Estimations of (n, p) reaction cross-sections at 14.5 MeV incident neutron energy by artificial neural networks

Precision in medical isotope production: Nuclear model calculations using artificial neural networks

In this groundbreaking study, artificial neural networks (ANNs) are employed to predict the production cross-sections of crucial radioisotopes, namely 18O, 209Bi, 232Th, and 68Zn, via the (p,n) reaction. We employed a comparative approach to validate the ANN model's predictions by comparing them to outputs generated by established nuclear reaction codes (TALYS 1.9, EMPIRE-3.2 (Malta)) and data from the authoritative source, the Experimental Nuclear Reaction Data (EXFOR).Motivated by the increasing demand for radioisotopes in precise medical diagnostics and successful therapies, this study focuses on investigating methods and new techniques for determining production cross-sections with high accuracy, which are crucial for the consistent supply of vital radioisotopes. In line with this objective, the ANN model demonstrated exceptional performance, achieving remarkably high correlation coefficients, exceeding 0.999 for training and all data, and reaching 0.98665 for testing. Supportive of this, the high correlation coefficients indicate that the ANN estimations effectively match experimental data. Significantly, our findings illustrate the potential of ANNs as a promising alternative for estimating the production cross-sections of 18O, 209Bi, 232Th, and 68Zn, with the possibility of extending this application to other medically relevant radioisotopes.

Neural network predictions of (α,n) reaction cross sections at 18.5±3 MeV using the Levenberg-Marquardt algorithm

In recent developments, artificial neural networks (ANNs) have demonstrated their capability to predict reaction cross-sections based on experimental data. Specifically, for predicting (α,n) reaction cross-sections, we meticulously fine-tuned the neural network's performance by optimizing its parameters through the Levenberg-Marquardt algorithm. The effectiveness of this approach is corroborated by notable correlation coefficients; an R-value of 0.90928 for overall correlation, 0.98194 for validation, 0.99981 for testing, and 0.94116 for the comprehensive network prediction. We conducted a rigorous comparison between the results and theoretical computations derived from the TALYS 1.95 nuclear code to validate the predictive accuracy. The mean square error value for artificial neural network results is 7620.92, whereas for TALYS 1.95 calculations, it has been found to be 50,312.74. This comprehensive evaluation process validates the reliability of the ANN based on the Levenberg-Marquardt algorithm in approximating the reaction sections, thus demonstrating its potential for comprehensive investigations. These recent developments confirm the feasibility of using ANN models to gain insight into (α,n) reaction cross-sections.

Estimations for (n,α) reaction cross sections at around 14.5MeV using Levenberg-Marquardt algorithm-based artificial neural network

Prediction of neutron-induced reaction cross-sections at around the 14.5 MeV neutron energy is crucial to calculate nuclear transmutation rates, nuclear heating, and radiation damage from gas formation in fusion reactor technology In this research, the new approach of (n,α) reaction cross-section is presented. It has been assessed by utilizing the artificial neural network (ANN) when compared to more advanced algorithms, the Levenberg-Marquardt algorithm-based ANN can be exceedingly fast. The correlation coefficients for a training R-value of 0.99283, a validation R-value of 0.991190, a testing R-value of 0.97337, and an overall R-value of 0.98515 demonstrate that Levenberg-Marquardt algorithm-based ANN is well suited for this purpose. . The obtained results were compared to theoretical calculations of TALYS 1.95 nuclear code. As a consequence, it has been demonstrated that the ANN model can be used to determine the systemic study for (n, α) reaction cross-sections.