A study of topological quantities of lattice QCD by a modified DCGAN frame

Fuente: arXiv
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Auteurs principaux: Gao, Lin, Ying, Heping, Zhang, Jianbo
Format: Preprint
Publié: 2023
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author Gao, Lin
Ying, Heping
Zhang, Jianbo
author_facet Gao, Lin
Ying, Heping
Zhang, Jianbo
contents 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 the 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 show that the M-DCGAN scheme of the Machine learning should be helpful for us to calculate efficiently the 1D distribution of topological charge and the 4D topological charge density compared with the case by the MC simulation alone.
format Preprint
id arxiv_https___arxiv_org_abs_2312_03023
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle A study of topological quantities of lattice QCD by a modified DCGAN frame
Gao, Lin
Ying, Heping
Zhang, Jianbo
High Energy Physics - Lattice
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 the 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 show that the M-DCGAN scheme of the Machine learning should be helpful for us to calculate efficiently the 1D distribution of topological charge and the 4D topological charge density compared with the case by the MC simulation alone.
title A study of topological quantities of lattice QCD by a modified DCGAN frame
topic High Energy Physics - Lattice
url https://arxiv.org/abs/2312.03023