Jet SIFT-ing: a new scale-invariant jet clustering algorithm for the substructure era

TL;DR

The paper presents SIFT, a novel scale-invariant jet clustering algorithm that improves substructure resolution for highly boosted decay products, integrating filtering and adaptive criteria for better object identification.

Contribution

Introduction of SIFT, a scale-invariant jet clustering algorithm that enhances substructure analysis without fixed parameters, combining filtering, adaptive measures, and machine learning for improved jet reconstruction.

Findings

Effective at high transverse energies

Improves substructure tagging accuracy

Preserves hard structures through clustering

Abstract

We introduce a new jet clustering algorithm named SIFT (Scale-Invariant Filtered Tree) that maintains the resolution of substructure for collimated decay products at large boosts. The scale-invariant measure combines properties of kT and anti-kT by preferring early association of soft radiation with a resilient hard axis, while avoiding the specification of a fixed cone size. Integrated filtering and variable-radius isolation criteria block assimilation of soft wide-angle radiation and provide a halting condition. Mutually hard structures are preserved to the end of clustering, automatically generating a tree of subjet axis candidates. Excellent object identification and kinematic reconstruction for multi-pronged resonances are realized across more than an order of magnitude in transverse energy. The clustering measure history facilitates high-performance substructure tagging, which we quantify with the aid of supervised machine learning. These properties suggest that SIFT may prove to be a useful tool for the continuing study of jet substructure.