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 − rcolor separation, (2) linear discriminant analysis usingu − rand a shape parameter classification, and (3) a deep neural network using thermagnitude, 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.
Highlights
Galaxy morphological classification plays a fundamental role in descriptions of the galaxy population in the universe, and in our understanding of galaxy formation and evolution
To determine a minimal set of attributes that are able to classify between ET and LT galaxies, we focus on two directly observable characteristics: photometry and shape
Strateva et al (2001) used photometry from 147 920 Sloan Digital Sky Survey (SDSS) galaxies with magnitude g∗ ≤ 21 and redshifts z 0.4 to build a binary classification model based in the u−r = 2.22 discriminant color, which they tested on a sample of 287 galaxies visually labeled as ET or LT, recovering 94 out of 117 (80%) ET, and 112 out of 170 (66%) LT galaxies
Summary
Galaxy morphological classification plays a fundamental role in descriptions of the galaxy population in the universe, and in our understanding of galaxy formation and evolution. We compare the performance of different galaxy classification techniques applied to a sample of galaxies extracted from the photometric OTELO database (Bongiovanni et al 2019) with a fitted Sérsic profile (Nadolny et al 2020) These techniques are (1) the Strateva et al (2001) u − r color algorithm; (2) the LDA machine learning algorithm, which includes both the u − r color and a shape parameter, either the Sérsic index or the concentration index (Kelvin et al 2012); and (3) a DNN that uses optical and near-infrared photometry, and shape parameter for objects available in both OTELO and COSMOS catalogs.
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