一种基于贝叶斯像素的模型无关TMD重建方法
A Bayesian Pixel Based Approach for Model Independent TMD Reconstruction
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中文总结 AI 辅助
该研究提出基于贝叶斯像素的非参数化框架，结合生成式AI与特定算法实现TMD分布的高效贝叶斯推断成像，经多尺度闭合测试验证，可打破零TMD简并以实现三维部分子成像。
中文摘要 AI 辅助
我们提出了一种用于横向动量依赖（TMD）部分子分布的贝叶斯推断与成像的非参数化基于像素的框架。该方法在可微框架中整合了Collins-Soper-Sterman形式论内的TMD演化，并通过混合归一化流驱动的Metropolis-Hastings算法利用生成式AI进行高效后验采样。该框架通过复杂度递增的多尺度闭合测试得到验证，我们利用奇异值分解表征了零TMD（不受观测量约束的函数分量）的存在，并证明多尺度数据如何打破这些简并，实现三维部分子成像。
英文摘要
We introduce a nonparametric pixel-based framework for the Bayesian inference and imaging of transverse momentum dependent (TMD) parton distributions. The methodology integrates TMD evolution within the Collins-Soper-Sterman formalism in a differentiable framework, and leverages generative AI through a hybrid normalizing flow-driven Metropolis-Hastings algorithm for efficient posterior sampling. The framework is validated through multi-scale closure tests of increasing complexity. Using singular value decomposition, we characterize the existence of null TMDs, functional components that remain unconstrained by observables, and demonstrate how multi-scale data break these degeneracies, enabling 3D partonic imaging.
