Nuclear theory sits at the fascinating intersection of particle physics and the forces that hold our universe together. This field explores how protons and neutrons bind inside atomic nuclei, seeking to understand the fundamental interactions that govern matter at its most dense and energetic levels. While the mathematics involved can be incredibly complex, the core questions are deeply human: how does the universe function at its smallest scales, and what happens when we push matter to its limits?

At Gist.Science, we make these cutting-edge discoveries accessible by processing every new preprint published in this category on arXiv. Our team transforms dense academic manuscripts into clear, plain-language summaries alongside detailed technical overviews, ensuring that both experts and curious readers can grasp the latest breakthroughs without getting lost in the jargon. Below are the latest papers in nuclear theory, distilled and ready for you to explore.

A Bayesian Inference of Hybrid Stars with Large Quark Cores

This study employs Bayesian inference with hadronic and quark matter models to demonstrate that while the presence of large quark cores in 1.4 solar mass neutron stars depends on the specific quark model used, both frameworks predict quark matter in 2 solar mass stars, with the mass-radius curve slope serving as a key indicator of non-nucleonic matter.

Milena Albino, Tuhin Malik, Márcio Ferreira, Constança Providência2026-02-20⚛️ nucl-th

Anisotropic flows in Au+Au collisions at sNN=2.4GeV\sqrt{s_{\rm{NN}}} = 2.4\,\text{GeV} with a Skyrme pseudopotential

Using a lattice Boltzmann-Uehling-Uhlenbeck transport model with a density-, momentum-, and isospin-dependent N5^5LO Skyrme pseudopotential, this study analyzes proton anisotropic flows in Au+Au collisions at sNN=2.4GeV\sqrt{s_{\rm{NN}}} = 2.4\,\text{GeV} to demonstrate their strong sensitivity to momentum-dependent mean-field potentials and the symmetric nuclear matter incompressibility, while highlighting the need to incorporate higher-order equation-of-state parameters and in-medium cross-section modifications for future Bayesian extractions of nuclear matter properties.

Xin Li, Si-Pei Wang, Rui Wang, Zhen Zhang, Jie Pu, Chun-Wang Ma, Lie-Wen Chen2026-02-20⚛️ nucl-th

Hyperon longitudinal polarization and vector meson spin alignment in a thermal model for heavy-ion collisions

This paper proposes a thermal model incorporating a common local spin equilibrium for spin-1/2 and spin-1 particles to explain the simultaneous longitudinal polarization of Λ\Lambda hyperons and positive spin alignment of vector mesons in heavy-ion collisions, noting that while the model reproduces observed trends, it currently lacks full quantitative agreement with experimental data.

Soham Banerjee, Samapan Bhadury, Wojciech Florkowski, Amaresh Jaiswal, Radoslaw Ryblewski2026-02-20⚛️ nucl-th

νpνp-process in Core-Collapse Supernovae: Imprints of General Relativistic Effects

This study demonstrates that incorporating General Relativistic effects into core-collapse supernova models significantly enhances the efficiency of the ν\nup-process, enabling a sufficiently massive progenitor to reproduce the full range of solar system pp-nuclide abundances up to 102^{102}Pd, whereas Newtonian calculations fail to do so.

Alexander Friedland, Derek J. Li, Giuseppe Lucente, Ian Padilla-Gay, Amol V. Patwardhan2026-02-19⚛️ nucl-th

Bayesian Analysis of the Neutron Star Equation of State and Model Comparison: Insights from PSR J0437+4715, PSR J0614+3329, and Other Multi-Physics Data

This study employs a comprehensive Bayesian analysis integrating terrestrial nuclear physics data with astrophysical observations from PSR J0437+4715, PSR J0614+3329, and GW170817 to constrain the neutron star equation of state, ultimately favoring the Skyrme model and yielding precise determinations of symmetry energy parameters and the radius and tidal deformability of a 1.4 solar mass neutron star.

Sk Md Adil Imam, N. K. Patra2026-02-19⚛️ nucl-th

Solving BDNK diffusion using physics-informed neural networks

This paper reformulates the relativistic BDNK diffusion equation in flux-conservative form and solves it in (1+1)(1+1)D using a second-order Kurganov-Tadmor scheme and a novel SA-PINN-ACTO framework, demonstrating that the neural network approach accurately reproduces finite volume solutions for smooth profiles while exhibiting expected errors near discontinuities.

Vicente Chomalí-Castro, Nick Clarisse, Nicki Mullins, Jorge Noronha2026-02-19⚛️ nucl-th