Built on

Physics of the HL-LHC Working Group
Original
1902
Earlier work this paper cites.
B. Bhattacherjee, S. Mukherjee and R. Sengupta, Discrimination between prompt and long-lived particles using convolutional neural network , 1904.04811
Original
1904
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting , J. Mach. Learn. Res. 15
1958
Earlier work this paper cites.
E. W. N. Glover and J. J. van der Bij, HIGGS BOSON PAIR PRODUCTION VIA GLUON FUSION , Nucl. Phys. B309
1988
Earlier work this paper cites.
R. D. Field, Y. Kanev, M. Tayebnejad and P. A. Griffin, Using neural networks to enhance the Higgs boson signal at hadron colliders , Phys. Rev. D53
1996
Earlier work this paper cites.
R. D. Field, Y. Kanev and M. Tayebnejad, A Topological analysis of the top quark signal and background at hadron colliders , Phys. Rev. D55
1997
Earlier work this paper cites.
R. Field, “Genetic algorithms and neural networks as tools in particle physics.” Tevatron University, Fermilab, May 21 1998
1998
Earlier work this paper cites.
C. G. Lester and D. J. Summers, Measuring masses of semiinvisibly decaying particles pair produced at hadron colliders , Phys. Lett. B463
1999
Earlier work this paper cites.
T. G. Dietterich, Ensemble methods in machine learning , in Multiple Classifier Systems , (Berlin, Heidelberg), pp. 1–15, Springer Berlin Heidelberg, 2000
2000
Earlier work this paper cites.
U. Baur, T. Plehn and D. L. Rainwater, Examining the Higgs boson potential at lepton and hadron colliders: A Comparative analysis , Phys. Rev. D68
2003
Earlier work this paper cites.
F. Maltoni, K. Paul, T. Stelzer and S. Willenbrock, Color Flow Decomposition of QCD Amplitudes , Phys. Rev. D67
2003
Earlier work this paper cites.
U. Baur, T. Plehn and D. L. Rainwater, Probing the Higgs selfcoupling at hadron colliders using rare decays , Phys. Rev. D69
2004
Earlier work this paper cites.
A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne and H. Voss, TMVA: Toolkit for Multivariate Data Analysis , PoS ACAT
2007
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
P. Konar, K. Kong and K. T. Matchev, s ^ m i n \sqrt{\hat{s}}_{min} : A Global inclusive variable for determining the mass scale of new physics in events with missing energy at hadron colliders , JHEP 03
Original
2009
Earlier work this paper cites.
M. Burns, K. Kong, K. T. Matchev and M. Park, Using Subsystem M T 2 M_{T2} for Complete Mass Determinations in Decay Chains with Missing Energy at Hadron Colliders , JHEP 03
Original
2009
Earlier work this paper cites.
CMS Collaboration
2009
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure , Phys. Rev. Lett. 105
Original
2010
Earlier work this paper cites.
T. Han, I.-W. Kim and J. Song, Kinematic Cusps: Determining the Missing Particle Mass at Colliders , Phys. Lett. B693
Original
2010
Earlier work this paper cites.
P. Konar, K. Kong, K. T. Matchev and M. Park, Superpartner Mass Measurement Technique using 1D Orthogonal Decompositions of the Cambridge Transverse Mass Variable M T 2 M_{T2} , Phys. Rev. Lett. 105
Original
2010
Earlier work this paper cites.
P. Konar, K. Kong, K. T. Matchev and M. Park, Dark Matter Particle Spectroscopy at the LHC: Generalizing M T 2 M_{T2} to Asymmetric Event Topologies , JHEP 04
Original
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks , Journal of Machine Learning Research - Proceedings Track 9
2010
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J. Gallicchio, J. Huth, M. Kagan, M. D. Schwartz, K. Black and B. Tweedie, Multivariate discrimination and the Higgs + W/Z search , JHEP 04
Original
2011
Earlier work this paper cites.
A. J. Barr, T. J. Khoo, P. Konar, K. Kong, C. G. Lester, K. T. Matchev et al., Guide to transverse projections and mass-constraining variables , Phys. Rev. D84
Original
2011
Earlier work this paper cites.
P. Baringer, K. Kong, M. McCaskey and D. Noonan, Revisiting Combinatorial Ambiguities at Hadron Colliders with M T 2 M_{T2} , JHEP 10
Original
2011
Earlier work this paper cites.
X. Glorot, A. Bordes and Y. Bengio, Deep sparse rectifier neural networks , Proceedings of the 14th International Conference on Artificial Intelligence and Statisitics (AISTATS) 2011 15
2011
Earlier work this paper cites.
J. Duchi, E. Hazan and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization , J. Mach. Learn. Res. 12
2011
Earlier work this paper cites.
M. J. Dolan, C. Englert and M. Spannowsky, Higgs self-coupling measurements at the LHC , JHEP 10
Original
2012
Earlier work this paper cites.

