Abstract
ABSTRACT We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100 ,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set.
Highlights
Galaxy morphology is linked to the stellar populations of galaxies, providing essential clues to their formation history and evolution
With the convolutional neural networks (CNNs) trained with the subset of the Dark Energy Survey (DES) Y1 data with the Galaxy Zoo 1 (GZ1) labels corrected in C20 (Sections 2.2 and 2.3), we provide one of the largest catalogues to date with galaxy morphological classifications for over million galaxies from the DES Y3 data (16 ≤ i < and z < 1.0; Section 2.4; along with the companion catalogue produced by Vega-Ferrero et al 2021)
We present in this paper one of the largest galaxy morphological classification catalogues produced to date (along with the other DES catalogue presented in Vega-Ferrero et al (2021), using the DES Y3 data with over 20 million galaxies
Summary
Galaxy morphology is linked to the stellar populations of galaxies, providing essential clues to their formation history and evolution. Dieleman, Willett & Dambre 2015; Huertas-Company et al 2015, 2018; Domınguez Sanchez et al 2018; Cheng et al 2020a; Ghosh et al 2020; Hausen & Robertson 2020; Walmsley et al 2020) In this new study, we apply the CNN set up and calibration investigated and assembled in Cheng et al (2020a, hereafter, C20) to predict probabilities of binary galaxy morphological classification for the DES Year 3 GOLD data (hereafter, the DES Y3 data; Sevilla-Noarbe et al 2020). The comparison of the two studies is ongoing and will provide a solid validation in morphological classification of the overlap samples using the different approaches This will give an insight for future deep learning applications in galaxy morphological classification, but this type of detail is beyond the scope of this catalogue paper.
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