Hadronic top quark polarimetry with ParticleNet

IF 4.5 2区物理与天体物理 Q1 ASTRONOMY & ASTROPHYSICS

Physics Letters B Pub Date : 2025-02-11 DOI:10.1016/j.physletb.2025.139314

Zhongtian Dong , Dorival Gonçalves , Kyoungchul Kong , Andrew J. Larkoski , Alberto Navarro

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引用次数: 0

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.

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用粒子网进行强子顶夸克偏振测定

顶夸克物理的精确研究是大型强子对撞机项目的基石。通过衰变运动学研究的极化，提供了一种独特的工具，可以仔细检查顶夸克的各种产生模式，并探索潜在的新物理效应。然而，顶夸克最常发生强子衰变，因此对顶夸克极化敏感的衰变产物的明确识别是不可能的。在这篇论文中，我们引入了一种射流风味标记方法，以显著提高强子衰变中的自旋分析能力，超越了以往研究中使用的专属运动学信息。我们提供了风味标签改进的参数估计，并使用图神经网络（GNN）在模拟数据上演示了这一点。我们发现，假设网络的效率为0.5(0.2)，与运动学方法相比，强子衰变中的自旋分析能力可以提高约20%（40%）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physics Letters B 物理-物理：综合

CiteScore

9.10

自引率

6.80%

发文量

647

审稿时长

3 months

期刊介绍： Physics Letters B ensures the rapid publication of important new results in particle physics, nuclear physics and cosmology. Specialized editors are responsible for contributions in experimental nuclear physics, theoretical nuclear physics, experimental high-energy physics, theoretical high-energy physics, and astrophysics.