Speaker
Dr
Dillon Barry
(Jozef Stefan Institute)
Description
We have developed a framework for performing unsupervised new physics searches using jet substructure in di-jet events, where the likelihood functions are inferred in a data-driven manner. This framework is based on a machine learning algorithm called Latent Dirichlet Allocation, a statistical model initially used for describing the topical structure of documents. In this talk I will present an overview of and results from this recent work. The results will include: a proof-of-principle analysis on boosted final states from top-quark pair-production, a new physics search for a 3TeV W' boson decaying to boosted jets, and results from the LHC Olympics challenge.
Primary authors
Dr
Dillon Barry
(Jozef Stefan Institute)
Jernej F. Kamenik
(Jozef Stefan Institute)
Darius Faroughy
(Jozef Stefan Institute)
Manuel Szewc
(ICAS-UNSAM)
