  JUNIPR is an approach to unsupervised learning in particle physics that
scaffolds a probabilistic model for jets around their representation as binary
trees. Separate JUNIPR models can be learned for different event or jet types,
then compared and explored for physical insight. The relative probabilities can
also be used for discrimination. In this paper, we show how the training of the
separate models can be refined in the context of classification to optimize
discrimination power. We refer to this refined approach as Binary JUNIPR.
Binary JUNIPR achieves state-of-the-art performance for quark/gluon
discrimination and top-tagging. The trained models can then be analyzed to
provide physical insight into how the classification is achieved. As examples,
we explore differences between quark and gluon jets and between gluon jets
generated with two different simulations.
