New level density parameter beyond Egidy-Bucurescu's systematics
Junzhe Zhang, Yanan Zheng, Caixin Yuan, Yangyang Shen, Yingchen Mao

TL;DR
This paper extends the systematics of nuclear level density parameters beyond Egidy-Bucurescu, fitting new models and comparing their performance to existing parameters, with implications for theoretical calculations of transitional nuclei.
Contribution
It introduces new global level density parameters fitted with different models that outperform some existing parameters in statistical calculations.
Findings
New LDM-type parameters outperform Toke-Swiatecki parameters.
Reisdorf parameters show the best overall performance.
Power-law A-dependence parameters perform better than existing ones.
Abstract
Extending beyond the Egidy-Bucurescu systematics, the nuclear level density parameters (LDPs) for the back-shifted Fermi gas model were compiled. Three forms of LDPs were fitted: the liquid-drop model (LDM), the droplet model (DM), and the power-law dependence on mass number A. Additionally, the root-mean-square deviations (RMSDs) of the new LDPs and existing literature values were calculated. The newly fitted global LDM-type parameters outperform the commonly used Toke-Swiatecki parameters in various statistical model calculations. In contrast, neither the global nor the combined DM-type parameters yielded satisfactory results. Among the tested parameter sets, the widely adopted Reisdorf parameters exhibited the best overall performance, as evidenced by the larger number of experimental data points falling within their narrower RMSD confidence intervals. For the power-law A-dependence,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTheoretical and Computational Physics · Advanced Thermodynamics and Statistical Mechanics · Statistical Mechanics and Entropy
