标题： Transformer模型在喷注标记中的解释 显示英文标题

标题： Interpreting Transformers for Jet Tagging

摘要： 机器学习（ML）算法，特别是基于注意力机制的变压器模型，已成为分析欧洲核子研究中心大型强子对撞机（CERN LHC）上的 ATLAS 和 CMS 等粒子物理实验产生的海量数据不可或缺的工具。 粒子变换器（ParT）是一种最先进的模型，利用粒子级注意力来改进喷注标记任务，这对于识别由质子碰撞产生的粒子至关重要。 本研究专注于通过分析注意力热图和粒子对在$\eta$-$\phi$平面上的关联来解读 ParT，揭示了一种二元注意力模式，其中每个粒子最多关注另一个粒子。 同时，我们观察到 ParT 根据衰变对重要粒子和子喷注的注意力有所不同，这表明该模型学会了传统的喷注子结构可观测量。 这些见解增强了我们对模型内部工作原理和学习过程的理解，为未来提高高能物理应用中的变压器架构效率提供了潜在途径。 显示英文摘要

摘要： Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging tasks, which are critical for identifying particles resulting from proton collisions. This study focuses on interpreting ParT by analyzing attention heat maps and particle-pair correlations on the $\eta$-$\phi$ plane, revealing a binary attention pattern where each particle attends to at most one other particle. At the same time, we observe that ParT shows varying focus on important particles and subjets depending on decay, indicating that the model learns traditional jet substructure observables. These insights enhance our understanding of the model's internal workings and learning process, offering potential avenues for improving the efficiency of transformer architectures in future high-energy physics applications.