Related papers: Inference of Parameters for Back-shifted Fermi Gas…
The prediction of cross sections for nuclei far off stability is crucial in the field of nuclear astrophysics. In recent calculations the nuclear level density -- as an important ingredient to the statistical model (Hauser-Feshbach) -- has…
The nuclear charge density distribution plays an important role in nuclear physics and atomic physics. As one of the most frequently used models to obtain charge density distribution, the two-parameter fermi (2pF) model has been widely…
Nuclear Astrophysics requires the knowledge of reaction rates over a wide range of nuclei and temperatures. In recent calculations the nuclear level density - as an important ingredient to the statistical model (Hauser-Feshbach) - has shown…
We systematically study the nuclear level densities of superheavy nuclei, including odd systems, using the single-particle energies obtained with the Woods-Saxon potential diagonalization. Minimization over many deformation parameters for…
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…
Realistic nuclear level densities (NLDs) obtained within the spectral distribution method (SDM) are employed to study nuclear processes of astrophysical interest. The merit of SDM lies in the fact that the NLDs corresponding to many body…
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory and incorporating available experimental charge radii of…
We calculate relativistic Fermi liquid parameters (RFLPs) for the description of the properties of dense nuclear matter (DNM) using Effective Chiral Model. Analytical expressions of Fermi liquid parameters (FLPs) are presented both for the…
The energy spectra of neutrons, protons, and alpha-particles have been measured from the d+59Co and 3He+58Fe reactions leading to the same compound nucleus, 61$Ni. The experimental cross sections have been compared to Hauser-Feshbach model…
A phenomenological level density model that has different level density parameter sets for the state densities of the deformed and the spherical states, and the optimization of the parameters using experimental data of the average s-wave…
Level densities are often parametrized using the back-shifted Bethe formula (BBF) for nuclei that possess experimental data for s-wave neutron resonance average spacings and a complete discrete level sequence at low excitation energies.…
A widely used relativistic Fermi gas model and plane-wave impulse approximation are tested against electron-nucleus scattering data. Inclusive quasi-elastic cross section are calculated and compared with high-precision data for C, O, and…
We propose a neural-network-based variational framework for nuclear Density Functional Theory based on the extended Thomas--Fermi (ETF) model, in which proton and neutron number densities are represented by multilayer perceptrons and…
The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and…
We develop an asymmetric relativistic Fermi gas model for the study of the electroweak nuclear response in the quasielastic region. The model takes into account the differences between neutron and proton densities in asymmetric (N > Z)…
A microscopic method for calculating nuclear level densities (NLD) is developed, based on the framework of energy density functionals. Intrinsic level densities are computed from single-quasiparticle spectra obtained in a finite-temperature…
The variation of total nuclear level densities (NLDs) and level density parameters with proton number $(Z)$ are studied around the $\beta$-stable isotope, $Z_{0}$, for a given mass number. We perform our analysis for a mass range $A=40$ to…
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000…
Field-scale properties of fractured rocks play crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion…
The knowledge of the nuclear level density is necessary for understanding various reactions including those in the stellar environment. Usually the combinatorics of Fermi-gas plus pairing is used for finding the level density. Recently a…