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arXiv · 2609.03646

A Bayesian Pixel Based Approach for Model Independent TMD Reconstruction

Marco Zaccheddu
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Abstract

We introduce a nonparametric pixel-based framework for the Bayesian inference and imaging of transverse momentum dependent (TMD) parton distributions. The methodology integrates TMD evolution within the Collins-Soper-Sterman formalism in a differentiable framework, and leverages generative AI through a hybrid normalizing flow-driven Metropolis-Hastings algorithm for efficient posterior sampling. The framework is validated through multi-scale closure tests of increasing complexity. Using singular value decomposition, we characterize the existence of null TMDs, functional components that remain unconstrained by observables, and demonstrate how multi-scale data break these degeneracies, enabling 3D partonic imaging.

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Marco Zaccheddu. 2026-09-03. A Bayesian Pixel Based Approach for Model Independent TMD Reconstruction. https://arxiv.org/abs/2609.03646

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