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This is a Quantitative Study study.

October 8, 2025Open Access

Prospects for novel track reconstruction algorithms based on Graph Neural Network models using telescope detector testbed

Key Points

Promising results were observed in link prediction tasks using various graph neural network models, enhancing track reconstruction.
Metrics for edge classification indicate that models using SAGEConv perform comparably to more complex architectures like Graph Transformer.
Comparative analysis was conducted among GNN architectures leveraging SAGEConv, Graph Transformer, and PointNet networks.
The ACTS toolkit is recommended for simulating realistic detector environments, aiding in the development of reconstruction algorithms.

Abstract

Graph Neural Networks (GNNs) marked their presence in track reconstruction a few years ago. Initial studies eventually grew into mature pipelines, performing end-to-end track reconstruction for different detectors. Publications describing those efforts usually present the selected GNN architectures for specific reconstruction steps. Therefore, in this study, we would like to contribute to research on which of the general-purpose GNN architectures are especially promising in link prediction of high-energy physics (HEP) data. For this analysis, we compare three graph neural networks: one leveraging only SAGEConv layers and two additionally using Graph Transformer or PointNet networks. We also present achievable metrics for the simplified edge classification task. In addition, we advocate for the use of the ACTS toolkit as a simulation testbed and present a simple VELO-inspired toy detector.

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