发表机构
Old Dominion University; William & Mary; Thomas Jefferson National Accelerator Facility（奥多明尼昂大学; 威廉玛丽学院; 托马斯杰斐逊国家加速器设施）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出条件扩散模型，直接从原始SIDIS事件运动学映射得到TMD PDFs，在模拟数据上表现良好，少至1000个事件即可给出可靠估计，适用于相关实验。
AI 中文摘要
从半举深非弹性散射（SIDIS）数据中提取横动量依赖部分子分布函数（TMD PDFs）是杰斐逊实验室及未来电子离子对撞机核子结构研究计划的核心目标。传统提取方法依赖参数化函数形式与迭代拟合，这会限制所得分布的灵活性并使不确定性量化变得繁琐。本文提出一种条件扩散模型，该模型可学习将原始SIDIS事件运动学直接映射到TMD PDFs，无需显式函数假设。在CLAS12运动学下的模拟SIDIS数据上评估显示，该模型能恢复潜在TMD并给出随事件统计量增加而稳步收窄的有效不确定性，即使在少至1000个条件事件的统计受限 regime 下也能产生可靠估计，该 regime 与正在进行及计划中的实验直接相关。
英文摘要
Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a central goal of the nucleon structure program at Jefferson Lab and the future Electron-Ion Collider. Traditional extraction methods rely on parameterized functional forms and iterative fitting, which can limit the flexibility of the resulting distributions and make uncertainty quantification cumbersome. We present a conditional diffusion model that learns to map raw SIDIS event kinematics directly to TMD PDFs, bypassing explicit functional assumptions. Evaluated on simulated SIDIS data at CLAS12 kinematics, the model recovers the underlying TMD with informative uncertainties that narrow steadily with increasing event statistics, and produces reliable estimates even with as few as 1,000 conditioning events, a statistics-limited regime directly relevant to ongoing and planned experiments.
CommentsAccepted at Physics and AI at Stanford University (PAI 2026)
