论文标题
比较点云策略的对撞机事件分类
Comparing Point Cloud Strategies for Collider Event Classification
论文作者
论文摘要
在本文中，我们比较了对撞机事件的点云表示定义的几个事件分类体系结构。这些方法基于深度集和边缘卷积的框架，避免了许多与传统功能工程相关的困难。为了根据更传统的事件分类策略进行基准测试，我们进行了一个涉及Higgs玻色子腐烂到Tau Leptons的案例研究。与具有工程功能的基线地图集分析相比，我们发现性能增加了2.5倍。我们的点云体系结构可以看作是图形神经网络的简化版本，其中事件中的每个粒子都对应于图节点。在我们的案例研究中，我们发现基于学习的边缘功能的简单成对架构的性能和计算成本的最佳平衡。
In this paper, we compare several event classification architectures defined on the point cloud representation of collider events. These approaches, which are based on the frameworks of deep sets and edge convolutions, circumvent many of the difficulties associated with traditional feature engineering. To benchmark our architectures against more traditional event classification strategies, we perform a case study involving Higgs boson decays to tau leptons. We find a 2.5 times increase in performance compared to a baseline ATLAS analysis with engineered features. Our point cloud architectures can be viewed as simplified versions of graph neural networks, where each particle in the event corresponds to a graph node. In our case study, we find the best balance of performance and computational cost for simple pairwise architectures, which are based on learned edge features.
