publication . Article . Other literature type . Preprint . 2018

Recursive Neural Networks in Quark/Gluon Tagging

Cheng, Taoli;
Open Access
  • Published: 28 Jun 2018 Journal: Computing and Software for Big Science, volume 2 (issn: 2510-2036, eissn: 2510-2044, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
Abstract
Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure. And it turns out that deep neural networks can match or outperform traditional approach of expert features. However, there are disadvantages such as sparseness of jet images. Based on the natural tree-like structure of jet sequential clustering, the recursive neural networks (RecNNs), which embed jet clustering history recursively as in natural language processing, have a better behavior when confronted with these problems. We thus try to explore the performance of RecNNs in quark/glu...
Subjects
free text keywords: Particle physics, Physics, Rejection rate, Quark, Artificial intelligence, business.industry, business, Recursion, Cluster analysis, Down quark, Deep learning, Gradient boosting, Artificial neural network, Algorithm, High Energy Physics - Phenomenology, High Energy Physics - Experiment, Statistics - Machine Learning
35 references, page 1 of 3

[1] A. J. Larkoski, I. Moult, and B. Nachman, Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning, arXiv:1709.04464.

[2] D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure, Eur. Phys. J. C75 (2015), no. 9 409, [arXiv:1504.00679].

[3] J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118, [arXiv:1407.5675].

[4] L. G. Almeida, M. Backovi, M. Cliche, S. J. Lee, and M. Perelstein, Playing Tag with ANN: Boosted Top Identi cation with Pattern Recognition, JHEP 07 (2015) 086, [arXiv:1501.05968].

[5] J. Pearkes, W. Fedorko, A. Lister, and C. Gay, Jet Constituents for Deep Neural Network Based Top Quark Tagging, arXiv:1704.02124.

[6] P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, Jet Substructure Classi cation in High-Energy Physics with Deep Neural Networks, Phys. Rev. D93 (2016), no. 9 094034, [arXiv:1603.09349].

[7] D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, Jet Flavor Classi cation in High-Energy Physics with Deep Neural Networks, Phys. Rev. D94 (2016), no. 11 112002, [arXiv:1607.08633].

[8] J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks, Phys. Rev. D95 (2017), no. 1 014018, [arXiv:1609.00607].

[9] L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, Jet-images deep learning edition, JHEP 07 (2016) 069, [arXiv:1511.05190].

[10] P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination, arXiv:1612.01551.

[12] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

[13] G. Louppe, K. Cho, C. Becot, and K. Cranmer, QCD-Aware Recursive Neural Networks for Jet Physics, arXiv:1702.00748.

[14] ATLAS Collaboration, G. Aad et al., Measurement of the charged-particle multiplicity inside jets from ps = 8 TeV pp collisions with the ATLAS detector, Eur. Phys. J. C76 (2016), no. 6 322, [arXiv:1602.00988].

[15] J. Gallicchio and M. D. Schwartz, Quark and Gluon Jet Substructure, JHEP 04 (2013) 090, [arXiv:1211.7038].

[16] ATLAS Collaboration Collaboration, Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector, Tech. Rep. ATL-PHYS-PUB-2017-017, CERN, Geneva, Jul, 2017.

35 references, page 1 of 3
Related research
Abstract
Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure. And it turns out that deep neural networks can match or outperform traditional approach of expert features. However, there are disadvantages such as sparseness of jet images. Based on the natural tree-like structure of jet sequential clustering, the recursive neural networks (RecNNs), which embed jet clustering history recursively as in natural language processing, have a better behavior when confronted with these problems. We thus try to explore the performance of RecNNs in quark/glu...
Subjects
free text keywords: Particle physics, Physics, Rejection rate, Quark, Artificial intelligence, business.industry, business, Recursion, Cluster analysis, Down quark, Deep learning, Gradient boosting, Artificial neural network, Algorithm, High Energy Physics - Phenomenology, High Energy Physics - Experiment, Statistics - Machine Learning
35 references, page 1 of 3

[1] A. J. Larkoski, I. Moult, and B. Nachman, Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning, arXiv:1709.04464.

[2] D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure, Eur. Phys. J. C75 (2015), no. 9 409, [arXiv:1504.00679].

[3] J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118, [arXiv:1407.5675].

[4] L. G. Almeida, M. Backovi, M. Cliche, S. J. Lee, and M. Perelstein, Playing Tag with ANN: Boosted Top Identi cation with Pattern Recognition, JHEP 07 (2015) 086, [arXiv:1501.05968].

[5] J. Pearkes, W. Fedorko, A. Lister, and C. Gay, Jet Constituents for Deep Neural Network Based Top Quark Tagging, arXiv:1704.02124.

[6] P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, Jet Substructure Classi cation in High-Energy Physics with Deep Neural Networks, Phys. Rev. D93 (2016), no. 9 094034, [arXiv:1603.09349].

[7] D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, Jet Flavor Classi cation in High-Energy Physics with Deep Neural Networks, Phys. Rev. D94 (2016), no. 11 112002, [arXiv:1607.08633].

[8] J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks, Phys. Rev. D95 (2017), no. 1 014018, [arXiv:1609.00607].

[9] L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, Jet-images deep learning edition, JHEP 07 (2016) 069, [arXiv:1511.05190].

[10] P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination, arXiv:1612.01551.

[12] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

[13] G. Louppe, K. Cho, C. Becot, and K. Cranmer, QCD-Aware Recursive Neural Networks for Jet Physics, arXiv:1702.00748.

[14] ATLAS Collaboration, G. Aad et al., Measurement of the charged-particle multiplicity inside jets from ps = 8 TeV pp collisions with the ATLAS detector, Eur. Phys. J. C76 (2016), no. 6 322, [arXiv:1602.00988].

[15] J. Gallicchio and M. D. Schwartz, Quark and Gluon Jet Substructure, JHEP 04 (2013) 090, [arXiv:1211.7038].

[16] ATLAS Collaboration Collaboration, Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector, Tech. Rep. ATL-PHYS-PUB-2017-017, CERN, Geneva, Jul, 2017.

35 references, page 1 of 3
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publication . Article . Other literature type . Preprint . 2018

Recursive Neural Networks in Quark/Gluon Tagging

Cheng, Taoli;