Classifying the Cosmic-Ray Proton and Light Groups on the LHAASO-KM2A Experiment with the Graph Neural Network

TL;DR

This paper introduces a fused Graph Neural Network model that leverages detector data structured as graphs to improve classification of cosmic-ray proton and light groups in the LHAASO-KM2A experiment, outperforming traditional methods.

Contribution

The paper presents a novel GNN-based approach that enhances cosmic-ray component classification accuracy using graph-structured detector data.

Findings

The GNN model effectively discriminates signal from background.

It outperforms traditional physics-based and CNN-based methods.

The approach works across the entire energy range.

Abstract

Precise measurement about the cosmic-ray (CR) component knee is essential for revealing the mistery of CR's acceleration and propagation mechanism, as well as exploring the new physics. However, classification about the CR components is a tough task especially for the groups with the atomic number close to each other. Realizing that the deep learning has achieved a remarkable breakthrough in many fields, we seek for leveraging this technology to improve the classification performance about the CR Proton and Light groups on the LHAASO-KM2A experiment. In this work, we propose a fused Graph Neural Network model in combination of the KM2A arrays, in which the activated detectors are structured into graphs. We find that the signal and background can be effectively discriminated in this model, and its performance outperforms both the traditional physics-based method and the CNN-based model across the whole energy range.