The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical techniques for effective interpretation. Neural Networks, including Graph Neural Networks, have shown promise in tasks such as event classification and object identification by representing collisions as graphs. However, while Graph Neural Networks excel in predictive accuracy, their "black box" nature often limits their interpretability, making it difficult to trust their decision-making processes. In this paper, we propose a novel approach that combines a Graph Transformer model with Mixture-of-Expert layers to achieve high predictive performance while embedding interpretability into the architecture. By leveraging attention maps and expert specialization, the model offers insights into its internal decision-making, linking predictions to physics-informed features. We evaluate the model on simulated events from the ATLAS experiment, focusing on distinguishing rare Supersymmetric signal events from Standard Model background. Our results highlight that the model achieves competitive classification accuracy while providing interpretable outputs that align with known physics, demonstrating its potential as a robust and transparent tool for high-energy physics data analysis. This approach underscores the importance of explainability in machine learning methods applied to high energy physics, offering a path toward greater trust in AI-driven discoveries.

翻译：欧洲核子研究中心的大型强子对撞机产生来自高能粒子碰撞的海量复杂数据，这需要先进的分析技术以实现有效解读。神经网络，包括图神经网络，通过将碰撞表示为图，已在事件分类和对象识别等任务中展现出潜力。然而，尽管图神经网络在预测准确性方面表现出色，但其“黑箱”特性往往限制了可解释性，使得难以信任其决策过程。本文提出一种新颖方法，将图Transformer模型与专家混合层相结合，以实现高预测性能，同时将可解释性嵌入架构中。通过利用注意力图和专家专业化，该模型能够洞察其内部决策，将预测与基于物理的特征联系起来。我们在ATLAS实验的模拟事件上评估该模型，重点关注从标准模型背景中区分罕见的超对称信号事件。结果表明，该模型在实现有竞争力的分类精度的同时，提供了与已知物理学相符的可解释输出，证明了其作为高能物理数据分析的稳健且透明工具的潜力。这一方法强调了应用于高能物理的机器学习方法中可解释性的重要性，为增强对人工智能驱动发现的信任提供了一条路径。