PreprintCC BY-NC-ND 4.0arXiv
A Bayesian Pixel Based Approach for Model Independent TMD Reconstruction
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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BibTeX
@misc{zaccheddu2026bayesian,
title = {{A Bayesian Pixel Based Approach for Model Independent TMD Reconstruction}},
author = {Marco Zaccheddu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.03646},
eprint = {2609.03646},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03646},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Bayesian Pixel Based Approach for Model Independent TMD Reconstruction
AU - Marco Zaccheddu
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.03646
UR - https://arxiv.org/abs/2609.03646
AB - 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.
ER -