The Lund jet plane

By Frédéric A. Dreyer, G. Salam, G. Soyez. Journal of High Energy Physics, 2018

161 citations recorded by the source corpus.

Lund diagrams, a theoretical representation of the phase space within jets, have long been used in discussing parton showers and resummations. We point out that they can be created for individual jets through repeated Cambridge/Aachen declustering, providing a powerful visual representation of the radiation within any given jet. Concentrating here on the primary Lund plane, we outline some of its analytical properties, highlight its scope for constraining Monte Carlo simulations and comment on its relation with existing observables such as the zg variable and the iterated soft-drop multiplicity. We then examine its use for boosted electroweak boson tagging at high momenta. It provides good performance when used as an input to machine learning. Much of this performance can be reproduced also within a transparent log-likelihood method, whose underlying assumption is that different regions of the primary Lund plane are largely decorrelated. This suggests a potential for unique insight and experimental validation of the features being used by machine-learning approaches.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/886925b9ba503646c567e8373d76e05b08f1c2af

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In Nuclear and High Energy Physics

Citations in the corpus

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “[539] described an innovative alternative scheme where the jet clustering history is used to build an image that mimics the QCD splitting function.”
Energy flow networks: deep sets for particle jets (2018, methodology): “round 2 by considering a uniform pixelization in (ln R ;’), where is the distance from the jet axis and ’is the azimuthal angle around the jet axis (not to be confused with ˚). As discussed in Ref. [13], this is the natural emission space of the jet. Translating an area element from this natural 10N”
Looking Inside Jets (2019, methodology): “l representation convenient for resummation calculations. It has actually been realised recently that, in the context of jet substructure, it was possible to promote this idea to a genuine observable [65]. In practice, one reclusters the constituents of the jet with the Cambridge/Aachen algorithm an”