QCD的另一个非正统介绍，现在是机器学习
Another Unorthodox Introduction to QCD and now Machine Learning
摘要：
这些是在费米实验室主办的在线2020年强子对撞机物理暑期学校上提交的讲义。这些是在arXiv：1709.06195的2017年和2018年CTEQ暑期学校中提出的讲座的延伸，仍然介绍了微扰QCD及其应用于射流子结构，从自下而上的角度来看，基于QCD近似为弱耦合，共形场论。随着机器学习成为粒子物理学越来越重要的工具，我从增加人类知识的偏见角度讨论了它的效用。一个简单的论点，夸克与胶子歧视的可能性是红外线和共线安全作为这种方法的一个例子。也提供了期末练习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
