Estimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches

Yesilkanat C. M., AKKOYUN S.

Journal of Physics G: Nuclear and Particle Physics, cilt.50, sa.5, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 5
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1088/1361-6471/acbaaf
  • Dergi Adı: Journal of Physics G: Nuclear and Particle Physics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Chemical Abstracts Core, INSPEC, DIALNET
  • Anahtar Kelimeler: fission barrier, super-heavy nuclei, extreme gradient boosting, random forest, support vector regression, cubist, artificial neural network
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

With the fission barrier height information, the survival probabilities of super-heavy nuclei can also be reached. Therefore, it is important to have accurate knowledge of fission barriers, for example, the discovery of super-heavy nuclei in the stability island in the super-heavy nuclei region. In this study, five machine learning techniques, Cubist model, Random Forest, support vector regression, extreme gradient boosting and artificial neural network were used to accurately predict the fission barriers of 330 even-even super-heavy nuclei in the region 140 ≤ N ≤ 216 with proton numbers between 92 and 120. The obtained results were compared both among themselves and with other theoretical model calculation estimates and experimental results. According to the results obtained, it was concluded that the Cubist model, support vector regression and extreme gradient boosting methods generally gave better results and could be a better tool for estimating fission barrier heights.