Non-Sequential 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 simultaneously galaxy morphological classification and photometric redshift estimates. Currently, only 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 non-sequential 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 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.
Keywords
Cite
@article{arxiv.2108.09415,
title = {Non-Sequential Neural Network for Simultaneous, Consistent Classification and Photometric Redshifts of OTELO Galaxies},
author = {José A. de Diego and Jakub Nadolny and Ángel Bongiovanni and Jordi Cepa and Maritza A. Lara-López and Jesús Gallego and Miguel Cerviño and Miguel Sánchez-Porta and J. Ignacio González-Serrano and Emilio J. Alfaro and Mirjana Pović and Ana María Pérez García and Ricardo Pérez Martínez and Carmen P. Padilla Torres and Bernabé Cedrés and Diego García-Aguilar and J. Jesús González and Mauro González-Otero and Rocío Navarro-Martínez and Irene Pintos-Castro},
journal= {arXiv preprint arXiv:2108.09415},
year = {2021}
}
Comments
Astronomy and Astrophysics (A&A) accepted