标题： 基于机器学习的$SU(2)$暗胶球格点模拟 显示英文标题

标题： Lattice simulation of $SU(2)$ dark glueball with machine learning

摘要： 我们使用格点模拟研究$SU(2)$胶球作为暗物质候选者的质量及散射截面。 我们同时采用原始和改进的$SU(2)$规范作用量，在$3+1$维度下使用多个$\beta$值，并采用传统的蒙特卡罗方法以及基于机器学习技术的流模型来生成格点构型。 计算了具有$J^{PC}=0^{++}$的标量胶球质量和 NBS 波函数。 使用龙格-库塔方法，我们提取胶球相互作用势和散射截面。 根据观测约束，我们得到作为暗物质潜在组分的标量胶球候选者的质量下限。 显示英文摘要

摘要： We study the mass and scattering cross section of $SU(2)$ glueballs as dark matter candidates using lattice simulations. We employ both naive and improved $SU(2)$ gauge actions in $3+1$ dimensions with several $\beta$ values, and adopt both the tranditional Monte Carlo method and the flow-based model based on machine learning techniques to generate lattice configurations. The mass of the scalar glueball with $J^{PC}=0^{++}$ and the NBS wave function are calculated. Using the Runge-Kutta method, we extract the glueball interaction potential and scattering cross section. From the observational constraints, we obtain the lower bound of the mass of scalar glueball candidates as potential components of dark matter.