Dynamic Radius Jet Clustering Algorithm

TL;DR

This paper introduces a dynamic radius jet clustering algorithm that adapts to local kinematics, improving jet identification in collider experiments, especially for complex scenarios involving heavy particle decays.

Contribution

The paper presents a novel jet clustering algorithm with a variable radius based on local event features, enhancing jet analysis capabilities in collider physics.

Findings

Demonstrated effectiveness on Standard Model processes

Applied successfully to BSM scenario at 13 TeV LHC

Shows improved jet reconstruction accuracy

Abstract

The study of standard QCD jets produced along with fat jets, which may appear as a result of the decay of a heavy particle, has become an essential part of collider studies. Current jet clustering algorithms, which use a fixed radius parameter for the formation of jets from the hadrons of an event, may be inadequate to capture the differing radius features. In this work, we develop an alternative jet clustering algorithm that allows the radius to vary dynamically based on local kinematics and distribution in the $\eta$-$\phi$ plane inside each evolving jet. We present the usefulness of this dynamic radius clustering algorithm through two Standard Model processes, and thereafter illustrate it for a scenario beyond the Standard Model at the 13 TeV LHC.