喷注物理与机器学习的QCD大师班讲座
QCD Masterclass Lectures on Jet Physics and Machine Learning
AI总结：
本讲座系统介绍机器学习在喷注识别与鉴别中的应用，涵盖H→bb̄识别、夸克对胶子鉴别等多种二元判别问题，并提出同时测量强子多重数与喷注电荷时夸克对胶子似然比的参数化新结果。
摘要：
这些讲座于2024年在法国Saint-Jacut-de-la-Mer举行的QCD大师班上讲授。它们在粒子对撞机物理的背景下介绍并回顾了机器学习的基本定理和原理，重点应用于喷注识别与鉴别。详细研究了喷注物理中二元鉴别的众多实例，包括固定阶微扰理论中的$H\ o b\ar b$识别、利用参数化幂次计数方法进行的一般单叉对二叉鉴别，以及通过假设中心极限定理、同位旋守恒和单粒子能量分布的收敛矩展开进行的上夸克对下夸克喷注分类。夸克对胶子喷注鉴别在多种情境下被考虑，从使用加性的、红外与共线安全的观测量，到使用强子多重数，再到包含喷注电荷的测量。虽然这里呈现的许多结果是众所周知的，但也给出了一些新颖的结果，其中最突出的是对同时测量强子多重数和喷注电荷的喷注，夸克对胶子鉴别的似然比的参数化表达式。还提供了讲座结束时的练习。
英文摘要：
These lectures were presented at the 2024 QCD Masterclass in Saint-Jacut-de-la-Mer, France. They introduce and review fundamental theorems and principles of machine learning within the context of collider particle physics, focused on application to jet identification and discrimination. Numerous examples of binary discrimination in jet physics are studied in detail, including $H\to b\bar b$ identification in fixed-order perturbation theory, generic one-versus two-prong discrimination with parametric power counting techniques, and up versus down quark jet classification by assuming the central limit theorem, isospin conservation, and a convergent moment expansion of the single particle energy distribution. Quark versus gluon jet discrimination is considered in multiple contexts, from using additive, infrared and collinear safe observables, to using hadronic multiplicity, and to including measurements of the jet charge. While many of the results presented here are well known, some novel results are presented, the most prominent being a parametrized expression for the likelihood ratio of quark versus gluon discrimination for jets on which hadronic multiplicity and jet charge are simultaneously measured. End-of-lecture exercises are also provided.
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