Astronomy and Astrophysics
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. ARRAY(0x2881840)
Galaxy evolution is a crucial topic in modern extragalactic astrophysics, linking cosmology to the Local Universe. Their study requires collecting statistically significant samples of galaxies of different luminosities at different distances. It implies the ability to observe faint objects using different techniques, and at different wavelengths