EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction

• URL: http://arxiv.org/abs/2407.13925v1
• Date: Thu, 18 Jul 2024 22:29:24 GMT
• Title: EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
• Authors: Paolo Calafiura, Jay Chan, Loic Delabrouille, Brandon Wang,
• Abstract summary: We consider a one-shot OC approach that reconstructs particle tracks directly from a set of hits. This approach iteratively updates the graphs and can better facilitate the message passing across each graph. Preliminary studies on the TrackML dataset show better track performance compared to the methods that require a fixed input graph.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks (GNNs) have emerged as a promising approach that can improve scalability. Most of these GNN-based methods, including the edge classification (EC) and the object condensation (OC) approach, require an input graph that needs to be constructed beforehand. In this work, we consider a one-shot OC approach that reconstructs particle tracks directly from a set of hits (point cloud) by recursively applying graph attention networks with an evolving graph structure. This approach iteratively updates the graphs and can better facilitate the message passing across each graph. Preliminary studies on the TrackML dataset show better track performance compared to the methods that require a fixed input graph.

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