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Jet-images — deep learning edition

  • Luke de Oliveira
  • Michael Kagan
  • Lester Mackey
  • Benjamin Nachman
  • Ariel Schwartzman
Open Access
Regular Article - Experimental Physics

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. This interplay between physicallymotivated 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.

Keywords

Jet substructure Hadron-Hadron scattering (experiments) 

Notes

Open Access

This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Luke de Oliveira
    • 1
  • Michael Kagan
    • 2
  • Lester Mackey
    • 3
  • Benjamin Nachman
    • 2
  • Ariel Schwartzman
    • 2
  1. 1.Institute for Computational and Mathematical EngineeringStanford UniversityStanfordU.S.A.
  2. 2.SLAC National Accelerator LaboratoryStanford UniversityMenlo ParkU.S.A.
  3. 3.Department of StatisticsStanford UniversityStanfordU.S.A.

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