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Title: 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:
 [1];  [2];  [3];  [2];  [2]
  1. Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering
  2. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  3. 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. doi: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. doi: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 = {2016},
month = {7}
}

Journal Article:
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Cited by: 13 works
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Works referenced in this record:

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    Works referencing / citing this record:

    Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
    journal, September 2017

    • de Oliveira, Luke; Paganini, Michela; Nachman, Benjamin
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    Quark jet versus gluon jet: fully-connected neural networks with high-level features
    journal, June 2019

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    • Science China Physics, Mechanics & Astronomy, Vol. 62, Issue 9
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    Performance of top-quark and $$\varvec{W}$$ W -boson tagging with ATLAS in Run 2 of the LHC
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    Learning representations of irregular particle-detector geometry with distance-weighted graph networks
    journal, July 2019


    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
    • DOI: 10.1007/s11433-019-9390-8

    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
    • DOI: 10.1007/s41781-017-0004-6

    Deep learning for R -parity violating supersymmetry searches at the LHC
    journal, October 2018


    Performance of top-quark and $$\varvec{W}$$ W -boson tagging with ATLAS in Run 2 of the LHC
    journal, April 2019


    Learning representations of irregular particle-detector geometry with distance-weighted graph networks
    journal, July 2019