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Peng-Xiang Du

Publications and source records attributed to Peng-Xiang Du.

4 recordsLinked to original sources

Impact of Nuclear Level Density on $r$-Process Rare-Earth Peak Nucleosynthesis

The rare-earth peak ($A\sim164$) is a prominent feature of the $r$-process, and previous theoretical studies suggest that it is possibly linked to local nuclear structural effects. However, the nuclear level density (NLD), a physical quantity directly reflecting these properties, has been largely overlooked compared to other structural properties such as nuclear masses. To address this, we perform $r$-process simulations across three astrophysical scenarios using neutron-capture rates derived from six distinct NLD models. Our results reveal that microscopic models yield systematic deviations in NLD relative to phenomenological ones, leading to critical impacts on nucleosynthesis. Specifically, systematic NLD differences in even-$A$ nuclei redirect the nuclear flow, accelerating the early formation of the rare-earth peak and temporarily enhancing its magnitude. This underlying structural shift also fundamentally alters the $r$-process sensitivity to the neutron-capture rate, effectively eliminating its dependence on the odd-even nature of protons. Overall, these findings demonstrate that the internal nuclear structure encoded within NLDs can collectively induce a global redirection of the nucleosynthesis pathway, highlighting the critical need for self-consistent microscopic inputs in future simulations.

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Microscopic Statistical Calculation of Nuclear Level Density Based on Relativistic Density Functional Theory

A microscopic statistical model based on the relativistic density functional theory (RDFT) is developed to calculate the nuclear level density (NLD). The approach employs self-consistent single-particle levels obtained from RDFT as input, incorporates pairing correlations within a finite-temperature Bardeen-Cooper-Schrieffer (BCS) theory, and accounts for rotational and vibrational collective enhancement effects. The spin cut-off parameter is calculated from the single-particle levels, thereby naturally retaining the shell effects and the structural characteristics of different nuclei. Using the shape-coexisting nucleus 98Sr as a representative example, the microscopic origin of the deformation effect on the NLD is investigated. In addition, the calculated NLDs are systematically compared with those from various phenomenological and microscopic models, as well as with available experimental data. The results indicate that although certain discrepancies exist among different models, they exhibit consistent overall evolutionary trends. Meanwhile, the RDFT-based microscopic statistical approach is capable of providing a reasonable description of the experimental NLDs as well as the s- and p-wave neutron resonance spacings.

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Inference of Parameters for Back-shifted Fermi Gas Model using Feedback Neural Network

The back-shifted Fermi gas model is widely employed for calculating nuclear level density (NLD) as it can effectively reproduce experimental data by adjusting parameters. However, selecting parameters for nuclei lacking experimental data poses a challenge. In this study, the feedforward neural network (FNN) was utilized to learn the level density parameters at neutron separation energy $a(S_{n})$ and the energy shift $\varDelta$ for 289 nuclei. Simultaneously, parameters for nearly 3000 nuclei are provided through the FNN. Using these parameters, calculations were performed for neutron resonance spacing in $s$ and $p$ waves, cumulative number of levels, and NLD. The FNN results were also compared with the calculated outcomes of the parameters from fitting experimental data (local parameters) and those obtained from systematic studies (global parameters), as well as the experimental data. The results indicate that parameters from the FNN achieve performance comparable to local parameters in reproducing experimental data. Moreover, for extrapolated nuclei, parameters from the FNN still offer a robust description of experimental data.

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Calculation of microscopic nuclear level densities based on covariant density functional theory

A microscopic method for calculating nuclear level density (NLD) based on the covariant density functional theory (CDFT) is developed. The particle-hole state density is calculated by combinatorial method using the single-particle levels schemes obtained from the CDFT. Then the level densities are obtained by taking into account collective effects such as vibration and rotation. Our results are compared with those from other NLD models, including phenomenological, microstatistical, and non-relativistic HFB combinatorial models. The comparison suggests that the general trends among these models are basically the same, except for some deviations from different NLD models. In addition, the NLDs of the CDFT combinatorial method with normalization are compared with experimental data, including the observed cumulative number of levels at low excitation energy and the measured NLDs. Compared with the existing experimental data, the CDFT combinatorial method can give reasonable results.

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