2024/07/18 by P. Calafiura, J. Chan, Calafiura, Paolo +5 · 2 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Advanced X-ray and CT Imaging #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2407.13925
openalex publication_date 2024/07/18 · openalex created_date 2024/09/26 · openalex updated_date 2026/07/28
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.