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Normalizing Flows to Reconstruct Pseudo-PDFs

arXiv 2607.25282 ↗First posted 28 Jul 2026hep-lat cs.LG hep-phcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 28 Jul 2026.

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References · 6

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  4. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 242
  5. 2017Machine Learning of Linear Differential Equations using Gaussian Processescited by 140
  6. 2025Quantization Of Probability Measures In Maximum~Mean~Discrepancy Distancecited by 2

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