Built on

T. Kinoshita, Mass singularities of Feynman amplitudes , J. Math. Phys. 3
1962
Earlier work this paper cites.
T. D. Lee and M. Nauenberg, Degenerate Systems and Mass Singularities , Phys. Rev. 133
1964
Earlier work this paper cites.
G. Parisi, Super Inclusive Cross-Sections , Phys. Lett. 74B
1978
Earlier work this paper cites.
G. C. Fox and S. Wolfram, Observables for the Analysis of Event Shapes in e+ e- Annihilation and Other Processes , Phys. Rev. Lett. 41
1978
Earlier work this paper cites.
J. F. Donoghue, F. E. Low and S.-Y. Pi, Tensor Analysis of Hadronic Jets in Quantum Chromodynamics , Phys. Rev. D20
1979
Earlier work this paper cites.
G. Sterman, J. Smith, J. C. Collins, J. Whitmore, R. Brock, J. Huston et al., Handbook of perturbative qcd , Reviews of Modern Physics 67
1995
Earlier work this paper cites.
N. A. Sveshnikov and F. V. Tkachov, Jets and quantum field theory , Phys. Lett. B382
1996
Earlier work this paper cites.
F. V. Tkachov, Measuring multi - jet structure of hadronic energy flow or What is a jet? , Int. J. Mod. Phys. A12
1997
Earlier work this paper cites.
P. S. Cherzor and N. A. Sveshnikov, Jet observables and energy momentum tensor , in Quantum field theory and high-energy physics. Proceedings, Workshop, QFTHEP’97, Samara, Russia, September 4-10, 1997 , pp. 402–407, 1997, hep-ph/9710349
1997
Earlier work this paper cites.
S. Weinberg, The Quantum theory of fields. Vol. 1: Foundations . Cambridge University Press, 2005
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Earlier work this paper cites.
T. Oliphant, Guide to NumPy . Trelgol Publishing, 2006
2006
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines , in Proceedings of the 27th international conference on machine learning (ICML-10) , pp. 807–814, 2010
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness , JHEP 03
Original
2011
Earlier work this paper cites.
A. Abdesselam et al., Boosted objects: A Probe of beyond the Standard Model physics , Eur. Phys. J. C71
Original
2011
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC , Phys. Rev. Lett. 107
Original
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in python , Journal of Machine Learning Research 12
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness , JHEP 02
Original
2012
Earlier work this paper cites.
A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks , J. Phys. G39
Original
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, FastJet User Manual , Eur. Phys. J. C72
Original
2012
Earlier work this paper cites.
D. Krohn, M. D. Schwartz, T. Lin and W. J. Waalewijn, Jet Charge at the LHC , Phys. Rev. Lett. 110
Original
2013
Earlier work this paper cites.
H.-M. Chang, M. Procura, J. Thaler and W. J. Waalewijn, Calculating Track-Based Observables for the LHC , Phys. Rev. Lett. 111
Original
2013
Earlier work this paper cites.
A. J. Larkoski, G. P. Salam and J. Thaler, Energy Correlation Functions for Jet Substructure , JHEP 06
Original
2013
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Quark and Gluon Jet Substructure , JHEP 04
Original
2013
Earlier work this paper cites.
A. J. Larkoski and J. Thaler, Unsafe but Calculable: Ratios of Angularities in Perturbative QCD , JHEP 09
Original
2013
Earlier work this paper cites.
P. Baldi, P. Sadowski and D. Whiteson, Searching for Exotic Particles in High-Energy Physics with Deep Learning , Nature Commun. 5
Original
2014
Earlier work this paper cites.
A. J. Larkoski, J. Thaler and W. J. Waalewijn, Gaining (Mutual) Information about Quark/Gluon Discrimination , JHEP 11
Original
2014
Earlier work this paper cites.
A. J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop , JHEP 05
Original
2014
Earlier work this paper cites.

