更多关于喷注物理与机器学习的QCD大师课讲义
More QCD Masterclass Lectures on Jet Physics and Machine Learning
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中文总结 AI 辅助
本讲义补充2026年QCD大师课内容，介绍事件度量、异常检测、缩放定律、分类极限及对称性回归等新主题，基于QCD基本原理推导新结果。
中文摘要 AI 辅助
这些讲义是在法国圣雅克德拉梅尔举办的2026年QCD大师课上呈现的。它们补充了2024年学校发布的笔记，介绍了一些近年来变得流行的新主题。这些主题包括事件度量、异常检测、缩放定律、分类性能的极限，以及尊重时空对称性的回归。所呈现的许多结果是已知的，但一些新结果被推导出来，特别是与异常检测相关的结果。与原始笔记的情况一样，虽然所有这些问题都源于机器学习应用于QCD的动机，但这些笔记中没有讨论机器学习的细节。所有结果都源于应用QCD的基本原理。我以对人文主义物理学的道歉开始这些笔记。
英文摘要
These lectures were presented at the 2026 QCD Masterclass in Saint-Jacut-de-la-mer, France. They supplement the published notes from the 2024 school with presentation of a few new topics that have become popular in recent years. These include event metrics, anomaly detection, scaling laws, the limits of classification performance, and regression that respects spacetime symmetries. Many of the presented results are known, but some new results are derived, especially related to anomaly detection. As was the case in those original notes, while all these problems are motivated from machine learning applied to QCD, no discussion of details of machine learning is presented in these notes. All results follow from application of fundamental principles of QCD. I begin these notes with an apology for humanist physics.
发表机构
- American Physical Society（美国物理学会）
机构由 AI 辅助整理，请以论文原文为准。
