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https://hdl.handle.net/10316/111901
Título: | Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks | Autor: | Carvalho, Valéria Ferreira, Márcio Malik, Tuhin Providência, Constança |
Palavras-chave: | Nuclear Theory; astro-ph.HE | Data: | 12-Jun-2023 | Editora: | American Physical Society | Projeto: | UIDP/04564/2020 UIDB/04564/2020 2022.06460.PTDC |
Título da revista, periódico, livro ou evento: | Physical Review D | Volume: | 108 | Número: | 4 | Resumo: | We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties. By analyzing a set of simulated observations, namely NS radius and tidal deformability, we leverage BNNs as effective tools for inferring the proton fraction and sound speed within NS interiors. To achieve this, several BNNs models were developed upon a dataset of $\sim$ 25K nuclear EoS within a relativistic mean-field framework, obtained through Bayesian inference that adheres to minimal low-density constraints. Unlike conventional neural networks, BNNs possess an exceptional quality: they provide a prediction uncertainty measure. To simulate the inherent imperfections present in real-world observations, we have generated four distinct training and testing datasets that replicate specific observational uncertainties. Our initial results demonstrate that BNNs successfully recover the composition with reasonable levels of uncertainty. Furthermore, using mock data prepared with the DD2, a different class of relativistic mean-field model utilized during training, the BNN model effectively retrieves the proton fraction and speed of sound for neutron star matter. | Descrição: | 16 pages, 15 figures, published version | URI: | https://hdl.handle.net/10316/111901 | ISSN: | 2470-0010 2470-0029 |
DOI: | 10.1103/PhysRevD.108.043031 | Direitos: | openAccess |
Aparece nas coleções: | I&D CFis - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Decoding neutron star observations Revealing composition through Bayesian neural networks.pdf | 1.98 MB | Adobe PDF | Ver/Abrir |
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