Improving galaxy morphologies for SDSS with Deep Learning

Understand

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).