Jet-images — deep learning edition

de Oliveira, Luke; Kagan, Michael; Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

doi:10.1007/JHEP07(2016)069

Title: Jet-images — deep learning edition

Journal Article · Wed Jul 13 00:00:00 EDT 2016 · Journal of High Energy Physics (Online)

DOI:https://doi.org/10.1007/JHEP07(2016)069· OSTI ID:1271300

de Oliveira, Luke ^[1]; Kagan, Michael ^[2]; Mackey, Lester ^[3]; Nachman, Benjamin ^[2]; Schwartzman, Ariel ^[2]

Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Stanford Univ., CA (United States). Dept. of Statistics

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

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Research Organization:: SLAC National Accelerator Lab., Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Contributing Organization:: Stanford Univ., CA (United States)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1271300

Journal Information:: Journal of High Energy Physics (Online), Vol. 2016, Issue 7; ISSN 1029-8479

Publisher:: Springer BerlinCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 181 works

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References (27)

Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarks Altheimer, A.; Arora, S.; Asquith, L. Journal of Physics G: Nuclear and Particle Physics, Vol. 39, Issue 6 https://doi.org/10.1088/0954-3899/39/6/063001	journal	May 2012
Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 Altheimer, A.; Arce, A.; Asquith, L. The European Physical Journal C, Vol. 74, Issue 3 https://doi.org/10.1140/epjc/s10052-014-2792-8	journal	March 2014
Towards an understanding of the correlations in jet substructure: Report of BOOST2013, hosted by the University of Arizona, 12th–16th of August 2013 Adams, D.; Arce, A.; Asquith, L. The European Physical Journal C, Vol. 75, Issue 9 https://doi.org/10.1140/epjc/s10052-015-3587-2	journal	September 2015
Jet-images: computer vision inspired techniques for jet tagging Cogan, Josh; Kagan, Michael; Strauss, Emanuel Journal of High Energy Physics, Vol. 2015, Issue 2 https://doi.org/10.1007/JHEP02(2015)118	journal	February 2015
Playing tag with ANN: boosted top identification with pattern recognition Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu Journal of High Energy Physics, Vol. 2015, Issue 7 https://doi.org/10.1007/JHEP07(2015)086	journal	July 2015
Search for vectorlike charge $2 / 3 T$ quarks in proton-proton collisions at $\sqrt{(} s) = 8 TeV$ Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A. Physical Review D, Vol. 93, Issue 1 https://doi.org/10.1103/PhysRevD.93.012003	journal	January 2016
Search for pair-produced vectorlike $B$ quarks in proton-proton collisions at $\sqrt{s} = 8 TeV$ Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A. Physical Review D, Vol. 93, Issue 11 https://doi.org/10.1103/PhysRevD.93.112009	journal	June 2016
Search for narrow high-mass resonances in proton–proton collisions at $\sqrt{s} = 8 TeV$ decaying to a Z and a Higgs boson Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A. Physics Letters B, Vol. 748 https://doi.org/10.1016/j.physletb.2015.07.011	journal	September 2015
Search for an additional, heavy Higgs boson in the $$H\rightarrow ZZ$$ H → Z Z decay channel at $$\sqrt{s} = 8\;\text{ TeV }$$ s = 8 TeV in $$pp$$ p p collision data with the ATLAS detector Aad, G.; Abbott, B.; Abdallah, J. The European Physical Journal C, Vol. 76, Issue 1 https://doi.org/10.1140/epjc/s10052-015-3820-z	journal	January 2016
Search for production of $$WW/WZ$$ W W / W Z resonances decaying to a lepton, neutrino and jets in $$pp$$ p p collisions at $$\sqrt{s}=8$$ s = 8 TeV with the ATLAS detector Aad, G.; Abbott, B.; Abdallah, J. The European Physical Journal C, Vol. 75, Issue 5 https://doi.org/10.1140/epjc/s10052-015-3425-6	journal	May 2015
Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at $\sqrt{s}$ = 7 TeV with the ATLAS detector Aad, G.; Abajyan, T.; Abbott, B. New Journal of Physics, Vol. 16, Issue 11 https://doi.org/10.1088/1367-2630/16/11/113013	journal	November 2014
A brief introduction to PYTHIA 8.1 Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter Computer Physics Communications, Vol. 178, Issue 11 https://doi.org/10.1016/j.cpc.2008.01.036	journal	June 2008
PYTHIA 6.4 physics and manual Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter Journal of High Energy Physics, Vol. 2006, Issue 05 https://doi.org/10.1088/1126-6708/2006/05/026	journal	May 2006
The anti- k _t jet clustering algorithm Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory Journal of High Energy Physics, Vol. 2008, Issue 04 https://doi.org/10.1088/1126-6708/2008/04/063	journal	April 2008
FastJet user manual: (for version 3.0.2) Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory The European Physical Journal C, Vol. 72, Issue 3 https://doi.org/10.1140/epjc/s10052-012-1896-2	journal	March 2012
Jet trimming Krohn, David; Thaler, Jesse; Wang, Lian-Tao Journal of High Energy Physics, Vol. 2010, Issue 2 https://doi.org/10.1007/JHEP02(2010)084	journal	February 2010
Identifying boosted objects with N-subjettiness Thaler, Jesse; Van Tilburg, Ken Journal of High Energy Physics, Vol. 2011, Issue 3 https://doi.org/10.1007/JHEP03(2011)015	journal	March 2011
Jet shapes with the broadening axis Larkoski, Andrew J.; Neill, Duff; Thaler, Jesse Journal of High Energy Physics, Vol. 2014, Issue 4 https://doi.org/10.1007/JHEP04(2014)017	journal	April 2014
Seeing in Color: Jet Superstructure Gallicchio, Jason; Schwartz, Matthew D. Physical Review Letters, Vol. 105, Issue 2 https://doi.org/10.1103/PhysRevLett.105.022001	journal	July 2010
Measurement of colour flow with the jet pull angle in $t \bar{t}$ events using the ATLAS detector at $\sqrt{s} = 8 TeV$ Aad, G.; Abbott, B.; Abdallah, J. Physics Letters B, Vol. 750 https://doi.org/10.1016/j.physletb.2015.09.051	journal	November 2015
Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at $$\sqrt{s} = 8$$ s = 8 TeV Aad, G.; Abbott, B.; Abdallah, J. The European Physical Journal C, Vol. 76, Issue 3 https://doi.org/10.1140/epjc/s10052-016-3978-z	journal	March 2016
Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at $\sqrt{s}$ = 7 TeV with the ATLAS detector Collaboration, Atlas; Canelli, M. F.; Al, Et IOP Publishing https://doi.org/10.5167/uzh-108114	text	January 2014
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 Agustoni, Marco; Beck, Hans Peter; Cervelli, Alberto Institute of Physics Publishing IOP https://doi.org/10.7892/boris.68679	text	January 2014
The anti-k_t jet clustering algorithm Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory arXiv https://doi.org/10.48550/arxiv.0802.1189	text	January 2008
Seeing in Color: Jet Superstructure Gallicchio, Jason; Schwartz, Matthew D. arXiv https://doi.org/10.48550/arxiv.1001.5027	text	January 2010
Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks Altheimer, A.; Arora, S.; Asquith, L. arXiv https://doi.org/10.48550/arxiv.1201.0008	text	January 2012
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging Cogan, Josh; Kagan, Michael; Strauss, Emanuel arXiv https://doi.org/10.48550/arxiv.1407.5675	text	January 2014

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