Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks

Bibliographic Details
Main Author: Carvalho, Valéria
Publication Date: 2023
Other Authors: Ferreira, Márcio, Malik, Tuhin, Providência, Constança
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/10316/111901
https://doi.org/10.1103/PhysRevD.108.043031
Summary: 16 pages, 15 figures, published version
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spelling Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural NetworksNuclear Theoryastro-ph.HE16 pages, 15 figures, published versionWe 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.American Physical Society2023-06-12info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://hdl.handle.net/10316/111901https://hdl.handle.net/10316/111901https://doi.org/10.1103/PhysRevD.108.043031eng2470-00102470-0029http://arxiv.org/abs/2306.06929v2Carvalho, ValériaFerreira, MárcioMalik, TuhinProvidência, Constançainfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-01-16T10:54:24Zoai:estudogeral.uc.pt:10316/111901Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T06:04:14.822744Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
title Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
spellingShingle Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
Carvalho, Valéria
Nuclear Theory
astro-ph.HE
title_short Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
title_full Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
title_fullStr Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
title_full_unstemmed Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
title_sort Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
author Carvalho, Valéria
author_facet Carvalho, Valéria
Ferreira, Márcio
Malik, Tuhin
Providência, Constança
author_role author
author2 Ferreira, Márcio
Malik, Tuhin
Providência, Constança
author2_role author
author
author
dc.contributor.author.fl_str_mv Carvalho, Valéria
Ferreira, Márcio
Malik, Tuhin
Providência, Constança
dc.subject.por.fl_str_mv Nuclear Theory
astro-ph.HE
topic Nuclear Theory
astro-ph.HE
description 16 pages, 15 figures, published version
publishDate 2023
dc.date.none.fl_str_mv 2023-06-12
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10316/111901
https://hdl.handle.net/10316/111901
https://doi.org/10.1103/PhysRevD.108.043031
url https://hdl.handle.net/10316/111901
https://doi.org/10.1103/PhysRevD.108.043031
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2470-0010
2470-0029
http://arxiv.org/abs/2306.06929v2
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833602566503530497

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