Galaxy classification: Deep learning on the OTELO and COSMOS databases

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.The authors are grateful to the referee for careful read[1]ing of the paper and valuable suggestions and comments. This work was sup[1]ported by the project Evolution of Galaxies, of reference AYA2014-58861-C3- 1-P and AYA2017-88007-C3-1-P, within the “Programa estatal de fomento de la investigacion cientifica y tecnica de excelencia del Plan Estatal de Investiga[1]cion Cientifica y Tecnica y de Innovacion (2013–2016)” of the “Agencia Estatal de Investigacion del Ministerio de Ciencia, Innovacion y Universidades”, and co-financed by the FEDER “Fondo Europeo de Desarrollo Regional”. JAD is grateful for the support from the UNAM-DGAPA-PASPA 2019 program, the UNAM-CIC, the Canary Islands CIE: Tricontinental Atlantic Campus 2017, and the kind hospitality of the IAC. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Innovation and Technology (MoIT), and from the Spanish Min[1]istry of Economy and Competitiveness (MINECO) through projects AYA2013- 42227-P and AYA2016-76682C3-1-P. APG, MSP and RPM were supported by the PNAYA project: AYA2017–88007–C3–2–P. MC and APG are also funded by Spanish State Research Agency grant MDM-2017-0737 (Unidad de Exce[1]lencia María de Maeztu CAB). EJA acknowledges support from the Spanish Government Ministerio de Ciencia, Innovación y Universidades though grant PGC2018-095049-B-C21. M.P. and E.J.A. also acknowledge support from the State Agency for Research of the Spanish MCIU through the Center of Excel[1]lence Severo Ochoa award for the Instituto de Astrofísica de Andalucía (SEV[1]2017-0709). JG receives support through the project AyA2018-RTI-096188-B[1]100. MALL acknowledges support from the Carlsberg Foundation via a Sem[1]per Ardens grant (CF15-0384). JIGS receives support through the Proyecto Puente 52.JU25.64661 (2018) funded by Sodercan S.A. and the Universidad de Cantabria, and PGC2018–099705–B–100 funded by the Ministerio de Ciencia, Innovación y Universidades. Based on observations made with the Gran Tele[1]scopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias, in the island of La Palma. This work is (partly) based on data obtained with the instrument OSIRIS, built by a Consortium led by the Instituto de Astrofísica de Canarias in collabora[1]tion with the Instituto de Astronomía of the Universidad Autónoma de México. OSIRIS was funded by GRANTECAN and the National Plan of Astronomy and Astrophysics of the Spanish Government