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The identification of boosted heavy particles such as top quarks or vectorbosons is one of the key problems arising in experimental studies at the LargeHadron Collider. In this article, we introduce LundNet, a novel jet taggingmethod which relies on graph neural networks and an efficient description ofthe radiation patterns within a jet to optimally disentangle signatures ofboosted objects from background events. We apply this framework to a number ofdifferent benchmarks, showing significantly improved performance for toptagging compared to existing state-of-the-art algorithms. We study therobustness of the LundNet taggers to non-perturbative and detector effects, andshow how kinematic cuts in the Lund plane can mitigate overfitting of theneural network to model-dependent contributions. Finally, we consider thecomputational complexity of this method and its scaling as a function ofkinematic Lund plane cuts, showing an order of magnitude improvement in speedover previous graph-based taggers.

Jet tagging in the Lund plane with graph networks