RT info:eu-repo/semantics/article
T1 Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies
A1 Cepa Nogue, Jorge
A1 Navarro Martínez, Rocío
K1 Galaxy general
K1 Methods statistical
AB 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.
SN 1432-0746
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/35399
UL http://riull.ull.es/xmlui/handle/915/35399
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 27-jun-2024