Improving galaxy morphologies for SDSS with Deep Learning

Abstract We present a morphological catalogue for ∼670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair 97 per cent), precision and recall values (90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.