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Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
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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.
Forward citations
Cited by 2 Pith papers
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Dynamics of Hot QCD Matter 2024 -- Hard Probes
Proceedings volume summarizing hard probe studies of quark-gluon plasma, with preliminary new results on non-Markovian quarkonium evolution, jet transport simulations, and machine learning taggers.
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Transformer networks for Heavy flavor jet tagging
A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.
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