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.

• Received 16 April 2019

DOI:https://doi.org/10.1103/PhysRevD.100.056002

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP³.

Published by the American Physical Society