Built on

A. Butter et al. , “The Machine Learning Landscape of Top Taggers,” SciPost Phys. 7
Original
1902
Earlier work this paper cites.
D. E. Kaplan, K. Rehermann, M. D. Schwartz, and B. Tweedie, “Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks,” Phys. Rev. Lett. 101
Original
2008
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.
J. Gallicchio and M. D. Schwartz, “Quark and Gluon Tagging at the LHC,” Phys. Rev. Lett. 107
Original
2011
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,” Boost 2010 Oxford, United Kingdom, June 22-25, 2010 , Eur. Phys. J. C71
Original
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , Vol. 15 (PMLR, Fort Lauderdale, FL, USA, 2011) pp. 315–323
2011
Earlier work this paper cites.
T. Plehn, M. Spannowsky, and M. Takeuchi, “How to Improve Top Tagging,” Phys. Rev. D85
Original
2012
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,” BOOST 2011 Princeton , NJ, USA, 22–26 May 2011 , J. Phys. G39
Original
2012
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.
D. E. Soper and M. Spannowsky, “Finding top quarks with shower deconstruction,” Phys. Rev. D87
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.
C. Anders, C. Bernaciak, G. Kasieczka, T. Plehn, and T. Schell, “Benchmarking an even better top tagger algorithm,” Phys. Rev. D89
Original
2014
Earlier work this paper cites.
A. Altheimer et al. , “Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012,” BOOST 2012 Valencia, Spain, July 23-27, 2012 , Eur. Phys. J. C74
Original
2014
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision – ECCV 2014 (Springer, Cham, 2014) pp. 818–833
2014
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,” Journal of Machine Learning Research 15
2014
Earlier work this paper cites.
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), “DELPHES 3, A modular framework for fast simulation of a generic collider experiment,” JHEP 02
Original
2014
Earlier work this paper cites.
B. Bhattacherjee, S. Mukhopadhyay, M. M. Nojiri, Y. Sakaki, and B. R. Webber, “Associated jet and subjet rates in light-quark and gluon jet discrimination,” JHEP 04
Original
2015
Earlier work this paper cites.
G. Kasieczka, T. Plehn, T. Schell, T. Strebler, and G. P. Salam, “Resonance Searches with an Updated Top Tagger,” JHEP 06
Original
2015
Earlier work this paper cites.
D. Adams et al. , “Towards an Understanding of the Correlations in Jet Substructure,” Eur. Phys. J. C75
Original
2015
Earlier work this paper cites.
J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, “Jet-Images: Computer Vision Inspired Techniques for Jet Tagging,” JHEP 02
Original
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning , Vol. 37 (PMLR, Lille, France, 2015) pp. 448–456
2015
Earlier work this paper cites.

Similar

Then

E. M. Metodiev, B. Nachman, and J. Thaler, “Classification without labels: Learning from mixed samples in high energy physics,” JHEP 10
Original
2017
Later among the works it cites.
A. M. Sirunyan et al. (CMS), “Particle-flow reconstruction and global event description with the CMS detector,” JINST 12
Original
2017
Later among the works it cites.
M. Aaboud et al. (ATLAS), “Jet reconstruction and performance using particle flow with the ATLAS Detector,” Eur. Phys. J. C77
Original
2017
Later among the works it cites.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” in Advances in Neural Information Processing Systems 30 (Curran Associates, Inc., 2017) pp. 3391–3401
2017
Later among the works it cites.
I. Loshchilov and F. Hutter, “Fixing weight decay regularization in adam,” (2017), arXiv:1711.05101

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Reading the bibliography…

2019

ParticleNet: Jet Tagging via Particle Clouds

Qu, Huilin, Gouskos, Loukas

Understand

How to represent a jet is at the core of machine learning on jet physics.

Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud".
Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry.
Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems.

