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 ...
| Authors | Jitao Xu, Christopher Cocuzza, Kevin Braga et al. |
|---|---|
| Primary category | hep-ph |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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