PaperPanorama
FAQ

Nuclear Theory·nucl-th

Monday·August 17, 2026

4 papers—4 primary·0 cross-listed

All4Primary4Cross-lists0
  1. 01

    Role of the Meson in Softening the Symmetry Energy within the DDRHF Model

    Qirui Li🇨🇳 · Jinniu Hu🇨🇳 · Ying Zhang🇨🇳 · Hong Shen🇨🇳

    We investigate the effects of the isovector-scalar meson on the density dependence of the symmetry energy within the density-dependent relativistic Hartree--Fock (DDRHF) framework. As a baseline, we generate accepted DDRHF parametrizations including the , , , and mesons by imposing empirical constraints on the saturation properties of nuclear matter. The resulting symmetry-energy slope parameters are confined to relatively large values, --. Two representative parametrizations, denoted RHF-NK1 and RHF-NK2, are randomly selected from this ensemble. Starting from these two parametrizations, we introduce the meson and readjust the meson--nucleon couplings under the same saturation-property constraints. The numerical optimization shows that small values of are obtained most efficiently when the coupling is taken to be constant. In this case, is reduced from approximately to , while the binding energy per nucleon, saturation density, symmetry energy, and incompressibility coefficient remain nearly unchanged. A channel-by-channel decomposition shows that the softening is not caused by the direct -meson contribution alone, but by a redistribution among the , , and mesons together with the isoscalar Fock contributions. The resulting neutron-star mass--radius relations shift toward smaller radii, indicating that the meson provides an efficient additional degree of freedom for controlling the isovector properties of DDRHF models.

    nucl-thPLB(2026)·0 citations
  2. 02

    Bayesian inference of event-by-event collision geometry from charged-particle multiplicity in heavy-ion collisions

    Yige Huang🇨🇳 · Fu-Peng Li🇨🇳 · Hanwen Feng🇨🇳 · Nu Xu🇨🇳

    We propose the Inference-driven Participant Determination (IPD) method, a Bayesian framework for inferring event-by-event posterior distributions of the number of participants () and binary collisions () from final-state charged-particle multiplicities in relativistic heavy-ion collisions. The joint distribution of obtained from the Monte-Carlo Glauber model is used as the prior, while negative binomial distributions calibrated to charged-particle multiplicity fluctuations define the likelihood. This approach replaces conventional hard-cut centrality classification with a probabilistic assignment based on , making the multiplicity--geometry smearing explicit and reducing the impact of volume fluctuations on downstream observables. A closure test using an UrQMD-MCG hybrid model at ~GeV shows that the method yields well-calibrated posterior distributions with negligible bias and improves the reconstruction of net-proton cumulants relative to conventional multiplicity-based centrality selection.

    nucl-thhep-phphysics.data-an0 citations
  3. 03

    Neural network maximum entropy framework for distribution reconstruction in heavy-ion collisions

    Qian-Ru Lin🇨🇳 · Fu-Peng Li🇨🇳 · YiGe Huang🇨🇳 · Long-Gang Pang🇨🇳

    We develop a neural-network maximum-entropy (NN+MaxEnt) framework for reconstructing probability distributions from limited observables in heavy-ion collisions. The method combines flexible neural-network representations with Shannon-entropy regularization, preserving positivity and normalization without assuming a fixed analytic form. After validation with Gaussian, Poisson, and mixed-Poisson closure tests, we apply the framework to two physics-motivated inverse problems: an effective multiplicity reconstruction constrained by functional renormalization group cumulants, used as a closure test, and the conditional jet-energy-loss distribution extracted from single-inclusive jet data in Pb+Pb collisions at ~TeV. For the fRG closure test, NN+MaxEnt accurately reproduces the imposed cumulants and yields distributions consistent with conventional MaxEnt solutions. For jets, the reconstructed energy-loss distributions reproduce the measured ; at an initial jet momentum , the conditional mean energy loss is , with a central interval of . The extracted energy-loss profile is qualitatively consistent with Bayesian MCMC and LBT results. NN+MaxEnt thus provides a flexible, less ansatz-dependent framework for regularized distribution reconstruction from observables connected to the underlying distribution through differentiable forward maps.

    nucl-thhep-ph0 citations
  4. 04

    Enhancement of alpha-decay by positive hexadecapole deformation

    Kai Ren · Minghui Hu · Pengfei Ma · Junlong Tian · Cheng Li

    Whether hexadecapole deformation () influences decay remains controversial: machine-learning analyses suggest a strong link to cluster preformation, while empirical formulas find only marginal effects. We show that this discrepancy originates in the treatment of shell effects---without an explicit shell correction, residuals near magic numbers are absorbed into the deformation coefficients, obscuring the genuine dependence. Adding the inverse Casten factor , which encodes valence proton--neutron correlations relative to the nearest closed shells, and the parent-nucleus deformation to the Royer formula reduces the root-mean-square deviation from 0.309 to 0.184 for 192 even--even nuclei. The fitted negative coefficient shows that positive hexadecapole deformation systematically shortens half-lives, consistent with enhanced -cluster preformation at locally convex surface regions. For the isotopic chain (Pu), where pronounced occurs, the original Royer formula overestimates half-lives by up to ~dex, providing a clear, testable signature of this surface-preformation effect. The same correction also improves the UDL and yields predictions for 1060 even--even nuclei.

    nucl-th0 citations

Affiliations

first authorsco-authorsvia INSPIRE