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  • Küçük Resim
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    Applications of different machine learning methods on nuclear charge radius estimations
    (Institute of Physics, 2023) Bayram, Tuncay; Yeşilkanat, Cafer Mert; Akkoyun, Serkan
    Theoretical models come into play when the radius of nuclear charge, one of the most fundamental properties of atomic nuclei, cannot be measured using different experimental techniques. As an alternative to these models, machine learning (ML) can be considered as a different approach. In this study, ML techniques were performed using the experimental charge radius of 933 atomic nuclei (A ≥ 40 and Z ≥ 20) available in the literature. In the calculations in which eight different approaches were discussed, the obtained outcomes were compared with the experimental data, and the success of each ML approach in estimating the charge radius was revealed. As a result of the study, it was seen that the Cubist model approach was more successful than the others. It has also been observed that ML methods do not miss the different behavior in the magic numbers region.
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    Generation of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methods
    (Elsevier B.V., 2024) Akkoyun, Serkan; Yeşilkanat, Cafer Mert; Bayram, Tuncay
    In order to obtain cross-sections of heavy-ion fusion and fusion-evaporation reactions, artificial neural networks, cubist, random forest, support vector regression, extreme gradient boosting, and multiple linear regression machine learning approaches were used separately in this study. The outcomes from these different methods that are obtained from the training carried out with the existing experimental data in the literature were compared. Furthermore, it has been observed that a two-step process yielded better results for determining the heavy-ion reaction cross-sections, after first estimating which approach would be better for which reaction. In this manner, the method for which the cross-section needs to be calculated is determined by the machine learning classification application, and predictions can be made using the machine learning regression application with the determined method. It has been concluded that the obtained results are in harmony with the experimental data and that the methods can be used safely. The obtained results are published on a web page that allows for online calculation of heavy-ion fusion and fusion-evaporation reaction cross-sections.
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    Machine learning predictions for cross-sections of ⁴³,⁴⁴Sc radioisotope production by alpha-induced reactions on Ca target
    (Elsevier B.V., 2024) Akkoyun, Serkan; Yeşilkanat, Cafer Mert; Bayram, Tuncay
    ??,??Sc radioisotopes are an alternative to ¹?F in positron emission tomography. ??,??Sc radioisotopes, which can be generated at low costs by irradiating inexpensive natural Ca with alpha particles, can be produced, and distributed in a central cyclotron facility due to their relatively long half-lives. Since there is limited experimental data on the cross-sections in the literature, in this study, cross-section predictions of the production of ??,??Sc radioisotopes with alpha particles on Ca target were carried out with different machine learning approaches. In order to improve the results, the feature engineering method was applied to the variables of the cross-section predictions. Moreover, predictions have been improved with Stacked Ensemble Learning (SEL) approaches, a complex methodology that leverages the predictive capabilities of multiple underlying models to build a higher-level metamodel. We found that the best results were obtained with Bayesian Regularized Neural Network, Support Vector Regression and Stacked Ensemble Learning methods.