探测小x区域的致密核物质：一种全局分析框架的工作流程
Probing Dense Nuclear Matter at Small-x: A workflow for a global analysis framework
AI总结：
该研究针对偶极-核散射振幅演化的计算挑战，提出用机器学习模型替代传统数值演化，以实现低成本的全局分析，可更严格探测小x区域致密核物质结构。
AI中文摘要：
偶极模型为描述高能核相互作用提供了强大的框架，尤其适用于致密胶子物质区域。然而，准确演化偶极-核散射振幅仍是一项重大计算挑战，因为它受限于非线性QCD演化方程。为解决此问题，我们研究一种机器学习（ML）模型作为传统数值演化的高效替代。这些基于ML的近似方法大幅降低了全局分析的计算成本，同时保持描述广泛实验数据所需的精度。我们系统评估了ML结果在精度、计算效率以及捕捉核环境中偶极演化关键特征方面的表现。这些计算进展将支持在偶极模型和部分子模型框架内对不同数据集进行全局分析，从而更严格地探测致密区域的核结构。在共同拟合框架内比较两种描述可对胶子分布提供精确约束，深化我们对核的夸克与胶子结构，尤其是小x区域的理解。
英文摘要：
The dipole model provides a powerful framework for describing high-energy nuclear interactions, particularly in the regime of dense gluonic matter. However, accurately evolving the dipole--nucleus scattering amplitude remains a major computational challenge because it is governed by nonlinear QCD evolution equations. To address this, we investigate a machine learning (ML) model as an efficient surrogate for the conventional numerical evolution. These ML-based approximations dramatically reduce the computational cost of global analyses while maintaining the accuracy required to describe a broad range of experimental data. We systematically evaluate the ML results for accuracy, computational efficiency, and ability to capture essential features of dipole evolution in nuclear environments. These computational advancements will enable global analyses of diverse datasets within both the dipole and parton model frameworks, providing a more rigorous probe of nuclear structure in the dense regime. Comparing both descriptions within a common fitting framework can provide precise constraints on the gluon distributions and advance our understanding of the quark and gluon structure of nuclei, particularly in the small-x region.
