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神经网络提取非极化横向-动量-依赖分布
Alessandro Bacchetta1,2, Valerio Bertone3, Chiara Bissolotti4
1Università di Pavia, Dipartimento di Fisica, via Bassi 6, I-27100 Pavia, Italy.
Physical review letters
|July 31, 2025
概括
神经网络准确地从Drell-Yan数据中提取非极化夸克的横向动量依赖分布. 与传统方法相比,这种机器学习方法可以更精确地了解子结构.
科学领域:
- 高能物理 高能物理
- 量子色态动力学 量子色态动力学
- 粒子物理学 粒子物理学
背景情况:
- 横向动量依赖分布 (TMD) 描述了夸克在子内部的运动.
- 提取TMD对于理解物质的部分结构至关重要.
- 德雷尔-扬过程为TMD研究提供了有价值的实验数据.
研究的目的:
- 使用Drell-Yan实验数据进行第一个未极化夸克TMD的提取.
- 调查神经网络在参数化TMDs的非扰动性组成部分的有效性.
- 确定机器学习技术的可行性,以探测子结构.
主要方法:
- 使用了Drell-Yan实验数据.
- 利用神经网络进行TMDs的非扰动性参数化.
- 将神经网络性能与传统参数化方法进行比较.
主要成果:
- 成功提取了非极化夸克TMDs.
- 证明神经网络提供了比传统方法更准确的实验数据描述.
- 证实了神经网络在模拟TMD中的卓越性能.
结论:
- 神经网络是用于提取TMD的可行和有效工具.
- 机器学习技术可以更准确地确定子的多维部分结构.
- 这项工作为使用人工智能进行粒子物理学精度研究开辟了新的途径.
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