# name
Nuclear mass predictions based on convolutional neural networks

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Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutor: Arnau Rios Huguet

# metadata.dc.description.abstract[].value
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

# metadata.dc.title[].value
Nuclear mass predictions based on convolutional neural networks