Jet-images — deep learning edition

Journal of High Energy Physics

Volumn 2016, Issue 7, 2016, Pages

  de Oliveira, Luke ^a   Kagan, Michael ^b   Mackey, Lester ^c   Nachman, Benjamin ^b   Schwartzman, Ariel ^b  

^a Stanford University   (United States)

^b SLAC NATIONAL ACCELERATOR LABORATORY   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

Hadron Hadron scattering (experiments); Jet substructure

Indexed keywords

EID: 84978898035     PISSN: 11266708     EISSN: 10298479     Source Type: Journal    
DOI: 10.1007/JHEP07(2016)069     Document Type: Article

References (44)

1. A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks, J. Phys. G 39 (2012) 063001 [arXiv:1201.0008] [INSPIRE].
2. 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. C 74 (2014) 2792 [arXiv:1311.2708] [INSPIRE].
3. D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure, Eur. Phys. J. C 75 (2015) 409 [arXiv:1504.00679] [INSPIRE].
4. 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] [INSPIRE].
5. 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 (2015) 086 [arXiv:1501.05968] [INSPIRE].
6. K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556.
7. I.J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville and Y. Bengio, Maxout Networks, arXiv:1302.4389.
8. G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, arXiv:1207.0580.
9. CMS collaboration, Identification techniques for highly boosted W bosons that decay into hadrons, JHEP 12 (2014) 017 [arXiv:1410.4227] [INSPIRE].
10. ATLAS collaboration, Identification of boosted, hadronically-decaying W and Z bosons in √s=13 TeV Monte Carlo Simulations for ATLAS, ATL-PHYS-PUB-2015-033 (2015).
11. ATLAS collaboration, Performance of Boosted W Boson Identification with the ATLAS Detector, ATL-PHYS-PUB-2014-004 (2014).
12. ATLAS collaboration, Search for high-mass diboson resonances with boson-tagged jets in proton-proton collisions at s=8 TeV with the ATLAS detector, JHEP 12 (2015) 055 [arXiv:1506.00962] [INSPIRE].
13. CMS collaboration, Search for massive resonances in dijet systems containing jets tagged as W or Z boson decays in pp collisions at √s=8 TeV, JHEP 08 (2014) 173 [arXiv:1405.1994] [INSPIRE].
14. CMS collaboration, Search for the production of an excited bottom quark decaying to tW in proton-proton collisions at √s=8 TeV, JHEP 01 (2016) 166 [arXiv:1509.08141] [INSPIRE].
15. CMS collaboration, Search for vector-like charge 2/3 T quarks in proton-proton collisions at √s=8 TeV, Phys. Rev. D 93 (2016) 012003 [arXiv:1509.04177] [INSPIRE].
16. CMS collaboration, Search for pair-produced vectorlike B quarks in proton-proton collisions at √s=8 TeV, Phys. Rev. D 93 (2016) 112009 [arXiv:1507.07129] [INSPIRE].
17. CMS collaboration, Search for a massive resonance decaying into a Higgs boson and a W or Z boson in hadronic final states in proton-proton collisions at √s=8 TeV, JHEP 02 (2016) 145 [arXiv:1506.01443] [INSPIRE].
18. CMS collaboration, Search for a Higgs Boson in the Mass Range from 145 to 1000 GeV Decaying to a Pair of W or Z Bosons, JHEP 10 (2015) 144 [arXiv:1504.00936] [INSPIRE].
19. CMS collaboration, Search for Narrow High-Mass Resonances in Proton-Proton Collisions at √s=8 TeV Decaying to a Z and a Higgs Boson, Phys. Lett. B 748 (2015) 255 [arXiv:1502.04994] [INSPIRE].
20. ATLAS collaboration, Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using √s=8 TeV proton-proton collision data, JHEP 09 (2014) 176 [arXiv:1405.7875] [INSPIRE].
21. ATLAS collaboration, Search for a high-mass Higgs boson decaying to a W boson pair in pp collisions at √s=8 TeV with the ATLAS detector, JHEP 01 (2016) 032 [arXiv:1509.00389] [INSPIRE].
22. ATLAS collaboration, Search for an additional, heavy Higgs boson in the H → ZZ decay channel at √s=8 TeV in pp collision data with the ATLAS detector, Eur. Phys. J. C 76 (2016) 45 [arXiv:1507.05930] [INSPIRE].
23. ATLAS collaboration, Search for production of W W/W Z resonances decaying to a lepton, neutrino and jets in pp collisions at √s=8 TeV with the ATLAS detector, Eur. Phys. J. C 75 (2015)209.
24. [Erratum ibid. C 75 (2015) 370] [arXiv:1503.04677] [INSPIRE].
25. ATLAS collaboration, Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at √s=7 TeV with the ATLAS detector, New J. Phys. 16 (2014) 113013 [arXiv:1407.0800] [INSPIRE].
26. T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
27. T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
28. M. Cacciari, G.P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
29. M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
30. D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE].
31. J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness, JHEP 03 (2011) 015 [arXiv:1011.2268] [INSPIRE].
32. A.J. Larkoski, D. Neill and J. Thaler, Jet Shapes with the Broadening Axis, JHEP 04 (2014) 017 [arXiv:1401.2158] [INSPIRE].
33. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P.-A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res. 11 (2010) 3371.
34. X. Glorot, A. Bordes and Y. Bengio, Deep sparse rectifier neural networks, J. Mach. Learn. Res. 15 (2011) 315.
35. D. Scherer, A. Muller and S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, in proceedings of The International Conference on Artificial Neural Networks (ICANN), Thessaloniki, Greece, 15-18 September 2010, Springer.
36. K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, arXiv:1502.01852.
37. F. Chollet, Keras, https://github.com/fchollet/keras, (2015).
38. D.P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980.
39. Y. Nesterov, A method of solving a convex programming problem with convergence rate O(1/sqr(k)), Sov. Math. Dokl. 27 (1983) 372.
40. J. Gallicchio and M.D. Schwartz, Seeing in Color: Jet Superstructure, Phys. Rev. Lett. 105 (2010) 022001 [arXiv:1001.5027] [INSPIRE].
41. ATLAS collaboration, Measurement of colour flow with the jet pull angle in tt¯ events using the ATLAS detector at √s=8 TeV, Phys. Lett. B 750 (2015) 475 [arXiv:1506.05629] [INSPIRE].
42. ATLAS collaboration, Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at √s=8 TeV, Eur. Phys. J. C 76 (2016) 154 [arXiv:1510.05821] [INSPIRE].
43. ATLAS collaboration, Identification of high transverse momentum top quarks in pp collisions at √s=8 TeV with the ATLAS detector, arXiv:1603.03127 [INSPIRE].
44. CMS collaboration, Boosted Top Jet Tagging at CMS, CMS-PAS-JME-13-007.