利用深度神经网络研究伪星胶球的质量

arXiv - PHYS - High Energy Physics - Lattice Pub Date : 2024-06-22 DOI:arxiv-2407.12010

Lin Gao

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摘要

在蒙特卡罗模拟的基础上，利用深度神经网络（DNN）来研究格子QCD中假鳞球的质量。为了获得准确而稳定的质量值，我构建了一个新的网络。结果表明，与传统的最小二乘法相比，这个深度神经网络能提供更精确、更稳定的质量估计值。

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Study of the mass of pseudoscalar glueball with a deep neural network

A deep neural network (DNN) is utilized to study the mass of the pseudoscalar glueball in lattice QCD based on Monte Carlo simulations. To obtain an accurate and stable mass value, I constructed a new network. The results show that this DNN provides a more precise and stable mass estimate compared to the traditional least squares method.

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arXiv - PHYS - High Energy Physics - Lattice

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