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


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AKKOYUN S., Yakhelef A.

PHYSICAL REVIEW C, cilt.105, sa.4, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 105 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1103/physrevc.105.044309
  • Dergi Adı: PHYSICAL REVIEW C
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chemical Abstracts Core, INSPEC
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

The region around the doubly magic nuclide Sn-100 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 Sn-101 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 Sn-131 to determine neutron spe. The other approach is the use of the lightest isotope, Sn-107, for which the model space orbitals are determined. In this study, we estimated the spectrum of the Sn-100 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 Sn-100 isotope, the Sn-101 spectrum was obtained. Subsequently, neutron spe of the model space orbitals are defined. Shell-model calculations for Sn102-108 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.