Jefferson Lab团队用条件扩散模型直接从散射事件提取TMD PDF，比传统方法更灵活，小样本下也能可靠估计。
研究人员提出条件扩散模型，直接从半包容深度非弹性散射事件中提取横向动量依赖部分子分布函数。该模型在CLAS12运动学条件下评估，能恢复基础TMD并提供信息丰富的不确定性估计。即使仅使用1000个条件事件，模型也能产生可靠估计，这对当前和计划中的实验具有重要意义。
Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion
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.