Jet-images — deep learning edition
Abstract
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.
- Authors:
-
- 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
- Publication Date:
- Research Org.:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- Contributing Org.:
- Stanford Univ., CA (United States)
- OSTI Identifier:
- 1271300
- Grant/Contract Number:
- AC02-76SF00515
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of High Energy Physics (Online)
- Additional Journal Information:
- Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2016; Journal Issue: 7; Journal ID: ISSN 1029-8479
- Publisher:
- Springer Berlin
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 97 MATHEMATICS AND COMPUTING; jet substructure; hadron-hadron scattering (experiments)
Citation Formats
de Oliveira, Luke, Kagan, Michael, Mackey, Lester, Nachman, Benjamin, and Schwartzman, Ariel. Jet-images — deep learning edition. United States: N. p., 2016.
Web. doi:10.1007/JHEP07(2016)069.
de Oliveira, Luke, Kagan, Michael, Mackey, Lester, Nachman, Benjamin, & Schwartzman, Ariel. Jet-images — deep learning edition. United States. https://doi.org/10.1007/JHEP07(2016)069
de Oliveira, Luke, Kagan, Michael, Mackey, Lester, Nachman, Benjamin, and Schwartzman, Ariel. Wed .
"Jet-images — deep learning edition". United States. https://doi.org/10.1007/JHEP07(2016)069. https://www.osti.gov/servlets/purl/1271300.
@article{osti_1271300,
title = {Jet-images — deep learning edition},
author = {de Oliveira, Luke and Kagan, Michael and Mackey, Lester and Nachman, Benjamin and Schwartzman, Ariel},
abstractNote = {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.},
doi = {10.1007/JHEP07(2016)069},
journal = {Journal of High Energy Physics (Online)},
number = 7,
volume = 2016,
place = {United States},
year = {Wed Jul 13 00:00:00 EDT 2016},
month = {Wed Jul 13 00:00:00 EDT 2016}
}
Web of Science
Works referenced in this record:
Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarks
journal, May 2012
- Altheimer, A.; Arora, S.; Asquith, L.
- Journal of Physics G: Nuclear and Particle Physics, Vol. 39, Issue 6
Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012
journal, March 2014
- Altheimer, A.; Arce, A.; Asquith, L.
- The European Physical Journal C, Vol. 74, Issue 3
Towards an understanding of the correlations in jet substructure: Report of BOOST2013, hosted by the University of Arizona, 12th–16th of August 2013
journal, September 2015
- Adams, D.; Arce, A.; Asquith, L.
- The European Physical Journal C, Vol. 75, Issue 9
Jet-images: computer vision inspired techniques for jet tagging
journal, February 2015
- Cogan, Josh; Kagan, Michael; Strauss, Emanuel
- Journal of High Energy Physics, Vol. 2015, Issue 2
Playing tag with ANN: boosted top identification with pattern recognition
journal, July 2015
- Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu
- Journal of High Energy Physics, Vol. 2015, Issue 7
Search for vectorlike charge quarks in proton-proton collisions at
journal, January 2016
- Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.
- Physical Review D, Vol. 93, Issue 1
Search for pair-produced vectorlike quarks in proton-proton collisions at
journal, June 2016
- Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.
- Physical Review D, Vol. 93, Issue 11
Search for narrow high-mass resonances in proton–proton collisions at decaying to a Z and a Higgs boson
journal, September 2015
- Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.
- Physics Letters B, Vol. 748
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
journal, January 2016
- Aad, G.; Abbott, B.; Abdallah, J.
- The European Physical Journal C, Vol. 76, Issue 1
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
journal, May 2015
- Aad, G.; Abbott, B.; Abdallah, J.
- The European Physical Journal C, Vol. 75, Issue 5
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
journal, November 2014
- Aad, G.; Abajyan, T.; Abbott, B.
- New Journal of Physics, Vol. 16, Issue 11
A brief introduction to PYTHIA 8.1
journal, June 2008
- Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter
- Computer Physics Communications, Vol. 178, Issue 11
PYTHIA 6.4 physics and manual
journal, May 2006
- Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter
- Journal of High Energy Physics, Vol. 2006, Issue 05
The anti- k t jet clustering algorithm
journal, April 2008
- Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory
- Journal of High Energy Physics, Vol. 2008, Issue 04
FastJet user manual: (for version 3.0.2)
journal, March 2012
- Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory
- The European Physical Journal C, Vol. 72, Issue 3
Jet trimming
journal, February 2010
- Krohn, David; Thaler, Jesse; Wang, Lian-Tao
- Journal of High Energy Physics, Vol. 2010, Issue 2
Identifying boosted objects with N-subjettiness
journal, March 2011
- Thaler, Jesse; Van Tilburg, Ken
- Journal of High Energy Physics, Vol. 2011, Issue 3
Jet shapes with the broadening axis
journal, April 2014
- Larkoski, Andrew J.; Neill, Duff; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2014, Issue 4
Seeing in Color: Jet Superstructure
journal, July 2010
- Gallicchio, Jason; Schwartz, Matthew D.
