Artificial-intelligence-supported shell-model calculations for light Sn isotopes

Abstract

The region around the doubly magic nuclide 100 Sn is very interesting for nuclear physics studies in terms of structure, reaction, and nuclear astrophysics. The main ingredients in nuclear structure studies using the shell model are the single-particle energies (spe) and the two-body matrix elements. To obtain the former, experimental data of 101 Sn isotope spectrum are necessary. Since there are not enough experimental data, different approaches are used in the literature to obtain spe. In the sn100pn interaction, the hole excitation spectrum was used in 131 Sn to determine neutron spe. The other approach is the use of the lightest isotope, 107 Sn , for which the model space orbitals are determined. In this study, we estimated the spectrum of the 101 Sn isotope by an artificial neural network method in order to obtain neutron spe. After the training was carried out by using the experimental spectra of the nuclei around the 100 Sn isotope, the 101 Sn spectrum was obtained. Subsequently, neutron spe of the model space orbitals are defined. Shell-model calculations for 102 – 108 Sn isotopes were carried out and results are compared to the experimental data and results obtained using the widely used interaction in the region, sn100pn. According to the results, it is seen that the Sn isotope spectra obtained with the new spe values are more compatible with the experimental data.