Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200635344> ?p ?o ?g. }
Showing items 1 to 60 of
60
with 100 items per page.
- W4200635344 abstract "One of the most significant challenges involved in efforts to understand the equation of state of dense neutron-rich matter is the uncertain density dependence of the nuclear symmetry energy. Because of its broad impact, pinning down the density dependence of the nuclear symmetry energy has been a longstanding goal of both nuclear physics and astrophysics. Recent observations of neutron stars, in both electromagnetic and gravitational-wave spectra, have already constrained significantly the nuclear symmetry energy at high densities. Training deep neural networks to learn a computationally efficient representation of the mapping between astrophysical observables of neutron stars, such as masses, radii, and tidal deformabilities, and the nuclear symmetry energy allows its density dependence to be determined reliably and accurately. In this work we use a deep learning approach to determine the nuclear symmetry energy as a function of density directly from observational neutron star data. We show for the first time that artificial neural networks can precisely reconstruct the nuclear symmetry energy from a set of available neutron star observables, such as, masses and radii as those measured by, e.g., the NICER mission, or masses and tidal deformabilities as measured by the LIGO/VIRGO/KAGRA gravitational-wave detectors. These results demonstrate the potential of artificial neural networks to reconstruct the symmetry energy, and the equation of state, directly from neutron star observational data, and emphasize the importance of the deep learning approach in the era of Multi-Messenger Astrophysics." @default.
- W4200635344 created "2021-12-31" @default.
- W4200635344 creator A5067452947 @default.
- W4200635344 date "2021-12-07" @default.
- W4200635344 modified "2023-09-24" @default.
- W4200635344 title "Translating neutron star observations to nuclear symmetry energy via artificial neural networks" @default.
- W4200635344 hasPublicationYear "2021" @default.
- W4200635344 type Work @default.
- W4200635344 citedByCount "0" @default.
- W4200635344 crossrefType "posted-content" @default.
- W4200635344 hasAuthorship W4200635344A5067452947 @default.
- W4200635344 hasBestOaLocation W42006353441 @default.
- W4200635344 hasConcept C121332964 @default.
- W4200635344 hasConcept C152568617 @default.
- W4200635344 hasConcept C165410206 @default.
- W4200635344 hasConcept C172695262 @default.
- W4200635344 hasConcept C185544564 @default.
- W4200635344 hasConcept C190330329 @default.
- W4200635344 hasConcept C192887742 @default.
- W4200635344 hasConcept C2524010 @default.
- W4200635344 hasConcept C2779886137 @default.
- W4200635344 hasConcept C2780688901 @default.
- W4200635344 hasConcept C32848918 @default.
- W4200635344 hasConcept C33923547 @default.
- W4200635344 hasConcept C44870925 @default.
- W4200635344 hasConcept C53810900 @default.
- W4200635344 hasConcept C54116275 @default.
- W4200635344 hasConcept C62520636 @default.
- W4200635344 hasConceptScore W4200635344C121332964 @default.
- W4200635344 hasConceptScore W4200635344C152568617 @default.
- W4200635344 hasConceptScore W4200635344C165410206 @default.
- W4200635344 hasConceptScore W4200635344C172695262 @default.
- W4200635344 hasConceptScore W4200635344C185544564 @default.
- W4200635344 hasConceptScore W4200635344C190330329 @default.
- W4200635344 hasConceptScore W4200635344C192887742 @default.
- W4200635344 hasConceptScore W4200635344C2524010 @default.
- W4200635344 hasConceptScore W4200635344C2779886137 @default.
- W4200635344 hasConceptScore W4200635344C2780688901 @default.
- W4200635344 hasConceptScore W4200635344C32848918 @default.
- W4200635344 hasConceptScore W4200635344C33923547 @default.
- W4200635344 hasConceptScore W4200635344C44870925 @default.
- W4200635344 hasConceptScore W4200635344C53810900 @default.
- W4200635344 hasConceptScore W4200635344C54116275 @default.
- W4200635344 hasConceptScore W4200635344C62520636 @default.
- W4200635344 hasLocation W42006353441 @default.
- W4200635344 hasOpenAccess W4200635344 @default.
- W4200635344 hasPrimaryLocation W42006353441 @default.
- W4200635344 hasRelatedWork W18393889 @default.
- W4200635344 hasRelatedWork W20000564 @default.
- W4200635344 hasRelatedWork W24742048 @default.
- W4200635344 hasRelatedWork W2734591 @default.
- W4200635344 hasRelatedWork W29246877 @default.
- W4200635344 hasRelatedWork W3656385 @default.
- W4200635344 hasRelatedWork W36862555 @default.
- W4200635344 hasRelatedWork W39460821 @default.
- W4200635344 hasRelatedWork W41315093 @default.
- W4200635344 hasRelatedWork W42791884 @default.
- W4200635344 isParatext "false" @default.
- W4200635344 isRetracted "false" @default.
- W4200635344 workType "article" @default.