Galaxy classification: deep learning on the OTELO and COSMOS databases

¹ Instituto de Astronomía, Universidad Nacional Autónoma de México, Apdo. Postal 70-264, 04510 Ciudad de México, Mexico
e-mail: jdo@astro.unam.mx
² Instituto de Astrofisica de Canarias (IAC), 38200 La Laguna, Tenerife, Spain
³ Departamento de Astrofisica, Universidad de La Laguna (ULL), 38205 La Laguna, Tenerife, Spain
⁴ Instituto de Radioastronomía Milimétrica (IRAM), Av. Divina Pastora 7, Local 20, 18012 Granada, Spain
⁵ Asociacion Astrofisica para la Promocion de la Investigacion, Instrumentacion y su Desarrollo, ASPID, 38205 La Laguna, Tenerife, Spain
⁶ Ethiopian Space Science and Technology Institute (ESSTI), Entoto Observatory and Research Center (EORC), Astronomy and Astrophysics Research Division, PO Box 33679 Addis Ababa, Ethiopia
⁷ Instituto de Astrofísica de Andalucía, CSIC, Glorieta de la Astronomía s/n, 18080 Granada, Spain
⁸ Depto. Astrofísica, Centro de Astrobiología (INTA-CSIC), ESAC Campus, Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Spain
⁹ Fundación Galileo Galilei, Telescopio Nazionale Galileo, Rambla José Ana Fernández Pérez, 7, 38712 Breña Baja, Santa Cruz de la Palma, Spain
¹⁰ DARK, Niels Bohr Institute, University of Copenhagen, Lyngbyvej 2, Copenhagen 2100, Denmark
¹¹ ISDEFE for European Space Astronomy Centre (ESAC)/ESA, PO Box 78 28690 Villanueva de la Cañada, Madrid, Spain
¹² Departamento de Fisica, Escuela Superior de Fisica y Matematicas, Instituto Politécnico Nacional, Mexico DF, Mexico
¹³ Departamento de Física de la Tierra y Astrofísica, Facultad CC Físicas, Instituto de Física de Partículas y del Cosmos, IPARCOS, Universidad Complutense de Madrid, 28040 Madrid, Spain
¹⁴ Instituto de Fisica de Cantabria (CSIC-Universidad de Cantabria), 39005 Santander, Spain
¹⁵ Department of Astronomy & Astrophysics, University of Toronto, Toronto, Canada
¹⁶ English Language and Foundation Studies Centre, University of Newcastle, Callaghan, NSW 2308, Australia
¹⁷ Sydney Institute for Astronomy, School of Physics, University of Sydney, Sydney, NSW 2006, Australia

Received: 10 February 2020
Accepted: 25 April 2020

Abstract

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.