1104 papers
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.
This paper outlines a modern, transparent, and methodologically robust effort to improve the precision and reliability of nuclear charge radii determinations by integrating complementary experimental techniques with advanced theoretical frameworks.
This study employs covariant density functional theory to investigate the structural evolution of even-even tungsten isotopes from W to W, revealing dynamic shape transitions, identifying a potential subshell closure at , predicting a neutron drip line at , and validating these findings against experimental data and other theoretical models to enhance understanding of nuclear structure and r-process nucleosynthesis.
This paper presents a numerical program that calculates deep inelastic scattering structure functions at next-to-leading order accuracy within the dipole picture, utilizing a reformulated expression for massive quark impact factors to ensure stable cross-section evaluations.
This study utilizes lattice effective field theory to demonstrate that while refined nuclear interactions improve zero-temperature binding and saturation properties, they also significantly lower the critical temperature of symmetric nuclear matter, establishing finite-temperature criticality as a distinct and essential benchmark for future interaction development.
This paper employs deep learning within a Bayesian framework to simultaneously extract the real and imaginary components of the in-medium heavy quark potential from bottomonium suppression data across RHIC and LHC energies, revealing that while the real part remains close to the vacuum form, the imaginary part is the dominant driver of bottomonium suppression.
This paper employs a dinuclear system model with microscopic inputs derived from finite-temperature covariant density functional theory to successfully reproduce experimental cross-sections for known superheavy elements and predicts maximum synthesis cross-sections for four specific reaction pathways targeting element 120.
This paper presents a Bayesian framework for quantifying statistical uncertainties in covariant density functional theory by sampling parameter variations and propagating posterior distributions via a subspace-projected approach, successfully reproducing observables for deformed nuclei while highlighting current limitations in describing near-spherical systems.
The paper introduces NucleiML, a machine learning framework that accelerates Bayesian exploration of nuclear equations of state by providing a -fold speedup in calculating finite nuclei ground-state properties while maintaining reasonable accuracy, thereby enabling the efficient integration of finite nuclei and neutron star constraints.
This paper utilizes a quantum phase-space formalism to derive and verify the relativistic spatial distributions of transverse orbital angular momentum, intrinsic spin, and total angular momentum in the transverse plane for spin-0 and spin-1/2 targets, confirming the transverse spin sum rule and revealing non-trivial angular momentum distributions even for spin-0 systems.
This paper utilizes a phenomenological spectrum shift model to demonstrate a striking correlation between average partonic transverse momentum loss and the initial energy density of the Quark-Gluon Plasma across a wide range of collision energies, while also successfully predicting high- hadron elliptic flow by coupling the model to geometric event shape estimates.