机器学习的重规范化-组改进的测量器动作和经典完美的梯度流
Kieran Holland1, Andreas Ipp2, David I Müller2
1University of the Pacific, 3601 Pacific Avenue, Stockton, California 95211, USA.
Physical review letters
|February 6, 2026
概括
机器学习有效地描述了格子测量器的动作,使得从量子场理论中提取连续性质的特性. 一个机器学习的动作显著减少了离散的错误,允许从粗格子中精确地提取物理.
科学领域:
- 高能物理 高能物理
- 计算物理 计算物理
- 量子场理论 量子场理论
背景情况:
- 在量子场理论中,从离散的时空中提取连续性质的属性受到格子工件的阻碍.
- 再规范化组 (RG) 改进的格子动作可以保留连续性属性,但难以参数化.
- 机器学习 (ML) 提供了一种有效的方法来描述复杂的格子动作.
研究的目的:
- 为了测试机器学习的RG改进的格子测量器动作,特别是经典的完美固定点 (FP) 动作.
- 评估FP行动在减轻四维SU(3) 标尺理论中的分离化效应的有效性.
- 展示ML在开发量子场理论中改进的格子动作方面的潜力.
主要方法:
- 利用蒙特卡洛模拟来测试SU(3) 标尺理论的经典完美的固定点 (FP) 动作.
- 采用尺度等价卷积神经网络用于基于ML的RG改进的行动的参数化.
- 分析了可观测的梯度流量,以量化离散效应.
主要成果:
- 证实FP动作的梯度流没有树级离散效应对格子间距的所有顺序.
- 在梯度流的可观测物中,分离效应被抑制到不到1%,即使在最大0.14 fm的格子间距上也是如此.
- 在FP行动显示显著改善,使连续物理从粗格子的提取.
结论:
- 机器学习的FP作用在抑制分离化工件方面非常有效,促进了连续物理提取.
- 取得的改进质量验证了FP行动在未来的网格尺度理论研究中使用的价值.
- 基于ML的参数化显示出在晶格尺理论中实现量子完美动作的前景.
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