Machine learning techniques are fast becoming an ubiquitous tool for data analysis in all aspects of everyday life, from business to society, and high energy physics is no exception. The EPP group are employing advanced state-of-the art techniques to tackle some of the most challenging problems in the field.
Lily and Brett are studying the effects of implementing a Deep Learning approach in NOvA for measuring the [containment of muon tracks] in order to improve the muon neutrino energy measurement.
Lily and Josh are exploring potential areas where ML techniques could be used for improved energy reconstruction for NovA
Emma, Lily, and Dan use a form of machine learning called Boosted Decision Trees (BDTs) in their analysis of the Higgs boson formed with a pair of top quarks. These BDTs are used in order to distinguish signal events from background events, as well as for the identification of jets containing b hadrons.