To improve the predictability of complex computational models in the experimentally-
unknown domains, we propose a Bayesian statistical machine learning framework utilizing …

The exploration of nuclear mass or binding energy, a fundamental property of atomic nuclei,
remains at the forefront of nuclear physics research due to limitations in experimental …

A naive Bayesian model averaging (NBMA) method is developed to predict nuclear masses.
In the NBMA method, the weights of different models may be different for each nucleus …

Atomic masses are a foundational quantity in our understanding of nuclear structure,
astrophysics, and fundamental symmetries. The longstanding goal of creating a predictive …

Developments in the description of the masses of atomic nuclei have led to various nuclear
mass models that provide predictions for masses across the whole chart of nuclides. These …

The power-moderated mean (PMM) method is a new method approved by the Consultative
Committee for Ionizing Radiation for calculating a key comparison reference value and its …

The astrophysical r process plays a vital role in the production of heavy elements. Modeling
of the r process is sensitive to masses and further requires knowledge of masses beyond …

Decomposing theoretical nuclear mass predictions into a liquid-drop parametrization and
local shell effects shows that r-process abundances are virtually insensitive to large …

Most nuclei are deformed, and these deformations play an important role in various nuclear
and astrophysical phenomena. Microscopic nuclear mass models have been developed …