Improving galaxy morphologies for SDSS with Deep Learning

TL;DR

This paper introduces a large, accurate galaxy morphological catalogue for SDSS galaxies using deep learning, specifically CNNs, to classify galaxy types and features with high precision, improving upon previous methods.

Contribution

The paper presents the largest and most accurate galaxy morphological catalogue to date, utilizing CNNs trained on existing visual classifications for detailed galaxy morphology analysis.

Findings

CNN models achieved >97% accuracy on GZ2 questions.

T-Type classification showed smaller offset and scatter than previous models.

The catalogue is publicly available for further research.

Abstract

We present a morphological catalogue for $\sim$ 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 & Abraham (2010), for training CNNs with colour images in order to obtain T-Types and a series of GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge prominence, roundness and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-Type model is not so efficient. For the T-Type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (> 97\%), precision and recall values (> 90\%) when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.