Maximizing Boosted Top Identification by Minimizing N-subjettiness

TL;DR

This paper enhances N-subjettiness, a jet shape variable for identifying boosted top quarks, by optimizing subjet axes with a new clustering algorithm, leading to improved tagging efficiency and potential for broader event shape analysis.

Contribution

It introduces a minimization method for N-subjettiness using a novel k-means variant, significantly improving boosted top tagging performance and enabling extensions to event-level shape analysis.

Findings

20% tagging efficiency at 0.23% fake rate for boosted tops

Preferred jet broadening measure for top searches

Additional improvements with multivariate techniques

Abstract

N-subjettiness is a jet shape designed to identify boosted hadronic objects such as top quarks. Given N subjet axes within a jet, N-subjettiness sums the angular distances of jet constituents to their nearest subjet axis. Here, we generalize and improve on N-subjettiness by minimizing over all possible subjet directions, using a new variant of the k-means clustering algorithm. On boosted top benchmark samples from the BOOST2010 workshop, we demonstrate that a simple cut on the 3-subjettiness to 2-subjettiness ratio yields 20% (50%) tagging efficiency for a 0.23% (4.1%) fake rate, making N-subjettiness a highly effective boosted top tagger. N-subjettiness can be modified by adjusting an angular weighting exponent, and we find that the jet broadening measure is preferred for boosted top searches. We also explore multivariate techniques, and show that additional improvements are possible using a modified Fisher discriminant. Finally, we briefly mention how our minimization procedure can be extended to the entire event, allowing the event shape N-jettiness to act as a fixed N cone jet algorithm.