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arXiv · 2607.25282

Normalizing Flows to Reconstruct Pseudo-PDFs

Yamil Cahuana Medrano·Kostas Orginos
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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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Yamil Cahuana Medrano, Kostas Orginos. 2026-07-28. Normalizing Flows to Reconstruct Pseudo-PDFs. https://arxiv.org/abs/2607.25282

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