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.