Normalizing Flows to Reconstruct Pseudo-PDFs

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.

High Energy Physics - Lattice (hep-lat); Machine Learning (cs.LG); High Energy Physics - Phenomenology (hep-ph)

arXiv:2607.25282 [hep-lat]

(or arXiv:2607.25282v1 [hep-lat] for this version)

https://doi.org/10.48550/arXiv.2607.25282 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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