论文标题
主题流：用标准化的流拆开夸克和gluon喷气机
TopicFlow: Disentangling quark and gluon jets with normalizing flows
论文作者
论文摘要
夸克和gluon喷气机的纯样品的隔离是强子山脉的关键兴趣。最近的工作已采用主题建模来解散从实验获得的混合样品中的基本分布。但是，当前的实现在依赖数据时并不扩展到高维可观察到的东西。在这项工作中，我们介绍了主题流，这是一种基于标准化流的方法，以从混合数据集中学习夸克和Gluon Jet主题分布。这些网络与基于直方图的方法一样具有性能，但是由于它们是未链接的，因此即使在高维处也是有效的。这些模型也可以被过采样以减轻直方图的统计局限性。作为例子，我们演示了我们的模型如何提高分类器的校准精度。最后，我们讨论如何使用流动可能性来执行异常刺激性夸克/gluon分类。
The isolation of pure samples of quark and gluon jets is of key interest at hadron colliders. Recent work has employed topic modeling to disentangle the underlying distributions in mixed samples obtained from experiments. However, current implementations do not scale to high-dimensional observables as they rely on binning the data. In this work we introduce TopicFlow, a method based on normalizing flows to learn quark and gluon jet topic distributions from mixed datasets. These networks are as performant as the histogram-based approach, but since they are unbinned, they are efficient even in high dimension. The models can also be oversampled to alleviate the statistical limitations of histograms. As an example use case, we demonstrate how our models can improve the calibration accuracy of a classifier. Finally, we discuss how the flow likelihoods can be used to perform outlier-robust quark/gluon classification.
