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.

中文翻译：

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用可逆网络测量 QCD 分裂

QCD 分裂是 LHC 中最基本的理论概念之一。我们展示了如何在可逆神经网络的帮助下系统地研究它们。这些网络使用子喷射信息从喷射样本中提取基本参数。我们的方法将 QCD Casimirs 的 LEP 测量扩展到基于低水平射流可观测的 QCD 特性的系统测试。从一个玩具示例开始，我们详细研究了完整淋浴、强子化和探测器效应的影响。