1664 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 introduces a hierarchical framework combining ab initio many-body calculations with Bayesian neural networks to create accurate, uncertainty-quantified emulators for predicting nuclear properties across isotopic chains and performing global sensitivity analysis of nuclear forces.
This paper introduces a generalized definition of isothermal compressibility in (2+1)-flavor QCD based on conserved charge fluctuations, presenting lattice results that align with hadron resonance gas models and heavy-ion collision data while demonstrating that the compressibility on the pseudo-critical line closely resembles that of an ideal gas.
This study utilizes Monte Carlo simulations based on the Color Glass Condensate formalism to demonstrate that multiplicity distributions in proton-proton and proton-lead collisions can distinguish between different initial-state proton geometries, specifically testing the three-quark Y-shape baryon junction model, while highlighting the critical importance of accounting for intrinsic saturation scale fluctuations.
This paper proposes a method to construct the equation of state for supra-saturation density nuclear matter by modeling nucleons as topological solitons within a bosonized Nambu-Jona-Lasinio framework, demonstrating that the dynamical restoration of chiral symmetry leads to a stiffened equation of state compatible with neutron star observations.
This study demonstrates that applying the Medium Separation Scheme (MSS) alongside Magnetic Field Independent Regularization (MFIR) to the Nambu--Jona-Lasinio model of magnetized two-flavor color superconducting quark matter eliminates unphysical oscillations and ensures positive magnetization, thereby correcting artifacts found in traditional approaches that fail to properly separate medium effects from vacuum contributions.
This paper proposes that the limited validity of standard nucleon-nucleon effective field theories arises from nonanalytic structures in the complex momentum plane, and demonstrates that introducing dimer fields to account for these singularities yields a meromorphic -matrix that enables accurate, cutoff-insensitive descriptions of low-energy scattering up to the pion production threshold.
This paper presents lattice QCD calculations of light-nuclei masses across various quark masses to establish first-principles constraints on nuclear interactions and reveals that, while quark-mass contributions to binding energy are small and additive, the dominant contribution arises from the gluonic component via the QCD trace anomaly.
This paper presents the first *ab initio* nonlocal optical potential calculations for magnesium isotopes (Mg) using the symmetry-adapted no-core shell model and multiple-scattering theory, successfully reproducing experimental data for Mg and providing parameter-free predictions for heavier isotopes that validate global models near the N=20 island of inversion while highlighting their limitations.
This paper introduces a multi-task deep neural network equipped with a novel loss function and odd-even effect incorporation to more accurately predict both nuclear fission product yields and their experimental errors in peak-shaped data compared to conventional independent learning methods.
This study employs a physics-informed machine learning framework to determine field-dependent Nambu-Jona-Lasinio model parameters, specifically the running coupling constant and quark anomalous magnetic moment, by fitting lattice QCD data to successfully reproduce the inverse magnetic catalysis effect in magnetized QCD matter.