1634 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 presents a PYTHIA8-based Monte Carlo study of jet substructure observables, including angularities and the primary Lund Plane with a focus on the dead cone effect, for light and heavy flavor jets in 200 GeV collisions at RHIC to test non-perturbative physics models and enable feasible measurements at sPHENIX.
This paper proposes projected energy correlators measured on the recoiling QCD radiation of a boson as a benchmark observable for precision QCD at the LHC, presenting the first complete NLO+NNLL matched predictions by combining fixed-order amplitudes, high-order resummation, and non-perturbative matrix elements within a flexible framework that avoids jet algorithms.
This paper presents a Monte Carlo method based on the Raubold-Lynch algorithm to construct the exact relativistic three-body phase space for hypertriton mesonic decays into charged and neutral pions, providing a kinematic baseline for future theoretical predictions and experimental comparisons.
This paper derives a general non-abelian Lindblad-type quantum master equation for quarkonium evolution in a quark-gluon plasma that overcomes previous temperature-energy gap limitations, enabling a faithful description of the system's quantum dynamics across all expansion regimes.
This paper demonstrates that the Sample-based Quantum Diagonalization (SQD) framework, when applied to noisy intermediate-scale quantum (NISQ) hardware, can successfully solve large-scale nuclear shell model problems like that exceed the memory limits of classical high-performance computing, offering a more scalable and time-efficient alternative to traditional variational quantum algorithms.
This paper introduces a generalized stochastic quantization framework based on jump-diffusion processes (Lévy flights) to overcome topological freezing and restore ergodicity in Euclidean lattice field theories, specifically demonstrating its effectiveness in 2d U(1) gauge theory.
This paper employs holographic QCD to demonstrate that chiral soliton lattices in rotating matter under a magnetic field constitute a ground state, deriving an effective Hamiltonian with an anisotropic, field-dependent meson decay constant and analyzing the system's thermodynamic properties and brane interpretation.
This study demonstrates that incorporating nuclear drip-line physics into nuclear statistical equilibrium models significantly alters the composition of supernova matter by enhancing the formation of extremely neutron-rich light clusters, thereby reducing free-neutron density and the charge fraction of heavy nuclei.
This paper presents first-principles calculations of Be weak and radiative transition rates in Big Bang Nucleosynthesis, revealing significant deviations from previous estimates but concluding that these enhanced destruction channels are insufficient to resolve the primordial lithium problem, thereby necessitating a broader reassessment of the BBN nuclear network before invoking new physics.
This paper introduces the Inference-driven Participant Determination (IPD) method, a Bayesian framework that utilizes charged-particle multiplicity and Monte-Carlo Glauber priors to probabilistically infer event-by-event collision geometry, thereby reducing volume fluctuations and improving the reconstruction of net-proton cumulants compared to conventional centrality classification.