Jet-Images: Computer Vision Inspired Techniques for Jet Tagging

Abstract

We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as the elements of the image and establish jet-image preprocessing methods. For the jet-image processing step, we develop a discriminant for classifying the jet-images derived using Fisher discriminant analysis. The effectiveness of the technique is shown within the context of identifying boosted hadronic W boson decays with respect to a background of quark- and gluon- initiated jets. Using Monte Carlo simulation, we demonstrate that the performance of this technique introduces additional discriminating power over other substructure approaches, and gives significant insight into the internal structure of jets.

References in corpus (9)

• Herwig++ Physics and Manual
• The Catchment Area of Jets
• Top-tagging: A Method for Identifying Boosted Hadronic Tops
• Energy Correlation Functions for Jet Substructure
• Boosted objects: a probe of beyond the Standard Model physics
• Strategies to Identify Boosted Tops
• Boosted objects and jet substructure at the LHC
• Multivariate discrimination and the Higgs + W/Z search
• Tagging Boosted Ws with Wavelets

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