arXiv 2507.220122025-07hep-ph

神经网络提取色电和色磁胶子质量

Neural network extraction of chromo-electric and chromo-magnetic gluon masses

Jie Mei, Lingxiao Wang, Mei Huang

备注：: v2 added the calculation of $η/s$ in appendix

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AI总结：

该研究提出基于双残差网络的准粒子模型，从SU(3)格点热力学数据中提取色电和色磁胶子的温度相关质量，在$T/T_c \u2208 [1,10]$内高精度重现格点结果，揭示了其在$T_c$附近的非微扰动力学差异及高温收敛性。

摘要：

我们提出一种基于神经网络的准粒子模型，以分离色电和色磁胶子的贡献。利用双残差网络，我们从SU(3)格点热力学的压强和迹反常数据中提取温度相关的质量。纳入物理正则化后，训练后的模型在$T/T_c \in [1,10]$范围内高精度重现格点结果，捕捉到$T_c$附近的交叉行为和高温下的线性标度。提取的质量表现出物理合理的行为：在$T_c$附近急剧下降，随后线性增加。我们发现$T_c$附近热质量与屏蔽质量存在显著差异，反映非微扰动力学，而在$T \u2265 2T_c$时两者收敛。

英文摘要：

We present a neural network-based quasi-particle model to separate the contributions of chromo-electric and chromo-magnetic gluons. Using dual residual networks, we extract temperature-dependent masses from SU(3) lattice thermodynamic data of pressure and trace anomaly. After incorporating physics regularizations, the trained models reproduce lattice results with high accuracy over $T/T_c \in [1,10]$, capturing both the crossover behavior near $T_c$ and linear scaling at high temperatures. The extracted masses exhibit a physically reasonable behavior: they decrease sharply around $T_c$ and increase linearly thereafter. We find significant differences between thermal and screening masses near $T_c$, reflecting non-perturbative dynamics, while they converge at $T \gtrsim 2T_c$.

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