arXiv 2005.123192020-05-25hep-phhep-ex

学习对撞机事件的潜在结构

Learning the latent structure of collider events

Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik, Manuel Szewc

备注：: 38 pages, 23 figures
DOI：: 10.1007/JHEP10(2020)206

摘要：

我们描述了一种技术，直接从数据中学习对撞机事件的底层结构，而无需考虑特定的理论模型。它允许推断可能导致这种结构的理论模型的各个方面，并可用于对事件进行聚类或分类以供分析。无监督机器学习技术基于潜在狄利克雷分配的概率（贝叶斯）生成模型。我们将模型与称为变分推理的近似推理算法配对，然后使用该算法提取描述碰撞事件的学习基础结构的潜在概率分布。我们提供了一个详细的系统研究的技术，使用两个例子的情况下，学习由QCD背景事件和$t\bar{t}$或假设的$W' \to (ϕ\to WW) W$信号事件的di-jet事件样本的潜在结构。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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