Normalizing Flows to Reconstruct Pseudo-PDFs

Yamil Cahuana Medrano, Kostas Orginos
7/28/2026
hep-latcs.LGhep-ph

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Abstract

We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data. Our framework combines Gaussian Process priors with invertible neural networks to learn a posterior distribution over PDFs consistent with limited Ioffe-time data. We demonstrate that the architecture preserves physical constraints and extrapolation properties.

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Cite this paper

@article{medrano2026normalizing,
  title  = {Normalizing Flows to Reconstruct Pseudo-PDFs},
  author = {Yamil Cahuana Medrano and Kostas Orginos},
  year   = {2026},
  eprint = {2607.25282},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.25282v1}
}

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