The study of (p,γ) radiative capture reactions is crucial for understanding various astrophysical processes and nucleosynthesis in stars. These reactions play a significant role in the formation of elements in the universe and are essential for modeling stellar evolution and energy production. Theoretical and experimental investigations of these reactions offer valuable insights into the underlying nuclear physics and help refine astrophysical models. Key reactions such as 7Be(p,γ)8B, 3He(α,γ)7Be, 3H(α,γ)7Li, 7Li(p,γ)8Be, and 15N(p,γ)16O have been extensively studied, providing important data for astrophysical models. Machine learning (ML) techniques have also been applied to improve the accuracy of predictions and analyses in this field. This study evaluates various ML models for predicting the S(0)-factor for (p,γ) radiative capture reactions. Utilizing a comprehensive data set including both experimental and theoretical data, we performed a detailed analysis and comparison of the astrophysical S factors. Our findings offer a more right and comprehensive understanding of the reaction mechanisms and their implications for stellar nucleosynthesis. However, the study found that the universality of the S factor was not seen as light nuclei reactions due to complex nuclear interactions and sensitivity to specific nuclear structures and reaction mechanisms.
