Please use this identifier to cite or link to this item:
https://hdl.handle.net/2445/223132
Title: | Nuclear mass predictions based on convolutional neural networks |
Author: | Morales De León, David |
Director/Tutor: | Rios Huguet, Arnau |
Keywords: | Xarxes neuronals convolucionals Model de la gota líquida Treballs de fi de grau Convolutional neural networks Liquid drop model Bachelor's theses |
Issue Date: | Jan-2025 |
Abstract: | The precise determination of nuclear masses is essential for understanding atomic nuclei and for applications in astrophysics and nuclear energy. Traditional models like the liquid drop model, with a root mean squared error of σ = 3.94 MeV, fail to meet the accuracy of 100 keV required for nuclear astrophysics research. This work introduces a novel approach by implementing a convolutional neural network (CNN) and leveraging the spatial structure of the nuclide chart. Two models, I3 and I4, are trained and tested on the AME2016 database, achieving values of σ = 0.67 MeV and σ = 0.49 MeV, respectively. Extrapolating to the new nuclei of the AME2020 database, they hold values of σ = 0.64 MeV and σ = 0.57 MeV, demonstrating strong generalization capabilities and proving that CNNs constitute a powerful tool for accurate nuclear mass predictions |
Note: | Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutor: Arnau Rios Huguet |
URI: | https://hdl.handle.net/2445/223132 |
Appears in Collections: | Treballs Finals de Grau (TFG) - Física |
Files in This Item:
File | Description | Size | Format | |
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TFG-Morales-DeLeón-David.pdf | 2.96 MB | Adobe PDF | View/Open |
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