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 Sersic 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
