Lund diagrams, a representation of the phase space within jets,
have long been used in discussing parton showers and
resummations. We point out here that they can also serve as a
powerful tool for experimentally characterising the radiation
pattern within jets. We briefly comment on some of their
analytical properties and highlight their scope for constraining
Monte Carlo simulations. We then examine the use of the Lund
plane for boosted electroweak boson tagging. When used as an
input to deep-learning methods it yields high performance.
Furthermore, much of that performance can be reproduced by using
the Lund plane as an input to simpler log-likelihood type
discriminators. This suggests a potential for unique insight and
experimental validation of the features being used by
machine-learning approaches. In the context of our discussion, we
also highlight the importance of accounting for detector effects
when considering the performance of machine-learning approaches.