Built on

J. Ren, L. Wu, and J. M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC, Phys. Lett. B 802
Original
1901
Earlier work this paper cites.
E. Wagstaff, F. B. Fuchs, M. Engelcke, I. Posner, and M. A. Osborne, On the limitations of representing functions on sets, CoRR (2019) , arXiv:1901.09006
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101
Original
1902
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, JHEP 07
Original
1904
Earlier work this paper cites.
A. J. Larkoski and E. M. Metodiev, A Theory of Quark vs. Gluon Discrimination, JHEP 10
Original
1906
Earlier work this paper cites.
C. Pabbaraju and P. Jain, Learning functions over sets via permutation adversarial networks, CoRR (2019) , arXiv:1907.05638
Original
1907
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, Eur. Phys. J. C 80
Original
1908
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers, SciPost Phys. 8
Original
1908
Earlier work this paper cites.
M. H. Stone, The generalized weierstrass approximation theorem, Math. Mag. 21
1948
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Comput. 1
1989
Earlier work this paper cites.
L. Lonnblad, C. Peterson, and T. Rognvaldsson, Using neural networks to identify jets, Nucl. Phys. B349
1991
Earlier work this paper cites.
S. Catani, Y. L. Dokshitzer, M. H. Seymour, and B. R. Webber, Longitudinally invariant k t k_{t} clustering algorithms for hadron hadron collisions, Nucl. Phys. B406
1993
Earlier work this paper cites.
G. Kasieczka and D. Shih, Robust Jet Classifiers through Distance Correlation, Phys. Rev. Lett. 125
Original
2001
Earlier work this paper cites.
M. Abdughani, D. Wang, L. Wu, J. M. Yang, and J. Zhao, Probing triple Higgs coupling with machine learning at the LHC, (2020), arXiv:2005.11086 [hep-ph]
Original
2005
Earlier work this paper cites.
A. Bogatskiy, B. Anderson, J. T. Offermann, M. Roussi, D. W. Miller, and R. Kondor, Lorentz Group Equivariant Neural Network for Particle Physics, (2020), arXiv:2006.04780 [hep-ph]
Original
2006
Earlier work this paper cites.
G. Kasieczka, S. Marzani, G. Soyez, and G. Stagnitto, Towards Machine Learning Analytics for Jet Substructure, JHEP 09
Original
2007
Earlier work this paper cites.
T. Dorigo and P. De Castro Manzano, Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review, (2020), arXiv:2007.09121 [stat.ML]
Original
2007
Earlier work this paper cites.
J. Aguilar-Saavedra, F. Joaquim, and J. Seabra, Mass Unspecific Supervised Tagging (MUST) for boosted jets, (2020), arXiv:2008.12792 [hep-ph]
Original
2008
Earlier work this paper cites.

Similar

J. M. Butterworth, A. R. Davison, M. Rubin, and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC, Phys. Rev. Lett. 100
Original
2008
Cited alongside, same era.
X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons, Phys. Rev. D 102
Original
2008
Cited alongside, same era.
G. P. Salam, Towards Jetography, Eur. Phys. J. C67
Original
2010
Cited alongside, same era.
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, in ICML 10 (2010) pp. 807–814
2010
Cited alongside, same era.
T. Faucett, J. Thaler, and D. Whiteson, Mapping Machine-Learned Physics into a Human-Readable Space, (2020), arXiv:2010.11998 [hep-ph]
Original

Then

J. Pearkes, W. Fedorko, A. Lister, and C. Gay, Jet Constituents for Deep Neural Network Based Top Quark Tagging, (2017), arXiv:1704.02124 [hep-ex]
Original
2017
Later among the works it cites.
I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe, and G. Rochette, Neural message passing for jet physics, in DLPS (2017)
2017
Later among the works it cites.
D. Ferreira de Lima, P. Petrov, D. Soper, and M. Spannowsky, Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings, Phys. Rev. D95
Original
2017
Later among the works it cites.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Póczos, R. Salakhutdinov, and A. J. Smola, Deep sets, CoRR (2017) , arXiv:1703.06114 [cs.LG]
Original
2017
Later among the works it cites.
C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Søgaard, Decorrelated Jet Substructure Tagging using Adversarial Neural Networks, Phys. Rev. D 96

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2020

Equivariant Energy Flow Networks for Jet Tagging

Dolan, Matthew J., Ore, Ayodele

Understand

Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders.

One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety.
We develop a variant of the Energy Flow Network architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers.
We derive conditions under which infrared and collinear safety can be maintained, and study the performance of these networks on the canonical example of W-boson tagging.

