论文标题
用可逆网络测量QCD分裂
Measuring QCD Splittings with Invertible Networks
论文作者
论文摘要
QCD分割是LHC上最基本的理论概念之一。我们展示如何在可逆神经网络的帮助下系统地研究它们。这些网络可与子喷射信息一起工作，以从喷气样品中提取基本参数。我们的方法将QCD Casimirs的LEP测量扩展到基于低级喷气式可观察物的QCD属性的系统测试。从玩具示例开始，我们将详细研究全淋浴，强调和检测器效果的效果。
QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level jet observables. Starting with an toy example we study the effect of the full shower, hadronization, and detector effects in detail.