Built on

A. Butter et al. , “The Machine Learning Landscape of Top Taggers,” SciPost Phys. 7
Original
1902
Earlier work this paper cites.
D. E. Kaplan, K. Rehermann, M. D. Schwartz, and B. Tweedie, “Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks,” Phys. Rev. Lett. 101
Original
2008
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.
J. Gallicchio and M. D. Schwartz, “Quark and Gluon Tagging at the LHC,” Phys. Rev. Lett. 107
Original
2011
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,” Boost 2010 Oxford, United Kingdom, June 22-25, 2010 , Eur. Phys. J. C71
Original
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , Vol. 15 (PMLR, Fort Lauderdale, FL, USA, 2011) pp. 315–323
2011
Earlier work this paper cites.
T. Plehn, M. Spannowsky, and M. Takeuchi, “How to Improve Top Tagging,” Phys. Rev. D85
Original
2012
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,” BOOST 2011 Princeton , NJ, USA, 22–26 May 2011 , J. Phys. G39
Original
2012
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.
D. E. Soper and M. Spannowsky, “Finding top quarks with shower deconstruction,” Phys. Rev. D87
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.
C. Anders, C. Bernaciak, G. Kasieczka, T. Plehn, and T. Schell, “Benchmarking an even better top tagger algorithm,” Phys. Rev. D89
Original
2014
Earlier work this paper cites.
A. Altheimer et al. , “Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012,” BOOST 2012 Valencia, Spain, July 23-27, 2012 , Eur. Phys. J. C74
Original
2014
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision – ECCV 2014 (Springer, Cham, 2014) pp. 818–833
2014
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,” Journal of Machine Learning Research 15
2014
Earlier work this paper cites.
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), “DELPHES 3, A modular framework for fast simulation of a generic collider experiment,” JHEP 02
Original
2014
Earlier work this paper cites.
B. Bhattacherjee, S. Mukhopadhyay, M. M. Nojiri, Y. Sakaki, and B. R. Webber, “Associated jet and subjet rates in light-quark and gluon jet discrimination,” JHEP 04
Original
2015
Earlier work this paper cites.
G. Kasieczka, T. Plehn, T. Schell, T. Strebler, and G. P. Salam, “Resonance Searches with an Updated Top Tagger,” JHEP 06
Original
2015
Earlier work this paper cites.
D. Adams et al. , “Towards an Understanding of the Correlations in Jet Substructure,” Eur. Phys. J. C75
Original
2015
Earlier work this paper cites.
J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, “Jet-Images: Computer Vision Inspired Techniques for Jet Tagging,” JHEP 02
Original
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning , Vol. 37 (PMLR, Lille, France, 2015) pp. 448–456
2015
Earlier work this paper cites.

Similar

Then

E. M. Metodiev, B. Nachman, and J. Thaler, “Classification without labels: Learning from mixed samples in high energy physics,” JHEP 10
Original
2017
Later among the works it cites.
A. M. Sirunyan et al. (CMS), “Particle-flow reconstruction and global event description with the CMS detector,” JINST 12
Original
2017
Later among the works it cites.
M. Aaboud et al. (ATLAS), “Jet reconstruction and performance using particle flow with the ATLAS Detector,” Eur. Phys. J. C77
Original
2017
Later among the works it cites.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” in Advances in Neural Information Processing Systems 30 (Curran Associates, Inc., 2017) pp. 3391–3401
2017
Later among the works it cites.
I. Loshchilov and F. Hutter, “Fixing weight decay regularization in adam,” (2017), arXiv:1711.05101

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, “Jet Substructure Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev. D93

Original

2016

Cited alongside, same era.

D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, “Jet Flavor Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev. D94

Original

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Las Vegas, NV, USA, 2016) pp. 770–778

2016

Cited alongside, same era.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Las Vegas, NV, USA, 2016) pp. 2818–2826

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision – ECCV 2016 (Springer, Cham, 2016) pp. 630–645

2016

Cited alongside, same era.

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

Cited alongside, same era.

P. Gras, S. Höche, D. Kar, A. Larkoski, L. Lönnblad, S. Plätzer, A. Siódmok, P. Skands, G. Soyez, and J. Thaler, “Systematics of quark/gluon tagging,” JHEP 07

Original

2017

Cited alongside, same era.

C. Frye, A. J. Larkoski, J. Thaler, and K. Zhou, “Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables,” JHEP 09

Original

2017

Cited alongside, same era.

Original

2017