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arXiv · 2311.10108

Study of topological quantities of lattice QCD with a modified Wasserstein generative adversarial network

Lin Gao·Heping Ying·Jianbo Zhang
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Abstract

We propose a modified Wasserstein generative adversarial network (M-WGAN) to study the distribution of the topological charge in lattice QCD based on Monte Carlo simulations. We construct new generator and discriminator in M-WGAN to support the generation of high-quality distribution. Our results show that the M-WGAN scheme of machine learning should be helpful for us to calculate efficiently the 1D distribution of topological charge compared with the method by the MC simulation alone.

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Lin Gao, Heping Ying, Jianbo Zhang. 2023-11-15. Study of topological quantities of lattice QCD with a modified Wasserstein generative adversarial network. https://doi.org/10.1103/physrevd.109.074509

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