Similar

A. Altheimer et al., Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012 , Eur. Phys. J. C74
Original
2014
Cited alongside, same era.
A. J. Larkoski, D. Neill and J. Thaler, Jet Shapes with the Broadening Axis , JHEP 04
Original
2014
Cited alongside, same era.
A. J. Larkoski, I. Moult and D. Neill, Toward Multi-Differential Cross Sections: Measuring Two Angularities on a Single Jet , JHEP 09
Original
2014
Cited alongside, same era.
D. Bertolini, P. Harris, M. Low and N. Tran, Pileup Per Particle Identification , JHEP 10
Original
2014
Cited alongside, same era.
DELPHES 3
Original

Then

L. de Oliveira, M. Paganini and B. Nachman, Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters , in 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) Seattle, WA, USA, August 21-25, 2017 , 2017, 1711.08813
Original
2017
Later among the works it cites.
E. Barberio, B. Le, E. Richter-Was, Z. Was, D. Zanzi and J. Zaremba, Deep learning approach to the Higgs boson CP measurement in H → τ τ H\to\tau\tau decay and associated systematics , Phys. Rev. D96
Original
2017
Later among the works it cites.
C. R. Qi, H. Su, K. Mo and L. J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation , in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , pp. 77–85, 2017, DOI
2017
Later among the works it cites.
S. H. Rezatofighi, V. K. B. G, A. Milan, E. Abbasnejad, A. R. Dick and I. D. Reid, Deepsetnet: Predicting sets with deep neural networks , in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017 , pp. 5257–5266, 2017, DOI

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2018

Energy Flow Networks: Deep Sets for Particle Jets

Komiske, Patrick T., Metodiev, Eric M., Thaler, Jesse

Understand

A key question for machine learning approaches in particle physics is how to best represent and learn from collider events.

As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or "point clouds".
Adapting and specializing the "Deep Sets" framework to particle physics, we introduce Energy Flow Networks, which respect infrared and collinear safety by construction.
We also develop Particle Flow Networks, which allow for general energy dependence and the inclusion of additional particle-level information such as charge and flavor.

Built on

T. Kinoshita, Mass singularities of Feynman amplitudes , J. Math. Phys. 3
1962
Earlier work this paper cites.
T. D. Lee and M. Nauenberg, Degenerate Systems and Mass Singularities , Phys. Rev. 133
1964
Earlier work this paper cites.
G. Parisi, Super Inclusive Cross-Sections , Phys. Lett. 74B
1978
Earlier work this paper cites.
G. C. Fox and S. Wolfram, Observables for the Analysis of Event Shapes in e+ e- Annihilation and Other Processes , Phys. Rev. Lett. 41
1978
Earlier work this paper cites.
J. F. Donoghue, F. E. Low and S.-Y. Pi, Tensor Analysis of Hadronic Jets in Quantum Chromodynamics , Phys. Rev. D20
1979
Earlier work this paper cites.
G. Sterman, J. Smith, J. C. Collins, J. Whitmore, R. Brock, J. Huston et al., Handbook of perturbative qcd , Reviews of Modern Physics 67
1995
Earlier work this paper cites.
N. A. Sveshnikov and F. V. Tkachov, Jets and quantum field theory , Phys. Lett. B382
1996
Earlier work this paper cites.
F. V. Tkachov, Measuring multi - jet structure of hadronic energy flow or What is a jet? , Int. J. Mod. Phys. A12
1997
Earlier work this paper cites.
P. S. Cherzor and N. A. Sveshnikov, Jet observables and energy momentum tensor , in Quantum field theory and high-energy physics. Proceedings, Workshop, QFTHEP’97, Samara, Russia, September 4-10, 1997 , pp. 402–407, 1997, hep-ph/9710349
1997
Earlier work this paper cites.
S. Weinberg, The Quantum theory of fields. Vol. 1: Foundations . Cambridge University Press, 2005
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Earlier work this paper cites.
T. Oliphant, Guide to NumPy . Trelgol Publishing, 2006
2006
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines , in Proceedings of the 27th international conference on machine learning (ICML-10) , pp. 807–814, 2010
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness , JHEP 03
Original
2011
Earlier work this paper cites.
A. Abdesselam et al., Boosted objects: A Probe of beyond the Standard Model physics , Eur. Phys. J. C71
Original
2011
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC , Phys. Rev. Lett. 107
Original
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in python , Journal of Machine Learning Research 12
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness , JHEP 02
Original
2012
Earlier work this paper cites.
A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks , J. Phys. G39
Original
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, FastJet User Manual , Eur. Phys. J. C72
Original
2012
Earlier work this paper cites.
D. Krohn, M. D. Schwartz, T. Lin and W. J. Waalewijn, Jet Charge at the LHC , Phys. Rev. Lett. 110
Original
2013
Earlier work this paper cites.
H.-M. Chang, M. Procura, J. Thaler and W. J. Waalewijn, Calculating Track-Based Observables for the LHC , Phys. Rev. Lett. 111
Original
2013
Earlier work this paper cites.
A. J. Larkoski, G. P. Salam and J. Thaler, Energy Correlation Functions for Jet Substructure , JHEP 06
Original
2013
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Quark and Gluon Jet Substructure , JHEP 04
Original
2013
Earlier work this paper cites.
A. J. Larkoski and J. Thaler, Unsafe but Calculable: Ratios of Angularities in Perturbative QCD , JHEP 09
Original
2013
Earlier work this paper cites.
P. Baldi, P. Sadowski and D. Whiteson, Searching for Exotic Particles in High-Energy Physics with Deep Learning , Nature Commun. 5
Original
2014
Earlier work this paper cites.
A. J. Larkoski, J. Thaler and W. J. Waalewijn, Gaining (Mutual) Information about Quark/Gluon Discrimination , JHEP 11
Original
2014
Earlier work this paper cites.
A. J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop , JHEP 05
Original
2014
Earlier work this paper cites.

