Estimations of giant dipole resonance parameters using artificial neural network

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.

Effects of deuteron optical models on the cross-section calculations of deuteron induced reactions on natural germanium

A common feature of scientific studies is that when experimental observation data are not available, theoretical calculations are used to obtain information about the subject under investigation. In this context, many parameters and theoretical models have been developed that can be used in nuclear physics studies just as it is in other branches of sciences. It is intended that by doing so, theoretical models can be improved using recent experimental data while also learning about outcomes where experimental data is unavailable or difficult to access. Among the many theoretical models available, there are also deuteron optical models whose effects are examined in this study. The objective of this study is to examine the effects of different deuteron optical models on the cross-section calculations of deuteron induced reactions on natural germanium. The cross-section values of ^natGe(d,x)⁷⁰As, ^natGe(d,x)⁷¹As, ^natGe(d,x)⁷²As, ^natGe(d,x)⁷³As, ^natGe(d,x)⁷⁴As and ^natGe(d,x)⁷⁶As reactions were calculated using five deuteron optical models in the TALYS code's v1.95 for this aim, and the results were compared to the experimental data available in the database known as Experimental Nuclear Reaction Data (EXFOR) library. Graphics and quantitative analyses were also used to present the findings and interpretations of the outcomes.