A Refined Variational Framework for Proton Structure: Resolving Spin Dynamics, Calibration, and Deep QCD Observables

We present a refined variational framework for modeling the proton’s internal structure, integrating low-energy observables (mass, charge radius) with high-energy partonic distributions (GPDs, TMDs), spindependent interactions, and energy-momentum tensor diagnostics. The model uses a composite wavefunction with physically motivated basis functions, optimized via analytic gradients and JAX autodifferentiation. Unit calibration enables direct comparison to experimental data, while neutron and baryon extensions validate generalizability. We explicitly define the Hamiltonian, loss function, and regularization terms to avoid circular reasoning and ensure reproducibility. The model achieves 1–7% agreement with experimental values and offers a scalable alternative to lattice QCD.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-24
DOI
https://doi.org/10.5281/zenodo.17196467
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
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article

A Refined Variational Framework for Proton Structure: Resolving Spin Dynamics, Calibration, and Deep QCD Observables

Zenodo (CERN European Organization for Nuclear Research)
Image Processing and 3D Reconstruction
article

A Refined Variational Framework for Proton Structure: Resolving Spin Dynamics, Calibration, and Deep QCD Observables

article en

Abstract

We present a refined variational framework for modeling the proton’s internal structure, integrating low-energy observables (mass, charge radius) with high-energy partonic distributions (GPDs, TMDs), spindependent interactions, and energy-momentum tensor diagnostics. The model uses a composite wavefunction with physically motivated basis functions, optimized via analytic gradients and JAX autodifferentiation. Unit calibration enables direct comparison to experimental data, while neutron and baryon extensions validate generalizability. We explicitly define the Hamiltonian, loss function, and regularization terms to avoid circular reasoning and ensure reproducibility. The model achieves 1–7% agreement with experimental values and offers a scalable alternative to lattice QCD.

Zenodo (CERN European Organization for Nuclear Research)
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Image Processing and 3D Reconstruction
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