246 citations · 1.8k across the 38 of their papers we have counts for
5 papers · 1 filter
Nuclear mass predictions with machine learning reaching the accuracy required by -process studies
Z. M. Niu, H. Z. Liang
Nuclear masses are predicted with the Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighbouring nuclei. By keepin…
Calibration of nuclear charge density distribution by back-propagation neural networks
Zu-Xing Yang, Xiao-Hua Fan, Tomoya Naito +3
Based on the back-propagation neural networks and density functional theory, a supervised learning is performed firstly to generate the nuclear charge density distributions. The ch…
Deformed relativistic Hartree-Bogoliubov theory in continuum with a point-coupling functional. II. Examples of odd Nd isotopes
DRHBc Mass Table Collaboration, Cong Pan, Myung-Ki Cheoun +38
The aim of this work is to extend the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) based on the point-coupling density functionals to odd- and odd-odd nu…
Calculation of -decay half-lives with Skyrme Hartree-Fock-Bogoliubov+-QRPA and isoscalar pairing strengths optimized by a Bayesian method
Futoshi Minato, Z. M. Niu, Haozhao Liang
For radioactive nuclear data, decay is one of the most important information and is applied to various fields. However, some of the -decay data are not available due to expe…
associated production with a bottom quark pair from the Higgs boson decay in next-to-leading order QCD
Xue-An Pan, Zhong-Ming Niu, Mao Song +3
In this work, we investigate the next-to-leading order (NLO) QCD correction to associated production with a bottom quark pair from the Higgs boson decay within the nonrelativ…