论文标题
QCD和现在的机器学习的另一个非正统介绍
Another Unorthodox Introduction to QCD and now Machine Learning
论文作者
论文摘要
这些是在Fermilab举办的在线2020 HADRON Collider Physics Summer School上发表的讲义。这些是ARXIV的2017年和2018年CTEQ夏季学校发表的讲座的扩展：1709.06195，仍然从自下而上的角度引入了扰动QCD及其应用于喷气子结构，基于QCD的近似值，作为一种弱耦合的象形现场理论。随着机器学习成为粒子物理的越来越重要的工具，我专门从越来越多的人类知识来讨论其效用。一个简单的论点，即红外歧视的可能性与Gluon歧视是红外的，并以这种方法为例。还提供了结束练习。
These are lecture notes presented at the online 2020 Hadron Collider Physics Summer School hosted by Fermilab. These are an extension of lectures presented at the 2017 and 2018 CTEQ summer schools in arXiv:1709.06195 and still introduces perturbative QCD and its application to jet substructure from a bottom-up perspective based on the approximation of QCD as a weakly-coupled, conformal field theory. With machine learning becoming an increasingly important tool of particle physics, I discuss its utility exclusively from the biased view for increasing human knowledge. A simple argument that the likelihood for quark versus gluon discrimination is infrared and collinear safe is presented as an example of this approach. End-of-lecture exercises are also provided.
