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    Neural network predictions of (n,2n) reaction cross-sections at 14.6 MeV incident neutron energy
    (Pergamon-Elsevier Science Ltd, 2023) Akkoyun, Serkan; Amrani, Naima; Bayram, Tuncay
    In this study, we have estimated the (n,2n) reaction cross-section for 14.6 MeV incident neutron energy by using the artificial neural network (ANN) method. We have also predicted the reaction cross-sections whose experi-mental data are not available in the literature. For the construction of the present ANN, available experimental data in the literature has been borrowed. The ANN estimations have been compared with the available exper-imental data and the results from a theoretical calculation and the two commonly used computer codes. Ac-cording to the results that the ANN results are in good agreement with the experimental data than the codes and this shows that the method can be a powerful tool for the estimation of cross-section data for the neutron-induced reactions. Considering the predictions of the ANN of the cross-sections whose experimental data are not available in the literature, it is seen that they are in line with the trend of the experimental data, but far from the results given by the theoretical calculations and two computer codes.
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    Neutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches
    (Springer, 2024) Amrani, Naima; Yesilkanat, Cafer Mert; Akkoyun, Serkan
    This study focuses on leveraging powerful machine learning approaches to determine neutron- alpha reaction cross-sections within the 14-15 MeV energy range. The investigation utilizes an experimental dataset comprising measurements of 133 nuclei concerning (n, alpha) reaction cross- sections. These data are divided into training and validation subsets, following established protocols, with 80% allocated for model training and 20% for testing. Key nucleus characteristics, including neutron number (N), mass number (A), and symmetry representation [(N-Z)(2)/A], were used as input variables for the machine learning models. SVR and XGBoost methods showed superior performance among the other machine learning methods used in the present study. In addition, a machine learning based online calculation tool was developed to estimate the reaction cross section.