A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network

Ozdogan H., ÜNCÜ Y. A. , ŞEKERCİ M., KAPLAN A.

MODERN PHYSICS LETTERS A, vol.36, no.23, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 23
  • Publication Date: 2021
  • Doi Number: 10.1142/s0217732321501686
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, INSPEC, zbMATH
  • Keywords: (n, t) reaction cross-section, artificial neural network, Levenberg-Marquardt, TALYS 1.95, LEVEL DENSITY MODELS, SEMIEMPIRICAL SYSTEMATICS, FEEDFORWARD NETWORKS, STRENGTH FUNCTIONS, ENERGY


In this paper, calculations of the (n,t) reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg-Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression (R) values for the ANN estimation have been determined as 0.9998, 0.9927 and 0.9895 for training, testing and for all process. The (n,t) reaction cross-sections have been reproduced with the TALYS 1.95 and the EMPIRE 3.2 codes. In summary, it has been demonstrated that the ANN algorithms can be used to calculate the (n,t) reaction cross-section with the semi-empirical systematics.