利用ParticleNet实现强子衰变顶夸克自旋测量
Hadronic Top Quark Polarimetry with ParticleNet
AI总结：
本文提出利用喷注味标记结合图神经网络，显著提升强子衰变顶夸克的自旋分析能力，相比纯运动学方法可提高约20%至40%的效率。
摘要：
顶夸克物理的精密研究是大型强子对撞机项目的基石。通过衰变运动学探测的极化，为审视顶夸克在各种产生模式下的性质以及探索潜在的新物理效应提供了独特工具。然而，顶夸克最常发生强子衰变，在此情况下无法明确识别对其极化敏感的衰变产物。在本报告中，我们引入一种喷注味标记方法，显著提高强子衰变中的自旋分析能力，超越以往研究中使用的排他性运动学信息。我们提供了在任何一组可观测量的情况下，味标记改进的参数化估计，并使用图神经网络（GNN）在模拟数据上进行了实际演示。我们发现，与运动学方法相比，假设网络效率为0.5（0.2）时，强子衰变中的自旋分析能力可提高约20%（40%）。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
