用扩散模型直接从原始数据推导核子内部分子分布，无需预设函数形式。
Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion
- 基于条件扩散模型，直接从事件动量信息映射到TMD分布
- 1000个事件即可可靠估计，且不确定性随数据增加稳步降低
- 适用于实验数据稀缺场景，适合高能物理领域研究者
从半包容深度非弹性散射（SIDIS）数据中提取横动量依赖型部分子分布函数（TMD PDFs）是杰弗逊实验室和未来电子-离子对撞机核结构研究的核心目标。传统方法依赖参数化函数形式与迭代拟合，限制了分布灵活性，且难以量化不确定性。本文提出一种条件扩散模型，可直接从原始SIDIS事件动量信息映射至TMD PDFs，避免显式函数假设。在CLAS12实验条件下模拟的SIDIS数据上测试，模型能有效恢复底层TMD分布，并伴随事件数增加，不确定性持续缩小；即使仅使用1,000个条件事件，仍能给出可靠估计，覆盖当前及未来实验面临的统计受限场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。