Uncovering latent jet substructure

Not scheduled
1h 15m
32-123 (MIT)

32-123

MIT

https://goo.gl/maps/Wx14Gpe2wRy
Plenary Talk Session

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

Jernej F. Kamenik (Jozef Stefan Institute) Darius Faroughy (Jozef Stefan Institute) Barry Dillon (Jozef Stefan Institute)

Presentation materials

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