Jets from Jets: Re-clustering as a tool for large radius jet reconstruction and grooming at the LHC

TL;DR

This paper explores jet re-clustering as a method to optimize large-radius jet reconstruction at the LHC, demonstrating it achieves similar performance to traditional grooming methods without extra calibration, thus enhancing analysis flexibility.

Contribution

It systematically studies and proposes new jet re-clustering configurations, showing they match the performance of groomed jets while simplifying calibration procedures.

Findings

Re-clustered jets match groomed jet mass performance.

Re-clustering eliminates the need for additional large-R calibration.

Optimized re-clustering improves analysis flexibility.

Abstract

Jets with a large radius $R\gtrsim 1$ and grooming algorithms are widely used to fully capture the decay products of boosted heavy particles at the Large Hadron Collider (LHC). Unlike most discriminating variables used in such studies, the jet radius is usually not optimized for specific physics scenarios. This is because every jet configuration must be calibrated, insitu, to account for detector response and other experimental effects. One solution to enhance the availability of large-$R$ jet configurations used by the LHC experiments is {\it jet re-clustering}. Jet re-clustering introduces an intermediate scale $r<R$ at which jets are calibrated and used as the inputs to reconstruct large radius jets. In this paper we systematically study and propose new jet re-clustering configurations and show that re-clustered large radius jets have essentially the same jet mass performance as large radius groomed jets. Jet re-clustering has the benefit that no additional large-R calibration is necessary, allowing the re-clustered large radius parameter to be optimized in the context of specific precision measurements or searches for new physics.