Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performance on jet tagging benchmarks at the Large Hadron Collider, yet the physical reasoning behind their predictions remains opaque. We present different methods, i.e. perturbation-based (GNNExplainer), Shapley-value-based (GNNShap), and gradient-based (GRADCam); adapted to operate on LundNet's Lund-plane graph representation. Leveraging the fact that each node in the Lund plane corresponds to a physically meaningful parton splitting, we construct Monte Carlo truth explanation masks and introduce a physics-informed evaluation framework that goes beyond standard fidelity metrics. We perform the analysis in three transverse-momentum bins ($\mathrm{p_T} \in [500,700]$, $[800,1000]$, and the inclusive region $[500,1000]$ GeV), revealing how explanation quality and focus shift between non-perturbative and perturbative regimes. We further quantify the correlation between explainer-assigned node importance and classical jet substructure observables -- $N$-subjettiness ratios $τ_{21}$ and $τ_{32}$ and the energy correlation functions -- establishing the degree to which the model has learned known QCD features. We find that overall the weight assigned by explainability methods has a correlation with analytic observables, with expected shift across different phase space regimes, indicating that a trained neural network indeed learns some aspects of jet-substructure moments. Our open-source implementation enables reproducible explainability studies for graph-based jet taggers.

翻译：图神经网络（如ParticleNet）以及基于点云的Transformer网络（如ParticleTransformer）已在大型强子对撞机的喷注标记基准测试中取得最先进性能，但其预测背后的物理推理仍不透明。我们提出了三种不同的方法：基于扰动的GNNExplainer、基于Shapley值的GNNShap和基于梯度的GradCAM，并将其适配至基于LundNet的Lund平面图表示。利用Lund平面中每个节点对应具有物理意义的部分子分裂这一特性，我们构建了蒙卡真值解释掩码，并引入一种超越标准保真度指标的物理信息评估框架。我们在三个横动量区间（$\mathrm{p_T} \in [500,700]$、$[800,1000]$ GeV及包含性区间$[500,1000]$ GeV）内开展分析，揭示了解释质量与关注焦点如何在非微扰和微扰区域间转移。我们进一步量化了解释器分配的节点重要性与经典喷注子结构观测量——$N$-subjettiness比值$\tau_{21}$、$\tau_{32}$以及能量关联函数——之间的相关性，从而确定了模型学习已知量子色动力学特征的程度。我们发现，可解释性方法赋予的权重整体上与解析观测量存在相关性，并在不同相空间区域呈现预期的偏移，表明训练后的神经网络确实学习了喷注子结构矩的某些特征。我们的开源实现为基于图的喷注标记器提供了可复现的可解释性研究方案。