Identifying boosted objects with N-subjettiness and linear k-means clustering

Abstract

In this thesis, I explore aspects of a new jet shape - N-subjettiness - designed to identify boosted hadronically-decaying objects (with a particular focus on tagging top quarks) at particle accelerators such as the Large Hadron Collider. Combined with an invariant mass cut on jets, N-subjettiness is a powerful discriminating variable for tagging boosted objects such as top quarks and rejecting the fake background of QCD jets with large invariant mass. In a crossover analysis, the N-subjettiness method is found to outperform the common top tagging methods of the BOOST2010 conference, with top tagging efficiencies of 50% and 20% against mistag rates of 4.0% and 0.19%, respectively. The N-subjettiness values are calculated using a new infrared- and collinear-safe minimization procedure which I call the linear k-means clustering algorithm. As a true jet shape with highly effective tagging performances, N-subjettiness has many advantages on the experimental as well as on the theoretical side.