Nuclear level density from relativistic density functional theory and combinatorial method

Jiang, X.F.; Wu, X.H.; Zhao, P.W.; Meng, J.

doi:10.1016/j.physletb.2024.138448

Cited by 2 publications

(1 citation statement)

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“…These phenomenological models with simplified assumptions are usually more computationally accessible but are limited to excitation energies above 5 MeV. Microscopic models, such as Hartree-Fock-Bardeen-Cooper-Schrieffer (HF-BCS) [9], Gogny-Hartree-Fock-Bogoliubov (Gogny-HFB) [10], and Skyrme-Hartree-Fock-Bogoliubov (Skyrme-HFB) [11], and density functional theory combinatorial models [12,13], the Monte Carlo shell model [14], quasi-particle random phase approximation (QRPA) [15], and core-quasi particle coupling (CQC) model [16], have attempted to describe the NLD from more fundamental particle interactions with excitation energies ranging from 0 to 20 MeV. Thus, a high efficiency and unified predictive model is urgently required for the application of NLD.…”

Section: Introductionmentioning

confidence: 99%

Uncertainties of nuclear level density estimated using Bayesian neural networks*

Wang 王,

Cui 崔,

Tian 田

et al. 2024

Chinese Phys. C

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Nuclear level density (NLD) is a critical parameter for understanding nuclear reactions and the structure of atomic nuclei, yet accurate estimation of NLD is challenging due to limitations inherent in both experimental measurements and theoretical models. This paper presents a sophisticated approach using Bayesian Neural Networks (BNN) to analyse NLD across a wide range of models. It uniquely incorporates the assessment of model uncertainties. The application of BNN has demonstrated remarkable success in accurately predicting NLD values when compared to recent experimental data, confirming the effectiveness of our methodology. The reliability and predictive power of the BNN approach not only validates its current application, but also encourages its integration into future analyses of nuclear reaction cross sections.

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