Astronomy and Astrophysics
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.
Galaxy evolution is a crucial topic in modern extragalactic astrophysics, linking cosmology to the Local Universe. Their study requires collecting statistically significant samples of galaxies of different luminosities at different distances. It implies the ability to observe faint objects using different techniques, and at different wavelengths