Built on

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
Earlier work this paper cites.
J. R. Ellis, M. K. Gaillard, and D. V. Nanopoulos, A Phenomenological Profile of the Higgs Boson
1976
Earlier work this paper cites.
M. A. Shifman, A. I. Vainshtein, M. B. Voloshin, and V. I. Zakharov, Low-Energy Theorems for Higgs Boson Couplings to Photons
1979
Earlier work this paper cites.
hep-ex/9905057
SLD · 1999
Earlier work this paper cites.
M. L. Mangano, M. Moretti, F. Piccinini, and M. Treccani, Matching matrix elements and shower evolution for top-quark production in hadronic collisions
2007
Earlier work this paper cites.
J. M. Butterworth, A. R. Davison, M. Rubin, and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC
Original
2008
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Earlier work this paper cites.
M. Bahr et al., Herwig++ Physics and Manual
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The Catchment Area of Jets
Original
2008
Earlier work this paper cites.
T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter, Event generation with SHERPA 1.1
Original
2009
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure
Original
2010
Earlier work this paper cites.
I. Low, R. Rattazzi, and A. Vichi, Theoretical Constraints on the Higgs Effective Couplings
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
Earlier work this paper cites.
J. Duchi, E. Hazan, and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness
Original
2012
Earlier work this paper cites.
M. D. Zeiler, ADADELTA: an adaptive learning rate method
Original
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
Earlier work this paper cites.
M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, Towards an understanding of jet substructure
Original
2013
Earlier work this paper cites.
M. Dasgupta, A. Fregoso, S. Marzani, and A. Powling, Jet substructure with analytical methods
Original
2013
Earlier work this paper cites.
A. J. Larkoski, G. P. Salam, and J. Thaler, Energy Correlation Functions for Jet Substructure
Original
2013
Earlier work this paper cites.
C. Grojean, E. Salvioni, M. Schlaffer, and A. Weiler, Very boosted Higgs in gluon fusion
Original
2014
Earlier work this paper cites.

Similar

A. Azatov and A. Paul, Probing Higgs couplings with high p T p_{T} Higgs production
Original
2014
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M. Buschmann, C. Englert, D. Goncalves, T. Plehn, and M. Spannowsky, Resolving the Higgs-Gluon Coupling with Jets
Original
2014
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M. Schlaffer, M. Spannowsky, M. Takeuchi, A. Weiler, and C. Wymant, Boosted Higgs Shapes
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2014
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Original

Then

G. P. Salam, L. Schunk, and G. Soyez, Dichroic subjettiness ratios to distinguish colour flows in boosted boson tagging
Original
2017
Later among the works it cites.
J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
Original
2017
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination
Original
2017
Later among the works it cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
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Later among the works it cites.
G. Kasieczka, T. Plehn, M. Russell, and T. Schell, Deep-learning Top Taggers or The End of QCD?

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Reading the bibliography…

2018

Boosting $H\to b\bar b$ with Machine Learning

Lin, Joshua, Freytsis, Marat, Moult, Ian et al.

Understand

High $p_T$ Higgs production at hadron colliders provides a direct probe of the internal structure of the $gg \to H$ loop with the $H \to b\bar{b}$ decay offering the most statistics due to the large branching ratio.

Despite the overwhelming QCD background, recent advances in jet substructure have put the observation of the $gg\to H \to b\bar{b}$ channel at the LHC within the realm of possibility.
In order to enhance the sensitivity to this process, we develop a two stream convolutional neural network, with one stream acting on jet information and one using global event properties.
The neural network significantly increases the discovery potential of a Higgs signal, both for high $p_T$ Standard Model production as well for possible beyond the Standard Model contributions.

Built on

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
Earlier work this paper cites.
J. R. Ellis, M. K. Gaillard, and D. V. Nanopoulos, A Phenomenological Profile of the Higgs Boson
1976
Earlier work this paper cites.
M. A. Shifman, A. I. Vainshtein, M. B. Voloshin, and V. I. Zakharov, Low-Energy Theorems for Higgs Boson Couplings to Photons
1979
Earlier work this paper cites.
hep-ex/9905057
SLD · 1999
Earlier work this paper cites.
M. L. Mangano, M. Moretti, F. Piccinini, and M. Treccani, Matching matrix elements and shower evolution for top-quark production in hadronic collisions
2007
Earlier work this paper cites.
J. M. Butterworth, A. R. Davison, M. Rubin, and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC
Original
2008
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Earlier work this paper cites.
M. Bahr et al., Herwig++ Physics and Manual
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The Catchment Area of Jets
Original
2008
Earlier work this paper cites.
T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter, Event generation with SHERPA 1.1
Original
2009
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure
Original
2010
Earlier work this paper cites.
I. Low, R. Rattazzi, and A. Vichi, Theoretical Constraints on the Higgs Effective Couplings
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
Earlier work this paper cites.
J. Duchi, E. Hazan, and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness
Original
2012
Earlier work this paper cites.
M. D. Zeiler, ADADELTA: an adaptive learning rate method
Original
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
Earlier work this paper cites.
M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, Towards an understanding of jet substructure
Original
2013
Earlier work this paper cites.
M. Dasgupta, A. Fregoso, S. Marzani, and A. Powling, Jet substructure with analytical methods
Original
2013
Earlier work this paper cites.
A. J. Larkoski, G. P. Salam, and J. Thaler, Energy Correlation Functions for Jet Substructure
Original
2013
Earlier work this paper cites.
C. Grojean, E. Salvioni, M. Schlaffer, and A. Weiler, Very boosted Higgs in gluon fusion
Original
2014
Earlier work this paper cites.

Similar

A. Azatov and A. Paul, Probing Higgs couplings with high p T p_{T} Higgs production
Original
2014
Cited alongside, same era.
M. Buschmann, C. Englert, D. Goncalves, T. Plehn, and M. Spannowsky, Resolving the Higgs-Gluon Coupling with Jets
Original
2014
Cited alongside, same era.
M. Schlaffer, M. Spannowsky, M. Takeuchi, A. Weiler, and C. Wymant, Boosted Higgs Shapes
Original
2014
Cited alongside, same era.
A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, Soft Drop
Original
2014
Cited alongside, same era.
A. J. Larkoski, I. Moult, and D. Neill, Power Counting to Better Jet Observables
Original

Then

G. P. Salam, L. Schunk, and G. Soyez, Dichroic subjettiness ratios to distinguish colour flows in boosted boson tagging
Original
2017
Later among the works it cites.
J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
Original
2017
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination
Original
2017
Later among the works it cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
Original
2017
Later among the works it cites.
G. Kasieczka, T. Plehn, M. Russell, and T. Schell, Deep-learning Top Taggers or The End of QCD?

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2014

Cited alongside, same era.

P. Baldi, P. Sadowski, and D. Whiteson, Searching for Exotic Particles in High-Energy Physics with Deep Learning

Original

2014

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D. P. Kingma and J. Ba, Adam: A method for stochastic optimization

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2014

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J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

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2014

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Reconstruction and Modelling of Jet Pull with the ATLAS Detector

2014

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A. J. Larkoski, I. Moult, and D. Neill, Building a Better Boosted Top Tagger

Original

2015

Cited alongside, same era.

J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging

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2015

Cited alongside, same era.

L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, Playing Tag with ANN: Boosted Top Identification with Pattern Recognition

Original

2015

Cited alongside, same era.

Original

2017