RT info:eu-repo/semantics/article
T1 Galaxy classification: deep learning on the OTELO and COSMOS databases
A1 Cepa Nogue, Jorge
A1 Diego, José A. de
A1 Nadolny, Jakub
A1 Bongiovanni, Ángel
A1 Pović, Mirjana
A1 Pérez García, Ana María
A1 Padilla Torres, Carmen P.
A1 Lara-López, Maritza A.
A1 Cerviño, Miguel
A1 Pérez Martínez, Ricardo
A1 Alfaro, Emilio J.
A1 Castañeda, Héctor O.
A1 Fernández-Lorenzo, Miriam
A1 Gallego, Jesús
A1 González, J. Jesús
A1 González-Serrano, J. Ignacio
A1 Pintos-Castro, Irene
A1 Sánchez-Portal, Miguel
A1 Cedrés, Bernabé
A1 González-Otero, Mauro
A1 Jones, D. Heath
A1 Bland-Hawthorn, Joss
A2 Astrofísica
A2 Grupo de investigación: Evolución de galaxias
K1 galaxies: general
K1 methods: statistical
AB Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution.Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sérsic index or the concentration index.Methods. We used three classification methods for the OTELO database: (1) u − r color separation, (2) linear discriminant analysis using u − r and a shape parameter classification, and (3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data.Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog.Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged data.
YR 2020
FD 2020
LK http://riull.ull.es/xmlui/handle/915/35382
UL http://riull.ull.es/xmlui/handle/915/35382
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 18-may-2024