- Physical Review Letters, Vol. 105, Issue 2
Measurement of colour flow with the jet pull angle in events using the ATLAS detector at
journal, November 2015
- Aad, G.; Abbott, B.; Abdallah, J.
- Physics Letters B, Vol. 750
Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at $$\sqrt{s} = 8$$ s = 8 TeV
journal, March 2016
- Aad, G.; Abbott, B.; Abdallah, J.
- The European Physical Journal C, Vol. 76, Issue 3
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
text, January 2014
- Collaboration, Atlas; Canelli, M. F.; Al, Et
- IOP Publishing
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
text, January 2014
- Agustoni, Marco; Beck, Hans Peter; Cervelli, Alberto
- Institute of Physics Publishing IOP
The anti-k_t jet clustering algorithm
text, January 2008
- Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory
- arXiv
Seeing in Color: Jet Superstructure
text, January 2010
- Gallicchio, Jason; Schwartz, Matthew D.
- arXiv
Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
text, January 2012
- Altheimer, A.; Arora, S.; Asquith, L.
- arXiv
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
text, January 2014
- Cogan, Josh; Kagan, Michael; Strauss, Emanuel
- arXiv
Works referencing / citing this record:
Jets with electrons from boosted top quarks
journal, January 2020
- Chatterjee, Suman; Godbole, Rohini; Roy, Tuhin S.
- Journal of High Energy Physics, Vol. 2020, Issue 1
Calculating pull for non-singlet jets
journal, December 2019
- Bao, Yunjia; Larkoski, Andrew J.
- Journal of High Energy Physics, Vol. 2019, Issue 12
Quark jet versus gluon jet: fully-connected neural networks with high-level features
journal, June 2019
- Luo, Hui; Luo, Ming-Xing; Wang, Kai
- Science China Physics, Mechanics & Astronomy, Vol. 62, Issue 9
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
journal, September 2017
- de Oliveira, Luke; Paganini, Michela; Nachman, Benjamin
- Computing and Software for Big Science, Vol. 1, Issue 1
Recursive Neural Networks in Quark/Gluon Tagging
journal, June 2018
- Cheng, Taoli
- Computing and Software for Big Science, Vol. 2, Issue 1
Identifying the Relevant Dependencies of the Neural Network Response on Characteristics of the Input Space
journal, September 2018
- Wunsch, Stefan; Friese, Raphael; Wolf, Roger
- Computing and Software for Big Science, Vol. 2, Issue 1
Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC
journal, August 2019
- Nguyen, T. Q.; Weitekamp, D.; Anderson, D.
- Computing and Software for Big Science, Vol. 3, Issue 1
Supervised Deep Learning in High Energy Phenomenology: a Mini Review
journal, August 2019
- Abdughani, Murat; Ren, Jie; Wu, Lei
- Communications in Theoretical Physics, Vol. 71, Issue 8
End-to-End Event Classification of High-Energy Physics Data
journal, September 2018
- Andrews, M.; Paulini, M.; Gleyzer, S.
- Journal of Physics: Conference Series, Vol. 1085
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
journal, September 2018
- Bhimji, Wahid; Farrell, Steven Andrew; Kurth, Thorsten
- Journal of Physics: Conference Series, Vol. 1085
Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach
journal, March 2019
- Ai, P.; Wang, D.; Huang, G.
- Journal of Instrumentation, Vol. 14, Issue 03
Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
journal, July 2019
- Piscopo, Maria Laura; Spannowsky, Michael; Waite, Philip
- Physical Review D, Vol. 100, Issue 1
Deep learning for -parity violating supersymmetry searches at the LHC
journal, October 2018
- Guo, Jun; Li, Jinmian; Li, Tianjun
- Physical Review D, Vol. 98, Issue 7
Performance of top-quark and $$\varvec{W}$$ W -boson tagging with ATLAS in Run 2 of the LHC
journal, April 2019
- Aaboud, M.; Aad, G.; Abbott, B.
- The European Physical Journal C, Vol. 79, Issue 5
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
journal, July 2019
- Qasim, Shah Rukh; Kieseler, Jan; Iiyama, Yutaro
- The European Physical Journal C, Vol. 79, Issue 7
JEDI-net: a jet identification algorithm based on interaction networks
journal, January 2020
- Moreno, Eric A.; Cerri, Olmo; Duarte, Javier M.
- The European Physical Journal C, Vol. 80, Issue 1
Beyond $$M_{t\bar{t}}$$: learning to search for a broad $$t\bar{t}$$ resonance at the LHC
journal, February 2020
- Jung, Sunghoon; Lee, Dongsub; Xie, Ke-Pan
- The European Physical Journal C, Vol. 80, Issue 2
Deep learning at 15PF: supervised and semi-supervised classification for scientific data
conference, January 2017
- Kurth, Thorsten; Smorkalov, Mikhail; Deslippe, Jack
- Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '17
Jet Grooming through Reinforcement Learning
journal, April 2020
- Carrazza, Stefano; Dreyer, Frédéric A.
