Uncovering latent jet substructure

Abstract

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 multijet events. In particular, we use a mixed membership model known as latent Dirichlet allocation to build a data-driven unsupervised top-quark tagger and $t\overline{t}$ 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 multijet events in a model independent and unsupervised way.