHC ,
J. M. Butterworth, A. R. Davison, M. Rubin and G. P. Salam, · 2008
Earlier work this paper cites.
Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks ,
D. E. Kaplan, K. Rehermann, M. D. Schwartz and B. Tweedie, · 2008
Earlier work this paper cites.
The Anti-k(t) jet clustering algorithm ,
M. Cacciari, G. P. Salam and G. Soyez, · 2008
Earlier work this paper cites.
Scalable parallel programming with cuda ,
J. Nickolls, I. Buck, M. Garland and K. Skadron, · 2008
Earlier work this paper cites.
Fat Jets for a Light Higgs ,
T. Plehn, G. P. Salam and M. Spannowsky, · 2010
Earlier work this paper cites.
Stop Reconstruction with Tagged Tops ,
T. Plehn, M. Spannowsky, M. Takeuchi and D. Zerwas, · 2010
Earlier work this paper cites.
N-Jettiness: An Inclusive Event Shape to Veto Jets ,
I. W. Stewart, F. J. Tackmann and W. J. Waalewijn, · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines ,
V. Nair and G. E. Hinton, · 2010
Earlier work this paper cites.
Finding physics signals with shower deconstruction ,
D. E. Soper and M. Spannowsky, · 2011
Earlier work this paper cites.
Identifying Boosted Objects with N-subjettiness ,
J. Thaler and K. Van Tilburg, · 2011
Earlier work this paper cites.
MadGraph 5 : Going Beyond ,
J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer and T. Stelzer, · 2011
Earlier work this paper cites.
Maximizing Boosted Top Identification by Minimizing N-subjettiness ,
J. Thaler and K. Van Tilburg, · 2012
Cited alongside, same era.
Jet mass and substructure of inclusive jets in s = 7 \sqrt{s}=7 TeV p p pp collisions with the ATLAS experiment ,
G. Aad et al. , · 2012
Cited alongside, same era.
Parton distributions with LHC data ,
R. D. Ball, V. Bertone, S. Carrazza, C. S. Deans, L. Del Debbio et al. , · 2012
Cited alongside, same era.
FastJet User Manual ,
M. Cacciari, G. P. Salam and G. Soyez, · 2012
Cited alongside, same era.
Finding top quarks with shower deconstruction ,
D. E. Soper and M. Spannowsky, · 2013
Cited alongside, same era.
Energy Correlation Functions for Jet Substructure ,
A. J. Larkoski, G. P. Salam and J. Thaler, · 2013
Cited alongside, same era.
New Angles on Energy Correlation Functions ,
I. Moult, L. Necib and J. Thaler, · 2016
Later among the works it cites.
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks ,
P. Baldi, K. Bauer, C. Eng, P. Sadowski and D. Whiteson, · 2016
Later among the works it cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems ,
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. J. Goodfellow et al. , · 2016
Later among the works it cites.
Evidence for the H → b b ¯ H\to b\overline{b} decay with the ATLAS detector ,
M. Aaboud et al. , · 2017
Later among the works it cites.
Deep learning in color: towards automated quark/gluon jet discrimination ,
P. T. Komiske, E. M. Metodiev and M. D. Schwartz, · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rivet user manual ,
A. Buckley, J. Butterworth, L. Lonnblad, D. Grellscheid, H. Hoeth et al. , · 2013
Cited alongside, same era.
Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012 ,
A. Altheimer et al. , · 2014
Cited alongside, same era.
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations ,
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli and M. Zaro, · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting ,
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization ,
D. P. Kingma and J. Ba, · 2014
Cited alongside, same era.
Resonance Searches with an Updated Top Tagger ,
G. Kasieczka, T. Plehn, T. Schell, T. Strebler and G. P. Salam, · 2015
Cited alongside, same era.
Deep-learning Top Taggers or The End of QCD? ,
G. Kasieczka, T. Plehn, M. Russell and T. Schell, · 2017
Later among the works it cites.
Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks ,
J. Barnard, E. N. Dawe, M. J. Dolan and N. Rajcic, · 2017
Later among the works it cites.
How Much Information is in a Jet? ,
K. Datta and A. Larkoski, · 2017
Later among the works it cites.
Tech. Rep. ATL-PHYS-PUB-2017-017, CERN, Geneva (2017)
Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector , · 2017
Later among the works it cites.
New Developments for Jet Substructure Reconstruction in CMS (2017)
2017
Later among the works it cites.
A generic anti-QCD jet tagger ,
J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, · 2017
Later among the works it cites.
Deep-learned Top Tagging with a Lorentz Layer (2017),
A. Butter, G. Kasieczka, T. Plehn and M. Russell, · 2017
Later among the works it cites.
Pulling Out All the Tops with Computer Vision and Deep Learning (2018),
S. Macaluso and D. Shih, · 2018
Closest in time.
Jet Substructure at the Large Hadron Collider : Experimental Review (2018),
L. Asquith et al. , · 2018
Closest in time.
Energy flow polynomials: A complete linear basis for jet substructure ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 2018
Closest in time.
Novel Jet Observables from Machine Learning ,
K. Datta and A. J. Larkoski, · 2018
Closest in time.