GNN For Muon Particle Momentum estimation

TL;DR

This paper investigates the application of Graph Neural Networks to improve the accuracy of muon particle momentum estimation in the CMS experiment, demonstrating superior performance over traditional models and emphasizing the importance of node feature dimension.

Contribution

The paper introduces GNN-based methods for muon momentum estimation, outperforming traditional models and highlighting the significance of node feature dimension in GNN efficiency.

Findings

GNN outperforms TabNet in MAE for momentum estimation

Graph construction methods are effective for data representation

Node feature dimension significantly impacts GNN performance

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.