Jet Substructure Without Trees
Martin Jankowiak, Andrew J. Larkoski
TL;DR
To address jet substructure without relying on clustering histories, the paper introduces a clustering-free angular correlation function $\mathcal{G}(R)$ that encodes angular and mass scales from jet constituents. It then derives an angular structure function $\Delta \mathcal{G}(R)$ to identify prominent angular scales $R_*$ and associated partial masses $m_*$, yielding a suite of IRC-safe observables. As an application, it builds a top-quark tagger using these observables, achieving competitive performance against existing methods on BOOST2010 samples. The work suggests broad applicability to QCD studies and boosted-object tagging, and outlines future refinements to the observable set and peak identification approach.
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.
