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.

⚛️ phenomenology

Radiative corrections to the nucleon isovector gVg_V and gAg_A

This paper calculates enhanced electroweak, QCD, and QED radiative corrections to the nucleon coupling constants gVg_V and gAg_A, providing updated relations between lattice-QCD and physical values that yield a total correction of approximately 3.5% to 5.6% and predict a physical axial charge gAg_A of 1.265(26) or 1.240(9) depending on the input methodology.

Oleksandr Tomalak, Yi-Bo Yang2026-03-10
⚛️ phenomenology

A Lattice QCD study of p−Λp-\Lambda scattering in continuum and chiral limits

This paper presents the first systematic lattice QCD study of I=1/2I=1/2 proton-Λ\Lambda scattering across multiple pion masses and lattice spacings, yielding scattering parameters and cross sections that agree with experimental data and confirm attractive interactions critical for nuclear theory and neutron star modeling.

Hang Liu, Liuming Liu, Jin-Xin Tan, Wei Wang, Haobo Yan, Qian-Teng Zhu2026-03-09
🔭 astrophysics

Inverse-mapped density-dependent relativistic mean-field inference of the neutron-star equation of state with multi-messenger constraints

This study employs Bayesian inference within a density-dependent relativistic mean-field framework, constrained by multi-messenger data ranging from chiral effective field theory to massive pulsar observations, to reconstruct a thermodynamically consistent neutron-star equation of state that predicts a compact canonical radius of approximately 11.6 km and reveals strongly interacting matter cores with sound speeds exceeding the conformal limit.

Wen-Jie Xie, Cheng-Jun Xia2026-03-09
🔬 physics

Physics-Embedded Bayesian Neural Network (PE-BNN) to predict Energy Dependence of Fission Product Yields with Fine Structures

This paper introduces a physics-embedded Bayesian neural network (PE-BNN) framework that integrates an energy-independent phenomenological shell factor and WAIC-based hyperparameter optimization to accurately predict energy-dependent fission product yields with fine structures and close agreement with known nuclear shell effects.

Jingde Chen, Yuta Mukobara, Kazuki Fujio, Satoshi Chiba, Tatsuya Katabuchi, Chikako Ishizuka2026-03-06