TY - JOUR

T1 - Galaxy classification

T2 - Deep learning on the OTELO and COSMOS databases

AU - De DIego, José A.

AU - Nadolny, Jakub

AU - Bongiovanni, Ángel

AU - Cepa, Jordi

AU - Pović, Mirjana

AU - Pérez García, Ana María

AU - Padilla Torres, Carmen P.

AU - Lara-López, Maritza A.

AU - Cerviño, Miguel

AU - Martínez, Ricardo Pérez

AU - Alfaro, Emilio J.

AU - Castañeda, Héctor O.

AU - Fernández-Lorenzo, Miriam

AU - Gallego, Jesús

AU - González, J. Jesús

AU - González-Serrano, J. Ignacio

AU - Pintos-Castro, Irene

AU - Sánchez-Portal, Miguel

AU - Cedrés, Bernabe&acute

AU - González-Otero, Mauro

AU - Heath Jones, D.

AU - Bland-Hawthorn, Joss

N1 - Publisher Copyright: © ESO 2020.

PY - 2020

Y1 - 2020

N2 - 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.

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.

KW - Galaxies: general

KW - Methods: statistical

U2 - 10.1051/0004-6361/202037697

DO - 10.1051/0004-6361/202037697

M3 - Journal article

AN - SCOPUS:85088259724

VL - 638

JO - Astronomy & Astrophysics

JF - Astronomy & Astrophysics

SN - 0004-6361

M1 - A134

ER -