利用改进的 DCGAN 框架研究格子 QCD 的拓扑量

IF 3.6 2区物理与天体物理 Q1 PHYSICS, NUCLEAR

中国物理C Pub Date : 2024-05-01 DOI:10.1088/1674-1137/ad2b51

Lin Gao, Heping Ying, Jianbo Zhang

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引用次数: 0

摘要

我们提出了一种改进的深度卷积生成对抗网络（M-DCGAN）框架，用于研究基于蒙特卡洛（MC）模拟的网格 QCD 中的 N 维（ND）拓扑量。我们构建了一种新的缩放结构，包括全连接层，以支持 M-DCGAN 生成高质量的高维图像。我们的结果表明，与单独的 MC 模拟相比，机器学习的 M-DCGAN 方案将有助于更有效地计算拓扑电荷的一维分布和四维拓扑电荷密度。

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Study of the topological quantities of lattice QCD using a modified DCGAN frame

A modified deep convolutional generative adversarial network (M-DCGAN) frame is proposed to study the N-dimensional (ND) topological quantities in lattice QCD based on Monte Carlo (MC) simulations. We construct a new scaling structure including fully connected layers to support the generation of high-quality high-dimensional images for the M-DCGAN. Our results suggest that the M-DCGAN scheme of machine learning will help to more efficiently calculate the 1D distribution of topological charge and the 4D topological charge density compared with MC simulation alone.

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来源期刊

中国物理C 物理-物理：核物理

CiteScore

6.50

自引率

8.30%

发文量

8976

审稿时长

1.3 months

期刊介绍： Chinese Physics C covers the latest developments and achievements in the theory, experiment and applications of: Particle physics; Nuclear physics; Particle and nuclear astrophysics; Cosmology; Accelerator physics. The journal publishes original research papers, letters and reviews. The Letters section covers short reports on the latest important scientific results, published as quickly as possible. Such breakthrough research articles are a high priority for publication. The Editorial Board is composed of about fifty distinguished physicists, who are responsible for the review of submitted papers and who ensure the scientific quality of the journal. The journal has been awarded the Chinese Academy of Sciences ‘Excellent Journal’ award multiple times, and is recognized as one of China''s top one hundred key scientific periodicals by the General Administration of News and Publications.