用混合模型提升底夸克喷注识别精度，显著优化对魅夸克的区分能力。
B-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture
- 融合边卷积与自注意力机制，同时捕捉局部轨迹和全局喷注特征
- 在ATLAS数据上达到0.9333 AUC，优于ParticleNet和纯Transformer模型
- 推理延迟低于0.06毫秒，适合LHC实时筛选需求
喷注味分类在精确测量标准模型中至关重要，有助于提取夸克-喷注相互作用中的质量依赖性及夸克-胶子等离子体（QGP）相互作用特性。它还能推断高能碰撞中产生含重夸克粒子的本质。底夸克喷注分类对探索质子-质子碰撞中的新物理场景尤为关键。本研究提出一种混合深度学习架构——边卷积-变压器（ECT）模型，将边卷积与变压器自注意力机制集成于一体，用于底夸克喷注识别。ECT处理轨迹级特征（如影响参数、动量及其显著性）与喷注级可观测量（顶点信息和运动学量），实现顶尖性能。研究使用ATLAS模拟数据集，结果显示ECT在底夸克与组合魅夸克及轻夸克喷注区分任务中达到0.9333 AUC，超越ParticleNet（0.8904 AUC）和纯变压器基线（0.9216 AUC）。该模型在现代GPU上每喷注推理延迟低于0.060毫秒，满足LHC实时事件选择的严格要求。结果表明，结合局部与全局特征的混合架构在挑战性喷注分类任务中表现更优。该架构在底夸克识别上取得优异成果，尤其在魅夸克排斥方面表现突出，同时保持与纯变压器模型相当的轻夸克区分能力。
原文摘要 · Abstract (English)
Jet flavor tagging plays an important role in precise Standard Model measurement enabling the extraction of mass dependence in jet-quark interaction and quark-gluon plasma (QGP) interactions. They also enable inferring the nature of particles produced in high-energy particle collisions that contain heavy quarks. The classification of bottom jets is vital for exploring new Physics scenarios in proton-proton collisions. In this research, we present a hybrid deep learning architecture that integrates edge convolutions with transformer self-attention mechanisms, into one single architecture called the Edge Convolution Transformer (ECT) model for bottom-quark jet tagging. ECT processes track-level features (impact parameters, momentum, and their significances) alongside jet-level observables (vertex information and kinematics) to achieve state-of-the-art performance. The study utilizes the ATLAS simulation dataset. We demonstrate that ECT achieves 0.9333 AUC for b-jet versus combined charm and light jet discrimination, surpassing ParticleNet (0.8904 AUC) and the pure transformer baseline (0.9216 AUC). The model maintains inference latency below 0.060 ms per jet on modern GPUs, meeting the stringent requirements for real-time event selection at the LHC. Our results demonstrate that hybrid architectures combining local and global features offer superior performance for challenging jet classification tasks. The proposed architecture achieves good results in b-jet tagging, particularly excelling in charm jet rejection (the most challenging task), while maintaining competitive light-jet discrimination comparable to pure transformer models.
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