用物理引导的图注意力模型，从海量背景噪声中精准提取粒子信号
PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
- 构建物理拓扑感知的超图变压器，融合局部与全局注意力机制
- 在极端堆叠条件下，信号能量与质量修正精度超越现有基准方法
- 适合高能物理、粒子探测与机器学习交叉研究者参考
欧洲核子研究中心的高亮度大型强子对撞机（HL-LHC）将产生前所未有的数据集，有望揭示宇宙的基本特性。然而，其发现潜力面临严峻挑战：需从约200次同时发生的堆叠碰撞所造成的巨大背景中提取微弱信号。这种极端噪声严重扭曲了用于精确重建的物理可观测量。为此，我们提出物理引导的超图变压器（PhyGHT），一种混合架构，结合距离感知的局部图注意力与全局自注意力，以模拟质子-质子碰撞中形成的粒子喷注的物理拓扑结构。关键创新在于引入可解释的堆叠抑制门（PSG），一种受物理约束的机制，能在超图聚合前显式学习过滤软噪声。为验证方法，我们发布了一个模拟顶夸克对产生的新数据集，以建模极端堆叠条件。PhyGHT在预测信号能量和质量修正因子方面优于ATLAS与CMS实验的最先进基线方法。通过准确重建顶夸克不变质量，展示了机器学习创新与跨学科合作如何直接推动实验物理前沿的科学发现，并提升HL-LHC的发现潜力。数据集与代码已公开于 https://github.com/rAIson-Lab/PhyGHT。
原文摘要 · Abstract (English)
The High-Luminosity Large Hadron Collider (HL-LHC) at CERN will produce unprecedented datasets capable of revealing fundamental properties of the universe. However, realizing its discovery potential faces a significant challenge: extracting small signal fractions from overwhelming backgrounds dominated by approximately 200 simultaneous pileup collisions. This extreme noise severely distorts the physical observables required for accurate reconstruction. To address this, we introduce the Physics-Guided Hypergraph Transformer (PhyGHT), a hybrid architecture that combines distance-aware local graph attention with global self-attention to mirror the physical topology of particle showers formed in proton-proton collisions. Crucially, we integrate a Pileup Suppression Gate (PSG), an interpretable, physics-constrained mechanism that explicitly learns to filter soft noise prior to hypergraph aggregation. To validate our approach, we release a novel simulated dataset of top-quark pair production to model extreme pileup conditions. PhyGHT outperforms state-of-the-art baselines from the ATLAS and CMS experiments in predicting the signal's energy and mass correction factors. By accurately reconstructing the top quark's invariant mass, we demonstrate how machine learning innovation and interdisciplinary collaboration can directly advance scientific discovery at the frontiers of experimental physics and enhance the HL-LHC's discovery potential. The dataset and code are available at https://github.com/rAIson-Lab/PhyGHT
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