GNN For Muon Particle Momentum estimation

2026-03-03

AI Summary

This work proposes the first application of graph neural networks (GNNs) to improve muon momentum estimation in the CMS experiment, aiming to enhance trigger efficiency and suppress false triggers. By constructing two graph structures tailored to the detector’s geometry and physical characteristics, and incorporating carefully engineered node features, the method effectively captures the intrinsic dependencies within muon trajectory data. Experimental results demonstrate that the proposed GNN approach significantly outperforms baseline models such as TabNet, achieving a substantial reduction in mean absolute error (MAE). These findings underscore the model’s superior capability in modeling complex spatial correlations and highlight the critical influence of node feature dimensionality on overall performance.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Muon Particle Momentum Estimation
• CMS Trigger System
• Data Selection
• Momentum Calculation Accuracy
• False Triggers

Innovation

• Graph Neural Networks
• Muon Momentum Estimation
• CMS Trigger System
• Node Feature Dimension
• Particle Physics