- Journal of Physics: Conference Series, Vol. 1525
Deep-learned Top Tagging with a Lorentz Layer
text, January 2018
- Butter, Anja; Kasieczka, Gregor; Plehn, Tilman
- ETH Zurich
Automating the construction of jet observables with machine learning
text, January 2019
- Datta, Kaustuv; Larkoski, Andrew; Nachman, Benjamin
- ETH Zurich
The Machine Learning landscape of top taggers
text, January 2019
- Kasieczka, Gregor; Plehn, Tilman; Butter, Anja
- RWTH Aachen University
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
text, January 2019
- Bhattacherjee, Biplob; Mukherjee, Swagata; Sengupta, Rhitaja
- RWTH Aachen University
Deep-learning top taggers or the end of QCD
text, January 2017
- Kasieczka, Gregor; Plehn, Tilman; Russell, Michael
- ETH Zurich
Jet Grooming through Reinforcement Learning
journal, April 2020
- Carrazza, Stefano; Dreyer, Frédéric A.
- Journal of Physics: Conference Series, Vol. 1525, Issue 1
Deep learning in color: towards automated quark/gluon jet discrimination
text, January 2016
- Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.
- arXiv
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
text, January 2017
- de Oliveira, Luke; Paganini, Michela; Nachman, Benjamin
- arXiv
Deep-learning Top Taggers or The End of QCD?
text, January 2017
- Kasieczka, Gregor; Plehn, Tilman; Russell, Michael
- arXiv
QCD-Aware Recursive Neural Networks for Jet Physics
text, January 2017
- Louppe, Gilles; Cho, Kyunghyun; Becot, Cyril
- arXiv
Jet Substructure Studies with CMS Open Data
text, January 2017
- Tripathee, Aashish; Xue, Wei; Larkoski, Andrew
- arXiv
Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
text, January 2017
- Frye, Christopher; Larkoski, Andrew J.; Thaler, Jesse
- arXiv
(Machine) Learning to Do More with Less
text, January 2017
- Cohen, Timothy; Freytsis, Marat; Ostdiek, Bryan
- arXiv
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
preprint, January 2017
- Kurth, Thorsten; Zhang, Jian; Satish, Nadathur
- arXiv
Energy flow polynomials: A complete linear basis for jet substructure
text, January 2017
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- arXiv
Learning to Classify from Impure Samples with High-Dimensional Data
text, January 2018
- Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin
- arXiv
Identifying the relevant dependencies of the neural network response on characteristics of the input space
text, January 2018
- Wunsch, Stefan; Friese, Raphael; Wolf, Roger
- arXiv
Infrared Safety of a Neural-Net Top Tagging Algorithm
text, January 2018
- Choi, Suyong; Lee, Seung J.; Perelstein, Maxim
- arXiv
Topology classification with deep learning to improve real-time event selection at the LHC
text, January 2018
- Nguyen, Thong Q.; Weitekamp, Daniel; Anderson, Dustin
- arXiv
Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
text, January 2018
- Lim, Sung Hak; Nojiri, Mihoko M.
- arXiv
Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images
text, January 2018
- Moore, Liam; Nordström, Karl; Varma, Sreedevi
- arXiv
Transverse Momentum Spectra at Threshold for Groomed Heavy Quark Jets
text, January 2018
- Makris, Yiannis; Vaidya, Varun
- arXiv
Reweighting a parton shower using a neural network: the final-state case
text, January 2018
- Bothmann, Enrico; Del Debbio, Luigi
- arXiv
Energy Flow Networks: Deep Sets for Particle Jets
text, January 2018
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- arXiv
Quark-Gluon Tagging: Machine Learning vs Detector
text, January 2018
- Kasieczka, Gregor; Kiefer, Nicholas; Plehn, Tilman
- arXiv
Automating the Construction of Jet Observables with Machine Learning
text, January 2019
- Datta, Kaustuv; Larkoski, Andrew; Nachman, Benjamin
- arXiv
Jet grooming through reinforcement learning
text, January 2019
- Carrazza, Stefano; Dreyer, Frédéric A.
- arXiv
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
text, January 2019
- Chakraborty, Amit; Lim, Sung Hak; Nojiri, Mihoko M.
- arXiv
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
text, January 2019
- Bhattacherjee, Biplob; Mukherjee, Swagata; Sengupta, Rhitaja
- arXiv
Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar t$ resonance at the LHC
text, January 2019
- Jung, Sunghoon; Lee, Dongsub; Xie, Ke-Pan
- arXiv
CapsNets Continuing the Convolutional Quest
text, January 2019
- Diefenbacher, Sascha; Frost, Hermann; Kasieczka, Gregor
- arXiv
JEDI-net: a jet identification algorithm based on interaction networks
text, January 2019
- Moreno, Eric A.; Cerri, Olmo; Duarte, Javier M.
- arXiv