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Investigating top tagging with Ym-Splitter and N-subjettiness

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Summary: In this article, a novel jet tagging method called LundNet, utilizing graph neural networks and an efficient description of the radiation patterns within a jet, has been introduced for optimally disentangling signatures of boosted objects from background events in experimental studies at the Large Hadron Collider. Application of this method to various benchmarks shows significantly improved performance for top tagging compared to existing algorithms. The LundNet taggers demonstrate robustness to non-perturbative and detector effects, along with improved computational complexity and speed compared to previous graph-based taggers.

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