JAMC
Theoretical Study on the Calculation of the Mass of the Nucleus 103Sn
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Abstract
The mass of an atomic nucleus holds significant importance in the fields of nuclear structure and nuclear astrophysics. Accurate mass predictions of radionuclides, which are often challenging to measure experimentally, are crucial for advancing research in these areas. Machine learning, with its ability to process and analyze large datasets effectively, is particularly suited for this task. This study integrates insights from nuclear physics and computer science, employing various machine learning algorithms to predict the mass of the nucleus 103Sn. These algorithms utilize inputs such as the number of protons, neutrons, and other experimental measurements. The objective is to optimize the algorithm for the best-fitting residuals and predictive accuracy. Additionally, the performance of these machine learning models is compared with traditional theoretical nuclear models to highlight differences and potential improvements. This comparative analysis helps in understanding the advantages of using machine learning techniques over conventional methods in nuclear mass predictions.
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How to cite this paper
Theoretical Study on the Calculation of the Mass of the Nucleus 103Sn
How to cite this paper: Junhao Liu, Xi Zhao, Xiang Hua. (2024) Theoretical Study on the Calculation of the Mass of the Nucleus 103Sn. Journal of Applied Mathematics and Computation, 8(2), 132-136.
DOI: http://dx.doi.org/10.26855/jamc.2024.06.006
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