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Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

Abstract: Context. 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.

 Fuente: Astronomy & Astrophysics, 2021, 655, A56

 Editorial: EDP Sciences

 Fecha de publicación: 01/11/2021

 Nº de páginas: 12

 Tipo de publicación: Artículo de Revista

 DOI: 10.1051/0004-6361/202141360

 ISSN: 0004-6361,1432-0746

 Proyecto español: AYA2014-58861-C3-1-P

 Url de la publicación: https://doi.org/10.1051/0004-6361/202141360

Autoría

DIEGO, J.A.

NADOLNY, J.

BONGIOVANNI, A.

CEPA , J.

LARA-LÓSPEZ, M.A.

GALLEGO, J.

CERVIÑO, M.

SÁNCHEZ PORTAL, M.

POVIC, M.

PÉREZ GARCÍA, A.M.

PÉREZ MARTÍNEZ, R.

PADILLA TORRES, C.P.

CEDRÉS, B.

GARCÍA-AGUILA, R D.

GONZÁLEZ, J.J.

GONZÁLEZ OTERO, M.

NAVARRO-MARTÍNEZ, R.