Built on

J. Ren, L. Wu, and J. M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC, Phys. Lett. B 802
Original
1901
Earlier work this paper cites.
E. Wagstaff, F. B. Fuchs, M. Engelcke, I. Posner, and M. A. Osborne, On the limitations of representing functions on sets, CoRR (2019) , arXiv:1901.09006
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101
Original
1902
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, JHEP 07
Original
1904
Earlier work this paper cites.
A. J. Larkoski and E. M. Metodiev, A Theory of Quark vs. Gluon Discrimination, JHEP 10
Original
1906
Earlier work this paper cites.
C. Pabbaraju and P. Jain, Learning functions over sets via permutation adversarial networks, CoRR (2019) , arXiv:1907.05638
Original
1907
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, Eur. Phys. J. C 80
Original
1908
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers, SciPost Phys. 8
Original
1908
Earlier work this paper cites.
M. H. Stone, The generalized weierstrass approximation theorem, Math. Mag. 21
1948
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Comput. 1
1989
Earlier work this paper cites.
L. Lonnblad, C. Peterson, and T. Rognvaldsson, Using neural networks to identify jets, Nucl. Phys. B349
1991
Earlier work this paper cites.
S. Catani, Y. L. Dokshitzer, M. H. Seymour, and B. R. Webber, Longitudinally invariant k t k_{t} clustering algorithms for hadron hadron collisions, Nucl. Phys. B406
1993
Earlier work this paper cites.
G. Kasieczka and D. Shih, Robust Jet Classifiers through Distance Correlation, Phys. Rev. Lett. 125
Original
2001
Earlier work this paper cites.
M. Abdughani, D. Wang, L. Wu, J. M. Yang, and J. Zhao, Probing triple Higgs coupling with machine learning at the LHC, (2020), arXiv:2005.11086 [hep-ph]
Original
2005
Earlier work this paper cites.
A. Bogatskiy, B. Anderson, J. T. Offermann, M. Roussi, D. W. Miller, and R. Kondor, Lorentz Group Equivariant Neural Network for Particle Physics, (2020), arXiv:2006.04780 [hep-ph]
Original
2006
Earlier work this paper cites.
G. Kasieczka, S. Marzani, G. Soyez, and G. Stagnitto, Towards Machine Learning Analytics for Jet Substructure, JHEP 09
Original
2007
Earlier work this paper cites.
T. Dorigo and P. De Castro Manzano, Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review, (2020), arXiv:2007.09121 [stat.ML]
Original
2007
Earlier work this paper cites.
J. Aguilar-Saavedra, F. Joaquim, and J. Seabra, Mass Unspecific Supervised Tagging (MUST) for boosted jets, (2020), arXiv:2008.12792 [hep-ph]
Original
2008
Earlier work this paper cites.

Similar

J. M. Butterworth, A. R. Davison, M. Rubin, and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC, Phys. Rev. Lett. 100
Original
2008
Cited alongside, same era.
X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons, Phys. Rev. D 102
Original
2008
Cited alongside, same era.
G. P. Salam, Towards Jetography, Eur. Phys. J. C67
Original
2010
Cited alongside, same era.
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, in ICML 10 (2010) pp. 807–814
2010
Cited alongside, same era.
T. Faucett, J. Thaler, and D. Whiteson, Mapping Machine-Learned Physics into a Human-Readable Space, (2020), arXiv:2010.11998 [hep-ph]
Original

Then

J. Pearkes, W. Fedorko, A. Lister, and C. Gay, Jet Constituents for Deep Neural Network Based Top Quark Tagging, (2017), arXiv:1704.02124 [hep-ex]
Original
2017
Later among the works it cites.
I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe, and G. Rochette, Neural message passing for jet physics, in DLPS (2017)
2017
Later among the works it cites.
D. Ferreira de Lima, P. Petrov, D. Soper, and M. Spannowsky, Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings, Phys. Rev. D95
Original
2017
Later among the works it cites.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Póczos, R. Salakhutdinov, and A. J. Smola, Deep sets, CoRR (2017) , arXiv:1703.06114 [cs.LG]
Original
2017
Later among the works it cites.
C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Søgaard, Decorrelated Jet Substructure Tagging using Adversarial Neural Networks, Phys. Rev. D 96

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2010

Cited alongside, same era.

J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC, Phys. Rev. Lett. 107

Original

2011

Cited alongside, same era.

T. Plehn and M. Spannowsky, Top tagging, J. Phys. G 39

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.

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

2014

Cited alongside, same era.

T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An Introduction to PYTHIA 8.2, Comput. Phys. Commun. 191

Original

2015

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, CoRR (2015) , arXiv:1502.01852

Original

2015

Cited alongside, same era.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, in ICLR (2015) arXiv:1412.6980

Original

2015

Cited alongside, same era.

Original

2017