Speaker
Description
The extraction of Transverse Momentum Dependent distributions (TMDs) from experimental observables represents a classic inverse problem in hadronic physics. Traditionally, this challenge is addressed by assuming specific analytical functional forms, which can introduce inherent parameterization bias and limit the exploration of the full epistemic uncertainty.
In this talk, we present a novel "Pixel-Based" approach that conceptualizes TMD imaging as a discrete image reconstruction task. By formulating the TMD convolutions through discrete tensor algebra, we treat each pixel in the impact parameter ($b_T$) space as a stochastic variable. This framework allows us to perform a non-parametric reconstruction of the TMDs within a robust Bayesian inference scheme. To efficiently sample the high-dimensional posterior distribution of the pixels, we employ modern Machine Learning techniques, specifically Normalizing Flows.
We focus on a systematic study of the resolution limits and stability of this inversion process. By analyzing the resolution matrix in $b_T$-space, we quantify how different kinematic coverages and experimental precision constrain the underlying 3D structure. Furthermore, we provide a comparative analysis between standard analytical parameterizations and our pixel-based approach to quantify the systematic bias introduced by fixed functional forms. This work serves as a proof-of-concept for a low-bias imaging framework, establishing a robust foundation for model-independent extractions of nucleon structure.
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