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Original

Then

D. Kim and K. Kong, Kinematic discrimination of tW and t t ¯ t\overline{t} productions using initial state radiation , Phys. Lett. B751
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Later among the works it cites.
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Original
2016
Later among the works it cites.
J. K. Behr, D. Bortoletto, J. A. Frost, N. P. Hartland, C. Issever and J. Rojo, Boosting Higgs pair production in the b b ¯ b b ¯ b\bar{b}b\bar{b} final state with multivariate techniques , Eur. Phys. J. C76
Original

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2019

Portraying Double Higgs at the Large Hadron Collider

Kim, Jeong Han, Kim, Minho, Kong, Kyoungchul et al.

Understand

We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two $b$-tagged jets, two leptons and missing transverse momentum.

Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain sizable signal significance, by adopting a deep learning framework making full use of the relevant kinematics along with the jet images from the Higgs decay.
For the relevant number of signal events we obtain a substantial increase in signal sensitivity over existing analyses.
We discuss relative improvements at each stage and the correlations among the different input variables for the neutral network.

Built on

Physics of the HL-LHC Working Group
Original
1902
Earlier work this paper cites.
B. Bhattacherjee, S. Mukherjee and R. Sengupta, Discrimination between prompt and long-lived particles using convolutional neural network , 1904.04811
Original
1904
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting , J. Mach. Learn. Res. 15
1958
Earlier work this paper cites.
E. W. N. Glover and J. J. van der Bij, HIGGS BOSON PAIR PRODUCTION VIA GLUON FUSION , Nucl. Phys. B309
1988
Earlier work this paper cites.
R. D. Field, Y. Kanev, M. Tayebnejad and P. A. Griffin, Using neural networks to enhance the Higgs boson signal at hadron colliders , Phys. Rev. D53
1996
Earlier work this paper cites.
R. D. Field, Y. Kanev and M. Tayebnejad, A Topological analysis of the top quark signal and background at hadron colliders , Phys. Rev. D55
1997
Earlier work this paper cites.
R. Field, “Genetic algorithms and neural networks as tools in particle physics.” Tevatron University, Fermilab, May 21 1998
1998
Earlier work this paper cites.
C. G. Lester and D. J. Summers, Measuring masses of semiinvisibly decaying particles pair produced at hadron colliders , Phys. Lett. B463
1999
Earlier work this paper cites.
T. G. Dietterich, Ensemble methods in machine learning , in Multiple Classifier Systems , (Berlin, Heidelberg), pp. 1–15, Springer Berlin Heidelberg, 2000
2000
Earlier work this paper cites.
U. Baur, T. Plehn and D. L. Rainwater, Examining the Higgs boson potential at lepton and hadron colliders: A Comparative analysis , Phys. Rev. D68
2003
Earlier work this paper cites.
F. Maltoni, K. Paul, T. Stelzer and S. Willenbrock, Color Flow Decomposition of QCD Amplitudes , Phys. Rev. D67
2003
Earlier work this paper cites.
U. Baur, T. Plehn and D. L. Rainwater, Probing the Higgs selfcoupling at hadron colliders using rare decays , Phys. Rev. D69
2004
Earlier work this paper cites.
A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne and H. Voss, TMVA: Toolkit for Multivariate Data Analysis , PoS ACAT
2007
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
P. Konar, K. Kong and K. T. Matchev, s ^ m i n \sqrt{\hat{s}}_{min} : A Global inclusive variable for determining the mass scale of new physics in events with missing energy at hadron colliders , JHEP 03
Original
2009
Earlier work this paper cites.
M. Burns, K. Kong, K. T. Matchev and M. Park, Using Subsystem M T 2 M_{T2} for Complete Mass Determinations in Decay Chains with Missing Energy at Hadron Colliders , JHEP 03
Original
