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dc.contributor.authorCepa Nogue, Jorge 
dc.contributor.authorNavarro Martínez, Rocío
dc.date.accessioned2024-01-16T21:06:46Z
dc.date.available2024-01-16T21:06:46Z
dc.date.issued2021
dc.identifier.issn1432-0746
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/35399
dc.description.abstractContext. Computational techniques are essential for mining large databases produced in modern surveys with value-added products. Aims. This paper presents a machine learning procedure to carry out a galaxy morphological classification and photometric redshift estimates simultaneously. Currently, only a spectral energy distribution (SED) fitting has been used to obtain these results all at once. Methods. We used the ancillary data gathered in the OTELO catalog and designed a nonsequential neural network that accepts optical and near-infrared photometry as input. The network transfers the results of the morphological classification task to the redshift fitting process to ensure consistency between both procedures. Results. The results successfully recover the morphological classification and the redshifts of the test sample, reducing catastrophic redshift outliers produced by an SED fitting and avoiding possible discrepancies between independent classification and redshift estimates. Our technique may be adapted to include galaxy images to improve the classification.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesAstronomy & Astrophysics, V. 655, A56, 2021
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleNonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1051/0004-6361/202141360
dc.subject.keywordGalaxy general
dc.subject.keywordMethods statistical


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