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dc.contributor.authorDafonte, Carlos
dc.contributor.authorFustes, D.
dc.contributor.authorManteiga, Minia
dc.contributor.authorGarabato, D.
dc.contributor.authorÁlvarez, M. A.
dc.contributor.authorUlla, A.
dc.contributor.authorAllende-Prieto, Carlos
dc.date.accessioned2024-02-05T12:52:08Z
dc.date.available2024-02-05T12:52:08Z
dc.date.issued2016-10
dc.identifier.citationC. Dafonte, et al., "On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra", Astronomy & Astrophysics, vol. 594, A68, Oct. 2016, doi: 10.1051/0004-6361/201527045es_ES
dc.identifier.urihttp://hdl.handle.net/2183/35395
dc.description© ESO, 2016. This is the accepted version of the article published by EDP Sciences at: https://doi.org/10.1051/0004-6361/201527045es_ES
dc.description.abstract[Abstract]: Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case) to the given inputs (spectra). Such a model can be integrated in a Bayesian framework to estimate the posterior distribution of the outputs. Methods. The architecture of the GANN follows the same scheme as a normal ANN, but with the inputs and outputs inverted. We train the network with the set of atmospheric parameters (Teff, log g, [Fe/H] and [α/Fe]), obtaining the stellar spectra for such inputs. The residuals between the spectra in the grid and the estimated spectra are minimized using a validation dataset to keep solutions as general as possible. Results. The performance of both conventional ANNs and GANNs to estimate the stellar parameters as a function of the star brightness is presented and compared for different Galactic populations. GANNs provide significantly improved parameterizations for early and intermediate spectral types with rich and intermediate metallicities. The behaviour of both algorithms is very similar for our sample of late-type stars, obtaining residuals in the derivation of [Fe/H] and [α/Fe] below 0.1 dex for stars with Gaia magnitude Grvs < 12, which accounts for a number in the order of four million stars to be observed by the Radial Velocity Spectrograph of the Gaia satellite. Conclusions. Uncertainty estimation of computed astrophysical parameters is crucial for the validation of the parameterization itself and for the subsequent exploitation by the astronomical community. GANNs produce not only the parameters for a given spectrum, but a goodness-of-fit between the observed spectrum and the predicted one for a given set of parameters. Moreover, they allow us to obtain the full posterior distribution over the astrophysical parameters space once a noise model is assumed. This can be used for novelty detection and quality assessment.es_ES
dc.description.sponsorshipThis work was supported by the Spanish FEDER through Grants ESP-2014-55996-C2-2-R, and AYA2015-71820-REDT. A.U. acknowledges partial financial support from the Spanish MECD, under grant PRX15/0051, to entitle a research visit to the Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK.es_ES
dc.language.isoenges_ES
dc.publisherEDP Scienceses_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP-2014-55996-C2-2-R/ES/TECNICAS DE INTELIGENCIA ARTIFICIAL PARA LA EXPLOTACION DEL CATALOGO DE GAIA: ANALYSIS, VALIDACION Y VISUALIZACIONes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AYA2015-71820-REDT/ES/RED ESPAÑOLA DE EXPLOTACION CIENTIFICA DE GAIAes_ES
dc.relationinfo:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PRX15/0051/ES/es_ES
dc.relation.isversionofhttps://doi.org/10.1051/0004-6361/201527045
dc.relation.urihttps://doi.org/10.1051/0004-6361/201527045es_ES
dc.rights© ESO, 2016es_ES
dc.subjectastronomical databaseses_ES
dc.subjectmethods: data analysises_ES
dc.subjectmethods: numericales_ES
dc.subjectGalaxyes_ES
dc.titleOn the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectraes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleAstronomy & Astrophysicses_ES
UDC.volume594es_ES
UDC.issueA68es_ES
dc.identifier.doi10.1051/0004-6361/201527045


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