Measuring QCD Splittings with Invertible Networks

S. Bieringer, A. Butter, Theo Heimel, S. Hoche, Ullrich Kothe, T. Plehn, Stefan T. Radev
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引用次数: 29

Abstract

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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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。这些网络利用子射流信息从射流样本中提取基本参数。我们的方法将QCD卡西米尔的LEP测量扩展到基于低水平射流观测的QCD特性的系统测试。从一个简单的例子开始,我们详细研究了全阵雨、强子化和探测器效应的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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