Title: TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction Show Chinese title

Title: TIGER：一种用于事件重建的拓扑无关分层图网络

Abstract: Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally topology-agnostic. By incorporating only the common underlying structure of sequential two-body decays, our model can reconstruct complex events without process-specific assumptions. This flexible architecture supports multi-task learning, enabling simultaneous event reconstruction and classification. TIGER thus provides a powerful and generalizable tool for physics analysis at the LHC. Show Chinese abstract

Abstract: 在大型强子对撞机（LHC）上进行事件重建，即将观测到的物理对象与其真实来源进行关联，是精确测量和搜索中的核心挑战。 许多现有的机器学习方法解决了这个问题，但它们依赖于单一事件拓扑结构，这限制了它们在现实分析中的适用性，因为在这些分析中存在多个具有不同结构的信号和背景过程。 为了解决这个问题，我们提出了TIGER，一种根本上与拓扑结构无关的新型分层图网络。 通过仅结合顺序二体衰变的共同底层结构，我们的模型可以在不做出过程特定假设的情况下重建复杂事件。 这种灵活的架构支持多任务学习，实现了事件重建和分类的同时进行。 因此，TIGER为LHC上的物理分析提供了一个强大且可推广的工具。