Data on nuclear level densities extracted from transmission data or gamma energy spectrum store the basic statistical information about nuclei at various temperatures. Generally this extracted data goes through model fitting using computer codes like CASCADE. However, recently established semiclassical methods involving no adjustable parameters to determine the level density parameter for magic and semi-magic nuclei give a good agreement with the experimental values. One of the popular ways to paramaterize the level density parameter which includes the shell effects and its damping was given by Ignatyuk. This damping factor is usually fitted from the experimental data on nuclear level density and it comes around 0.05 M eV −1 . In this work we calculate the Ignatyuk damping factor for various nuclei using semiclassical methods.

Semiclasically derived shell structure and pairing correlation energies have been included in the recently developed formula by Royer et al to evaluate the binding energies of several well known α−radioactive nuclei after appending the effects of deformation as well. Exploiting single-particle level density for spherically symmetric harmonic oscillator potential with spin–orbit interactions within the microscopic-macroscopic framework, Qα-values and the penetration probabilities are determined. This method has been extended to several recently detected superheavy elements, experimentally unknown ones and, also to four α−decay chains in Z = 120 isotopes. The obtained results have been compared with the available experimental and theoretical ones. A stability analysis for such nuclei has been done on the basis of the obtained half-lives (T1/2-values) as well.

The symmetric components of the spatial part of S-and D-states' wavefunctions for triton ( 3 H) are investigated utilizing semiclassical expansion (in the powers of ). Analysis of the diagonalized Hamiltonian reveals the existence of two different mass states within the ground state of triton. We have solved the coupled differential equations for the two admixed states 2 S 1/2 and 4 D 1/2 owing to tensor interactions exploiting classical WKB-theory using phenomenological Feshbach-Pease potentials. The relative probability of the D-state is found to be in good agreement with the experimentally inferred value (4 -5 %).

We implement machine learning algorithms to nuclear data. These algorithms are purely data driven and generate models that are capable to capture intricate trends. Gradient boosted trees algorithm is employed to generate a trained model from existing nuclear data, which is used for prediction for data of damping parameter, shell correction energies, quadrupole deformation, pairing gaps, level densities and giant dipole resonance for large number of nuclei. We, in particular, predict level density parameter for superheavy elements which is of great current interest. The predictions made by the machine learning algorithm is found to have standard deviation from 0.00035 to 0.73.Recent works [10,11] use ML techniques to predict nuclear mass and charge radii using Support Vector Machines and Neural Networks. These works use two input parameters (features) of number of neutrons and number of protons. These techniques though give a good predictions for the above observables, but they do not predict with similar efficiency for other nuclear data. Further, the ML algorithms learn better with more number of relevant features. In

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