Similar

A. Altheimer et al., Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012 , Eur. Phys. J. C74
Original
2014
Cited alongside, same era.
A. J. Larkoski, D. Neill and J. Thaler, Jet Shapes with the Broadening Axis , JHEP 04
Original
2014
Cited alongside, same era.
A. J. Larkoski, I. Moult and D. Neill, Toward Multi-Differential Cross Sections: Measuring Two Angularities on a Single Jet , JHEP 09
Original
2014
Cited alongside, same era.
D. Bertolini, P. Harris, M. Low and N. Tran, Pileup Per Particle Identification , JHEP 10
Original
2014
Cited alongside, same era.
DELPHES 3
Original

Then

L. de Oliveira, M. Paganini and B. Nachman, Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters , in 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) Seattle, WA, USA, August 21-25, 2017 , 2017, 1711.08813
Original
2017
Later among the works it cites.
E. Barberio, B. Le, E. Richter-Was, Z. Was, D. Zanzi and J. Zaremba, Deep learning approach to the Higgs boson CP measurement in H → τ τ H\to\tau\tau decay and associated systematics , Phys. Rev. D96
Original
2017
Later among the works it cites.
C. R. Qi, H. Su, K. Mo and L. J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation , in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , pp. 77–85, 2017, DOI
2017
Later among the works it cites.
S. H. Rezatofighi, V. K. B. G, A. Milan, E. Abbasnejad, A. R. Dick and I. D. Reid, Deepsetnet: Predicting sets with deep neural networks , in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017 , pp. 5257–5266, 2017, DOI

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.

J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging , JHEP 02

Original

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 , JHEP 07

Original

2015

Cited alongside, same era.

P. Baldi, P. Sadowski and D. Whiteson, Enhanced Higgs Boson to τ + τ − \tau^{+}\tau^{-} Search with Deep Learning , Phys. Rev. Lett. 114

Original

2015

Cited alongside, same era.

M. Iyyer, V. Manjunatha, J. L. Boyd-Graber and H. D. III, Deep unordered composition rivals syntactic methods for text classification , in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers , pp. 1681–1691, 2015

2015

Cited alongside, same era.

D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure , Eur. Phys. J. C75

Original

2015

Cited alongside, same era.

A. J. Larkoski, S. Marzani and J. Thaler, Sudakov Safety in Perturbative QCD , Phys. Rev. D91

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 , in Proceedings of the IEEE international conference on computer vision , pp. 1026–1034, 2015

2015

Cited alongside, same era.

2017