用图神经网络提升粒子动量估算精度，助力高能物理触发系统优化
GNN For Muon Particle Momentum estimation
- 构建两种图结构，利用GNN捕捉粒子探测数据中的复杂关联
- 相比传统模型，MAE降低显著，证明GNN在动量估算上更有效
- 节点特征维度对GNN性能影响大，是关键设计参数
由于整体数据生成速率远高于感兴趣事件的生成速率，大型强子对撞机上的CMS实验采用软硬件结合的触发系统来筛选数据。精确计算粒子动量对于提升CMS触发系统的效率至关重要，有助于更好区分低动量与高动量粒子，并减少误触发。本文探索使用图神经网络（GNN）进行动量估计任务。提出两种图构建方法，并应用GNN模型以利用数据内在的图结构。首先，结果显示GNN在平均绝对误差（MAE）上优于传统模型如TabNet，证明其在捕捉数据复杂依赖关系方面的有效性。其次，结果表明节点特征维度对GNN效率具有决定性影响。
原文摘要 · Abstract (English)
Due to a high rate of overall data generation relative to data generation of interest, the CMS experiment at the Large Hadron Collider uses a combination of hardware- and software-based triggers to select data for capture. Accurate momentum calculation is crucial for improving the efficiency of the CMS trigger systems, enabling better classification of low- and high- momentum particles and reducing false triggers. This paper explores the use of Graph Neural Networks (GNNs) for the momentum estimation task. We present two graph construction methods and apply a GNN model to leverage the inherent graph structure of the data. In this paper firstly, we show that the GNN outperforms traditional models like TabNet in terms of Mean Absolute Error (MAE), demonstrating its effectiveness in capturing complex dependencies within the data. Secondly we show that the dimension of the node feature is crucial for the efficiency of GNN.
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