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TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
Abstract
This work introduces a novel, nonparametric pixel-based framework for the Bayesian inference and imaging of transverse momentum dependent (TMD) parton distributions. The methodology is built upon a fully differentiable framework that integrates TMD evolution with the Collins-Soper-Sterman formalism,
Why This Paper?
This paper represents cutting-edge research in high-energy physics, exploring fundamental particles and interactions. The latest developments in this field push our understanding of the Standard Model and the search for physics beyond it.
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TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
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