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Machine Learning for Infinite Neutron Matter at Finite Temperatures

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Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutors: Arnau Rios Huguet, Javier Rozalén Sarmiento

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In this paper, machine learning is used to model the imaginary part of the self-energy of a neutron-star binary system, which is approximated as infinite neutron matter. The performance of the model is evaluated by computing physical properties such as the momentum distribution and the total energy. The results obtained with the model show a high degree of accuracy when compared to theoretical values

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Machine Learning for Infinite Neutron Matter at Finite Temperatures