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Rediscovering the Standard Model with AI

delete2026-03-30
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OA
AI
A
Aya Abdelhaq
P
Pellegrino Piantadosi *
F
Fernando Quevedo
DOI：10.1007/JHEP03(2026)265delete
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摘要

摘要

En 中文
我们研究了人工智能是否能够利用实验数据和最小的理论输入来复制和整理粒子物理标准模型中已知的结构。通过将无监督机器学习技术——包括数据降维和聚类算法——应用于粒子的固有属性和衰变模式，我们表明，粒子物理中的关键组织特征，如不同相互作用的相对强度以及重子与介子的差异，在得到的数据表示中变得明显。我们还识别了守恒量，如重子数、奇异数和粲数，以及同位旋结构和八重道多重态。我们的分析随后揭示，聚类能够根据相互作用、味对称性以及量子数对粒子进行分离。此外，我们还观察到与重子激发的雷吉轨迹一致的规律。我们的结果表明，机器学习能够从数据中突出标准模型已知的关键结构，这为数据支持的基础物理学发现指明了一条有前景的路径。
AI总结

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Journal of High Energy Physics 封面图
Journal of High Energy Physics
IF:
5.5
论文数:
4.0W
被引数:
13.7W

机构

N
new york university
学者数:
6.2K
论文数: 3.0K
被引数: 1
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