点阵量子色动力学中的机器学习动作参数

P. Shanahan, D. Trewartha, W. Detmold
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引用次数: 56

摘要

强相互作用的数值点阵量子色动力学研究在粒子物理和核物理的许多方面都很重要。这样的研究需要大量的计算资源来进行。许多提出的方法有望提高晶格计算的效率,并通过需要对生成的晶格数据集进行参数回归的多尺度动作匹配方法,访问当前计算难以处理的参数空间区域。研究了机器学习对回归任务的适用性,发现深度神经网络即使在主成分分析等方法失败的情况下也能提供有效的解决方案。晶格QCD数据集所固有的高信息量和复杂对称性要求引入自定义神经网络层,并为进一步发展提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning action parameters in lattice quantum chromodynamics
Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.
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