Hadronic Top Quark Polarimetry with ParticleNet

TL;DR

This paper introduces a novel jet flavor tagging method using Graph Neural Networks to enhance top quark polarization measurements in hadronic decays, achieving significant improvements over traditional kinematic approaches.

Contribution

It presents a new flavor tagging technique with GNNs that improves spin analyzing power in hadronic top decays beyond previous methods.

Findings

Flavor tagging improves spin analyzing power by ~20-40%.

GNN-based method outperforms traditional kinematic approaches.

Demonstrated on simulated data with measurable efficiency gains.

Abstract

Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to explore potential new physics effects. However, the top quark most often decays hadronically, for which unambiguous identification of its decay products sensitive to top quark polarization is not possible. In this Letter, we introduce a jet flavor tagging method to significantly improve spin analyzing power in hadronic decays, going beyond exclusive kinematic information employed in previous studies. We provide parametric estimates of the improvement from flavor tagging with any set of measured observables and demonstrate this in practice on simulated data using a Graph Neural Network (GNN). We find that the spin analyzing power in hadronic decays can improve by approximately 20% (40%) compared to the kinematic approach, assuming an efficiency of 0.5 (0.2) for the network.