  We apply techniques from Bayesian generative statistical modeling to uncover
hidden features in jet substructure observables that discriminate between
different a priori unknown underlying short distance physical processes in
multi-jet events. In particular, we use a mixed membership model known as
Latent Dirichlet Allocation to build a data-driven unsupervised top-quark
tagger and $t\bar t$ event classifier. We compare our proposal to existing
traditional and machine learning approaches to top jet tagging. Finally,
employing a toy vector-scalar boson model as a benchmark, we demonstrate the
potential for discovering New Physics signatures in multi-jet events in a model
independent and unsupervised way.
