Jet-images — deep learning edition

Institute of Mathematical Statistics · Stanford University

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…

Citation impact

312
total citations
FWCI
Percentile
References
52
Citations per year

Authors

1

Topics & keywords

Keywords
  • Deep learning
  • Artificial intelligence
  • Jet (fluid)
  • Feature (linguistics)
  • Computer science
  • Detector
  • Sensitivity (control systems)
  • Collimated light
UN Sustainable Development Goals
  • Affordable and clean energy
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