AI 中文总结
本文开发两阶段机器学习方法，在全强子顶夸克对事例中实现下型喷注鉴别，提升自旋分析因子等指标，验证该通道可保留自旋关联信息。
AI 中文摘要
全强子顶夸克对（$t\ar{t}$）事例具有最大的分支比，且下型夸克可作为近最大自旋分析器（$\beta_d\backsimeq1$），但由于在大量子色动力学（QCD）多喷注本底存在下，需从复杂的喷注部分子重建中提取下型喷注，这类事例极少被用于自旋关联与纠缠研究。本文针对该通道开发了两阶段机器学习重建方法：第一阶段为图神经网络（GNN）+Transformer网络，将所有重建喷注分配为合法的六喷注顶夸克对候选；第二阶段为混合分类器，用于区分两个强子W玻色子衰变内部的四种下型/上型假设。第二阶段结合了分配评分器与辅助条件扩散头；研究发现，仅当采用间隔排序目标训练时，扩散分配评分才能对假设进行排序，而标准去噪目标会使其停留在随机基线水平，且两种目标基本解耦。本文采用重建与自旋可观测量，针对校准后的QCD本底与不匹配的$t\bar{t}$分量对该方法进行评估。与仅使用随机下型/上型标签的ST1基准相比，学习得到的ST2分类器在固定事例选择下，同时提升了有效自旋分析因子与纯度加权六部分子精确分数。对标准自旋关联系数D的直接检验进一步表明，机器学习重建值在重采样范围内与真值水平值相容。这些结果表明，下型/上型鉴别可在全强子通道中保留有用的自旋关联信息。
英文摘要
Fully hadronic $t\bar t$ events carry the largest branching fraction and provide the down-type quark as a near-maximal spin analyzer ($β_d\simeq1$), yet they are rarely used for spin-correlation and entanglement studies because the down-type jets must be extracted from a difficult jet-to-parton reconstruction in the presence of a large QCD multijet background. We develop a two-stage machine-learning reconstruction for this channel: a GNN+Transformer network assigns all reconstructed jets into a legal six-jet top-pair candidate, and a second hybrid classifier resolves the four down/up hypotheses inside the two hadronic $W$ decays. The second stage combines an assignment scorer with an auxiliary conditional diffusion head; we find that the diffusion assignment score can rank hypotheses only when trained with a margin ranking objective, while standard denoising objectives leave it at the random baseline, and the two objectives are largely decoupled. We evaluate the method against a calibrated QCD background and an unmatched $t\bar t$ component using both reconstruction and spin observables. Compared with a ST1 only benchmark with random down/up labels, the learned ST2 classifier improves both the effective spin-analysis factor and the purity-weighted six-parton exact fraction at fixed event selection. A direct check of the standard spin-correlation coefficient $D$ further gives an ML-reconstructed value compatible with the truth-level value within the resampled spread. These results show that down/up identification can retain useful spin-correlation information in the all-hadronic channel.
Comments31 pages, 7 figures
