Normalizing Flows to Reconstruct Pseudo-PDFs

• URL: http://arxiv.org/abs/2607.25282v1
• Date: Tue, 28 Jul 2026 04:40:25 GMT
• Title: Normalizing Flows to Reconstruct Pseudo-PDFs
• Abstract summary: We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data.<n>Our framework combines Gaussian Process priors with invertible neural networks to learn a posterior distribution over PDFs consistent with limited Ioffe-time data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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