The Lund Jet Plane

TL;DR

The paper introduces the primary Lund plane as a visual and analytical tool for understanding jet radiation, demonstrating its utility in constraining simulations and improving boosted boson tagging, especially when combined with machine learning.

Contribution

It presents a novel method to create and analyze Lund diagrams for individual jets, linking them with existing observables and exploring their use in machine learning and likelihood-based tagging.

Findings

Lund diagrams can be constructed for individual jets via declustering.

The primary Lund plane effectively constrains Monte Carlo simulations.

It enhances boosted boson tagging performance, especially with machine learning.

Abstract

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 $z_g$ 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.