论文标题
对撞机物理的无监督聚类
Unsupervised clustering for collider physics
论文作者
论文摘要
我们提出了一种在名为Ucluster的粒子物理中无监督聚类的新方法，其中神经网络创建的嵌入空间中的信息用于将碰撞事件分类为具有相似属性的不同群集。我们展示了该方法如何应用于无监督的多类分类以及用于新物理搜索的异常检测。
We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar properties. We show how this method can be applied to an unsupervised multiclass classification as well as for anomaly detection, which can be used for new physics searches.
