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.

翻译：径迹重建是粒子实验中的关键任务，由于其组合性质，传统上计算成本非常高。近年来，图神经网络（GNNs）作为一种能够提升可扩展性的有前景的方法而兴起。大多数基于GNN的方法，包括边分类（EC）和对象凝聚（OC）方法，都需要预先构建输入图。在本工作中，我们考虑一种单次OC方法，该方法通过递归应用具有演化图结构的图注意力网络，直接从一组击中点（点云）重建粒子径迹。这种方法迭代更新图，并能更好地促进各图间的消息传递。在TrackML数据集上的初步研究表明，与需要固定输入图的方法相比，本方法具有更好的径迹性能。