Speaker
Barry Dillon
(Jozef Stefan Institute)
Description
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 ttbar 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.
Primary authors
Barry Dillon
(Jozef Stefan Institute)
Jernej F. Kamenik
(Jozef Stefan Institute)
Darius Faroughy
(Jozef Stefan Institute)
