  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.
