Jet Substructure Without Trees

TL;DR

This paper introduces a novel jet substructure analysis method using an angular correlation function that does not rely on clustering algorithms, enabling new observables and effective top quark tagging.

Contribution

It presents an alternative approach to jet substructure analysis through an angular correlation function, avoiding clustering algorithms and enabling new observables.

Findings

Effective top quark tagging algorithm developed

Method competitive with existing jet substructure techniques

Provides new jet observables based on angular correlations

Abstract

We present an alternative approach to identifying and characterizing jet substructure. An angular correlation function is introduced that can be used to extract angular and mass scales within a jet without reference to a clustering algorithm. This procedure gives rise to a number of useful jet observables. As an application, we construct a top quark tagging algorithm that is competitive with existing methods.