Built on

J. Ren, L. Wu, and J. M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
Original
1901
Earlier work this paper cites.
A. Butter et al., The Machine Learning Landscape of Top Taggers
Original
1902
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds
Original
1902
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S. Carrazza and F. A. Dreyer, Jet grooming through reinforcement learning
Original
1903
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A. Chakraborty, S. H. Lim, and M. M. Nojiri, Interpretable deep learning for two-prong jet classification with jet spectra
Original
1904
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A. J. Larkoski and E. M. Metodiev, A Theory of Quark vs. Gluon Discrimination
Original
1906
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E. A. Moreno, O. Cerri, J. M. Duarte, H. B. Newman, T. Q. Nguyen, A. Periwal, M. Pierini, A. Serikova, M. Spiropulu, and J.-R. Vlimant, JEDI-net: a jet identification algorithm based on interaction networks
Original
1908
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1989
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Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, Better jet clustering algorithms
1997
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hep-ph/9907280
M. Wobisch and T. Wengler, Hadronization corrections to jet cross-sections in deep inelastic scattering · 1998
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M. Dasgupta, F. A. Dreyer, K. Hamilton, P. F. Monni, G. P. Salam, and G. Soyez, Parton showers beyond leading logarithmic accuracy
Original
2002
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A. Chakraborty, S. H. Lim, M. M. Nojiri, and M. Takeuchi, Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
Original
2003
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G. Kasieczka, S. Marzani, G. Soyez, and G. Stagnitto, Towards Machine Learning Analytics for Jet Substructure
Original
2007
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A. Lifson, G. P. Salam, and G. Soyez, Calculating the primary Lund Jet Plane density
Original
2007
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M. Cacciari, G. P. Salam, and G. Soyez, The Anti-k(t) jet clustering algorithm
Original
2008
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G. P. Salam, Towards Jetography
Original
2010
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S. H. Lim and M. M. Nojiri, Morphology for Jet Classification
Original
2010
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A. Abdesselam et al., Boosted Objects: A Probe of Beyond the Standard Model Physics
Original
2011
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G. Agarwal, L. Hay, I. Iashvili, B. Mannix, C. McLean, M. Morris, S. Rappoccio, and U. Schubert, Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation
Original
2011
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X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks
2011
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Similar

J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC
Original
2011
Cited alongside, same era.
A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
Original
2012
Cited alongside, same era.
M. J. Dolan and A. Ore, Equivariant Energy Flow Networks for Jet Tagging
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2012
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Then

C. Frye, A. J. Larkoski, J. Thaler, and K. Zhou, Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
Original
2017
Later among the works it cites.
A. Butter, G. Kasieczka, T. Plehn, and M. Russell, Deep-learned Top Tagging with a Lorentz Layer
Original
2018
Later among the works it cites.
T. Cheng, Recursive Neural Networks in Quark/Gluon Tagging
Original
2018
Later among the works it cites.
S. Macaluso and D. Shih, Pulling Out All the Tops with Computer Vision and Deep Learning
Original
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Later among the works it cites.
K. Datta and A. J. Larkoski, Novel Jet Observables from Machine Learning
Original

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2020

Jet tagging in the Lund plane with graph networks

Dreyer, Frédéric A., Qu, Huilin

Understand

The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider.

In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events.
We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms.
We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions.

Built on

J. Ren, L. Wu, and J. M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
Original
1901
Earlier work this paper cites.
A. Butter et al., The Machine Learning Landscape of Top Taggers
Original
1902
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds
Original
1902
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Jet grooming through reinforcement learning
Original
1903
Earlier work this paper cites.
A. Chakraborty, S. H. Lim, and M. M. Nojiri, Interpretable deep learning for two-prong jet classification with jet spectra
Original
1904
Earlier work this paper cites.
A. J. Larkoski and E. M. Metodiev, A Theory of Quark vs. Gluon Discrimination
Original
1906
Earlier work this paper cites.
E. A. Moreno, O. Cerri, J. M. Duarte, H. B. Newman, T. Q. Nguyen, A. Periwal, M. Pierini, A. Serikova, M. Spiropulu, and J.-R. Vlimant, JEDI-net: a jet identification algorithm based on interaction networks
Original
1908
Earlier work this paper cites.
B. Andersson, G. Gustafson, L. Lonnblad, and U. Pettersson, Coherence Effects in Deep Inelastic Scattering
1989
Earlier work this paper cites.
Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, Better jet clustering algorithms
1997
Earlier work this paper cites.
hep-ph/9907280
M. Wobisch and T. Wengler, Hadronization corrections to jet cross-sections in deep inelastic scattering · 1998
Earlier work this paper cites.
M. Dasgupta, F. A. Dreyer, K. Hamilton, P. F. Monni, G. P. Salam, and G. Soyez, Parton showers beyond leading logarithmic accuracy
Original
2002
Earlier work this paper cites.
A. Chakraborty, S. H. Lim, M. M. Nojiri, and M. Takeuchi, Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
Original
2003
Earlier work this paper cites.
G. Kasieczka, S. Marzani, G. Soyez, and G. Stagnitto, Towards Machine Learning Analytics for Jet Substructure
Original
2007
Earlier work this paper cites.
A. Lifson, G. P. Salam, and G. Soyez, Calculating the primary Lund Jet Plane density
Original
2007
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The Anti-k(t) jet clustering algorithm
Original
2008
Earlier work this paper cites.
G. P. Salam, Towards Jetography
Original
2010
Earlier work this paper cites.
S. H. Lim and M. M. Nojiri, Morphology for Jet Classification
Original
2010
Earlier work this paper cites.
A. Abdesselam et al., Boosted Objects: A Probe of Beyond the Standard Model Physics
Original
2011
Earlier work this paper cites.
G. Agarwal, L. Hay, I. Iashvili, B. Mannix, C. McLean, M. Morris, S. Rappoccio, and U. Schubert, Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation
Original
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks
2011
Earlier work this paper cites.

Similar

J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC
Original
2011
Cited alongside, same era.
A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
Original
2012
Cited alongside, same era.
M. J. Dolan and A. Ore, Equivariant Energy Flow Networks for Jet Tagging
Original
2012
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
Cited alongside, same era.
M. Son, C. Spethmann, and B. Tweedie, Diboson-Jets and the Search for Resonant Zh Production
Original
2012

Then

C. Frye, A. J. Larkoski, J. Thaler, and K. Zhou, Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
Original
2017
Later among the works it cites.
A. Butter, G. Kasieczka, T. Plehn, and M. Russell, Deep-learned Top Tagging with a Lorentz Layer
Original
2018
Later among the works it cites.
T. Cheng, Recursive Neural Networks in Quark/Gluon Tagging
Original
2018
Later among the works it cites.
S. Macaluso and D. Shih, Pulling Out All the Tops with Computer Vision and Deep Learning
Original
2018
Later among the works it cites.
K. Datta and A. J. Larkoski, Novel Jet Observables from Machine Learning
Original

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

A. J. Larkoski, G. P. Salam, and J. Thaler, Energy Correlation Functions for Jet Substructure

Original

2013

Cited alongside, same era.

A. Altheimer et al., Boosted Objects and Jet Substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012

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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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DELPHES 3

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2014

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2018