most citedReconstruction of Gravitational Form Factors using Generative Machine Learning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

nucl-th2026

Generative artificial intelligence for reconstructing neutron-star matter

Julia Yu. Panteleeva, Herzallah Alharazin, Evgeny Epelbaum

Neutron-star cores hold the only known matter in the universe that is simultaneously cold and strongly interacting, compressed beyond nuclear density into a state of unknown compos…

hep-ph20261 cited

Reconstruction of Gravitational Form Factors using Generative Machine Learning

Herzallah Alharazin, Julia Yu. Panteleeva

We develop a generative framework based on denoising diffusion for the model-independent reconstruction of hadronic form factors from sparse and noisy data. The generative prior is…

hep-ph2026

On the definition of the nucleon axial charge density

J. Yu. Panteleeva, E. Epelbaum, J. Gegelia +1

We work out the spatial density distributions corresponding to the axial-vector charge density operator for spin-1/2 systems using states described by sharply localized wave packet…

hep-lat2026

Diffusion Models for SU(2) Lattice Gauge Theory in Two Dimensions

H. Alharazin, J. Yu. Panteleeva, B. -D. Sun

We apply score-based diffusion models to two-dimensional SU(2) lattice pure gauge theory with the Wilson action, extending recent work on U(1) gauge theories. The SU(2) manifold st…

hep-ph2026

Gravitational form factors of the -boson

P. Beißner, J. Yu. Panteleeva, B. -D. Sun +2

Matrix elements of the energy-momentum tensor for one-particle states of the -boson are parameterized in terms of gravitational form factors. One-loop order electroweak correcti…

nucl-th2024

Gravitational form factors of the deuteron

J. Yu. Panteleeva, E. Epelbaum, A. M. Gasparyan +1

The gravitational form factors of the deuteron are calculated in the framework of non-relativistic chiral effective field theory. Non-relativistic reduction of the matrix element o…