2009
Earlier work this paper cites.
CMS Collaboration
2009
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure , Phys. Rev. Lett. 105
Original
2010
Earlier work this paper cites.
T. Han, I.-W. Kim and J. Song, Kinematic Cusps: Determining the Missing Particle Mass at Colliders , Phys. Lett. B693
Original
2010
Earlier work this paper cites.
P. Konar, K. Kong, K. T. Matchev and M. Park, Superpartner Mass Measurement Technique using 1D Orthogonal Decompositions of the Cambridge Transverse Mass Variable M T 2 M_{T2} , Phys. Rev. Lett. 105
Original
2010
Earlier work this paper cites.
P. Konar, K. Kong, K. T. Matchev and M. Park, Dark Matter Particle Spectroscopy at the LHC: Generalizing M T 2 M_{T2} to Asymmetric Event Topologies , JHEP 04
Original
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks , Journal of Machine Learning Research - Proceedings Track 9
2010
Earlier work this paper cites.
J. Gallicchio, J. Huth, M. Kagan, M. D. Schwartz, K. Black and B. Tweedie, Multivariate discrimination and the Higgs + W/Z search , JHEP 04
Original
2011
Earlier work this paper cites.
A. J. Barr, T. J. Khoo, P. Konar, K. Kong, C. G. Lester, K. T. Matchev et al., Guide to transverse projections and mass-constraining variables , Phys. Rev. D84
Original
2011
Earlier work this paper cites.
P. Baringer, K. Kong, M. McCaskey and D. Noonan, Revisiting Combinatorial Ambiguities at Hadron Colliders with M T 2 M_{T2} , JHEP 10
Original
2011
Earlier work this paper cites.
X. Glorot, A. Bordes and Y. Bengio, Deep sparse rectifier neural networks , Proceedings of the 14th International Conference on Artificial Intelligence and Statisitics (AISTATS) 2011 15
2011
Earlier work this paper cites.
J. Duchi, E. Hazan and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization , J. Mach. Learn. Res. 12
2011
Earlier work this paper cites.
M. J. Dolan, C. Englert and M. Spannowsky, Higgs self-coupling measurements at the LHC , JHEP 10
Original
2012
Earlier work this paper cites.

Similar

A. Hook, M. Jankowiak and J. G. Wacker, Jet Dipolarity: Top Tagging with Color Flow , JHEP 04
Original
2012
Cited alongside, same era.
M. Cacciari, G. P. Salam and G. Soyez, FastJet User Manual , Eur. Phys. J. C72
Original
2012
Cited alongside, same era.
Curran Associates, Inc., 2012
A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks , in Advances in Neural Information Processing Systems 25 (F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, eds.), pp. 1097–1105 · 2012
Cited alongside, same era.
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2012
Cited alongside, same era.
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Original

Then

D. Kim and K. Kong, Kinematic discrimination of tW and t t ¯ t\overline{t} productions using initial state radiation , Phys. Lett. B751
Original
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Later among the works it cites.
Y. Lecun, Y. Bengio and G. Hinton, Deep learning , Nature 521
2015
Later among the works it cites.
R. Ge, F. Huang, C. Jin and Y. Yuan, Escaping from saddle points - online stochastic gradient for tensor decomposition , CoRR abs/1503.02101
Original
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Later among the works it cites.
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Later among the works it cites.
J. K. Behr, D. Bortoletto, J. A. Frost, N. P. Hartland, C. Issever and J. Rojo, Boosting Higgs pair production in the b b ¯ b b ¯ b\bar{b}b\bar{b} final state with multivariate techniques , Eur. Phys. J. C76
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Beyond the bibliography

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Open on alphaXiv

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2012

Cited alongside, same era.

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2014

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2016