用可逆网络测量QCD分裂
Measuring QCD Splittings with Invertible Networks
摘要：
QCD分裂是LHC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。这些网络与子射流信息一起工作，以从射流样本中提取基本参数。我们的方法扩展了LEP测量QCD Casimirs的系统测试QCD性能的基础上，低层喷流观测。从一个玩具的例子开始，我们详细研究了全簇射、强子化和探测器效应的影响。
英文摘要：
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.
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