Flavoured jets with exact anti-$k_t$ kinematics and tests of infrared and collinear safety

TL;DR

This paper introduces a new method called Interleaved Flavour Neutralisation (IFN) that extends existing jet clustering algorithms to accurately assign jet flavour while maintaining infrared and collinear safety, with potential applications at the LHC.

Contribution

The paper presents a novel technique, IFN, that integrates flavour treatment into jet clustering algorithms, ensuring exact kinematics and safe flavour assignment at fixed perturbative orders.

Findings

Validated IFN with fixed-order tests for safety.

Identified issues in previous flavoured jet clustering methods.

Explored phenomenological implications at the LHC.

Abstract

We propose extensions of the anti-$k_t$ and Cambridge/Aachen hierarchical jet clustering algorithms that are designed to retain the exact jet kinematics of these algorithms, while providing an infrared-and-collinear-safe definition of jet flavour at any fixed order in perturbation theory. Central to our approach is a new technique called Interleaved Flavour Neutralisation (IFN), whereby the treatment of flavour is integrated with, but distinct from, the kinematic clustering. IFN allows flavour information to be meaningfully accessed at each stage of the clustering sequence, which enables a consistent assignment of flavour both to individual jets and to their substructure. We validate the IFN approach using a dedicated framework for fixed-order tests of infrared and collinear safety, which also reveals unanticipated issues in earlier approaches to flavoured jet clustering. We briefly explore the phenomenological impact of IFN with anti-$k_t$ jets for benchmark tasks at the Large Hadron Collider.