Related papers: Study on nuclear $\alpha$-decay energy by an artif…
The nuclear level densities and level-density parameters in fissioning nuclei at their saddle points of fission barriers - $a_{f}$, as well as those for neutron - $a_{n}$, proton - $a_{p}$ , and $\alpha$-particle - $a_{\alpha}$ emission…
Variation of nuclear shell effects with nucleon numbers are evaluated using the modified Bethe-Weizsacker mass formula (BWM) and the measured atomic masses. The shell effects at magic neutron numbers N = 8, 20, 28, 50, 82 and 126 and magic…
The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to…
Machine learning approaches to nuclear mass prediction have achieved remarkable accuracy, but typically rely on existing theoretical baselines or hand-crafted physics features. Here we demonstrate that these prerequisites can be supplanted…
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The…
A detailed study of $\alpha$-clusters decay is exhibited by incorporating crucial microscopic nuclear structure information into the estimations of half-life and preformation factor. For the first time, using the k-cross validation…
Due to the unavailability of solar irradiance data for many potential sites of Nepal, the paper proposes predicting solar irradiance based on alternative meteorological parameters. The study focuses on five distinct regions in Nepal and…
A general shell model formalism for the nonmesonic weak decay of the hypernuclei has been developed. It involves a partial wave expansion of the emitted nucleon waves, preserves naturally the antisymmetrization between the escaping…
The nuclear shell model is a useful and widely used tool for nuclear structure, but it can be hampered by the exponential growth of the basis. Drawing inspiration from quantum information theory, one can show that the proton and neutron…
Examples of the change of neutron shell-structure in both weakly-bound and resonant neutron one-particle levels in nuclei towards the neutron drip line are exhibited. It is shown that the shell-structure change due to the weak binding may…
Nuclear $\beta$ decay is a key element of the astrophysical rapid neutron capture process ($r$-process). In this paper, we present state-of-the-art global $\beta$-decay calculations based on the quantified relativistic nuclear energy…
We investigate the shell structure of bubble nuclei in simple phenomenological shell models and study their binding energy as a function of the radii and of the number of neutron and protons using Strutinsky's method. Shell effects come…
The neutrinoless double beta ($0\nu\beta\beta$) decay process could provide crucial information to determine the absolute scale of the neutrino masses, and it is the only one that can establish whether neutrino is a Dirac or a Majorana…
We select 300 different parent nuclei in the range Z from 64 to 106. The proximity potentials Zhang 2013 and Guo 2013 are employed to calculate the nuclear potential. The influence of the nuclear surface diffuseness is applied in the…
The first excited 2+ energy states of nuclei give many substantial information related to the nuclear structure. Including these levels, all excited states of nuclei are shown regularities in spin, parity and energy. In the even-even…
In this paper we introduce PkANN, a freely available software package for interpolating the non-linear matter power spectrum, constructed using Artificial Neural Networks (ANNs). Previously, using Halofit to calculate matter power spectrum,…
We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information…
One of the most common and universal problems in science is to investigate a function. The prediction can be made by an Artificial Neural Network (ANN) or a mathematical model. Both approaches have their advantages and disadvantages.…
Despite the remarkable performance, modern deep neural networks are inevitably accompanied by a significant amount of computational cost for learning and deployment, which may be incompatible with their usage on edge devices. Recent efforts…
Understanding and modeling plasma-surface interactions frame a multi-scale as well as multi-physics problem. Scale-bridging machine learning surface surrogate models have been demonstrated to perceive the fundamental atomic fidelity for the…