Dataset referencing 2018UT01

Phys.Rev. C 97, 014306 (2018)

R.Utama, J.Piekarewicz

Validating neural-network refinements of nuclear mass models

ATOMIC MASSES ^53,54Ca, ^56,57Sc, ⁶⁴Cr, ⁶²Mn, ⁵²Co, ⁵⁶Cu, ⁸²Zn, ⁸⁶Ge, ⁹¹Se, ⁸²Zn, ¹⁰⁰Rb, ¹⁰⁵Y, ^82,106,107Zr, ^84,110Nb, ^114,115Tc, ¹²¹Rh, ¹²³Pd, ^129,131Cd, ¹³⁸Sb, ¹⁴¹I, ¹⁴⁹Ba, ^150,151La, ¹³⁷Eu, ¹⁹⁰Tl, ²¹⁵Pb, ¹⁹⁴Bi, ¹⁹⁸At, ^{197,198,202,232,233}Fr, ²⁰¹Ra, ^205,206Ac, ^{215,216,221,222}U; ^132,133,134Cd, ^{133,134,135,136,137}In, ^136,138Sn; calculated total binding energies using the microscopic HFB-19-Bayesian neural network (BNN), and mic-mac model of Duflo and Zuker (DZ) with Bayesian neural network (BNN), and compared with various theoretical mass formulas (HFB-19, DZ, FRDM-2012, HFB-27 and WS3), and with experimental values in AME-2016; deduced root-mean-square deviations, refinements in Bayesian neural network (BNN) analysis of mass models.

doi: 10.1103/PhysRevC.97.014306

Matching datasets in ENSDF

Retrieve selected ENSDF datasets:

Nuclide	Dataset	Last Revised	References
	Select All
⁵⁶ Sc	ADOPTED LEVELS, GAMMAS	2022-03	All references
⁵⁶ Cu	ADOPTED LEVELS, GAMMAS	2022-03	All references
⁶² Mn	ADOPTED LEVELS, GAMMAS	2025-07	All references
⁶⁴ Cr	ADOPTED LEVELS, GAMMAS	2021-12	All references
¹⁰⁰ Rb	ADOPTED LEVELS	2021-02	All references
¹²³ Pd	ADOPTED LEVELS	2021-05	All references
¹⁴⁹ Ba	ADOPTED LEVELS, GAMMAS	2022-10	All references
²²² U	ADOPTED LEVELS	2023-10	All references

Retrieve selected ENSDF datasets: