Related papers: Statistical aspects of nuclear mass models
Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction…
We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning (ML) algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei.…
We present a systematic survey the range of predictions of the neutron star inner crust composition, crust-core transition densities and pressures, and density range of the nuclear `pasta' phases at the bottom of the crust provided by the…
In this proceeding, we have presented some highlight results on the constraints of the nuclear matter equation of state (EOS) from the data of nucleus resonance and neutron-skin thickness using the Bayesian approach based on the…
Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model…
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 keeping the known physics in various sophisticated mass models and…
The differences of the masses of nuclear isotopes with atomic numbers between \~10 and ~30 can be described within the chiral soliton approach in satisfactory agreement with data. Rescaling of the model is necessary for this purpose -…
We present an inference of the nuclear symmetry energy magnitude $J$, the slope $L$ and the curvature $K_{\rm sym}$ by combining neutron skin data on Ca, Pb and Sn isotopes and our best theoretical information about pure neutron matter…
A Bayesian method is used in this extensive work to generate a large set of minimally constrained equations of state (EOSs) for matters in neutron stars (NS). These EOSs are analyzed for their correlations with key NS properties, such as…
Potential energy surfaces of even-even superheavy nuclei are evaluated within the macroscopic-microscopic approximation. A very rapidly converging analytical Fourier-type shape parametrization is used to describe nuclear shapes throughout…
Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework for the equation of state (EOS) of strongly interacting…
The information-geometric statistical analysis on the stability of model reductions, reported previously [Imbri\v{s}ak and Nomura, Phys. Rev. C 107, 034304 (2023)] with a focus on the manifold boundary approximation method in the…
This article studies Bayesian model averaging (BMA) in the context of competing expensive computer models in a typical nuclear physics setup. While it is well known that BMA accounts for the additional uncertainty of the model itself, we…
The chi-squared based covariance approach allows one to estimate the correlations among desired observables related to nuclear matter directly from a set of fit data without taking recourse to the distributions of the nuclear matter…
Through ensemble learning with multitasking and complex connection neural networks, we aggregated nuclear properties, including ground state charge radii, binding energies, and single-particle state information obtained from the Kohn-Sham…
Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for…
This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the…
Nuclear liquid drop model is revisited and an explicit introduction of the surface-curvature terms is presented. The corresponding parameters of the extended classical energy formula are adjusted to the contemporarily known nuclear binding…
Using an explicitly isospin-dependent parametric Equation of State (EOS) for the core of neutron stars (NSs) within the Bayesian statistical approach, we infer the EOS parameters of super-dense neutron-rich nuclear matter from three sets of…
Extensive calculations of properties of supernova matter are presented, using the extended Nuclear Statistical Equilibrium model of PRC92 055803 (2015) based on a statistical distribution of Wigner-Seitz cells modeled using realistic…