论文标题
学习对撞机事件的潜在结构
Learning the latent structure of collider events
论文作者
论文摘要
我们描述了一种直接从数据中学习对撞机事件的基础结构的技术，而无需牢记特定的理论模型。它允许推断可能引起该结构的理论模型的各个方面，并可以用于将事件聚类或分类用于分析目的。无监督的机器学习技术基于潜在的Dirichlet分配的概率（贝叶斯）生成模型。我们将模型与称为变异推理的近似推理算法配对，然后我们用它来提取描述对撞机事件的潜在基础结构的潜在概率分布。我们使用两个示例场景对技术进行了详细的系统研究，以了解由QCD背景事件组成的DI-JET事件样本的潜在结构，以及$ t \ bar {t} $或假设的$ W'\ to（ϕ \ to WW）W $信号事件。
We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either $t\bar{t}$ or hypothetical $W' \to (ϕ\to WW) W$ signal events.
