The CNN classification of galaxies by their image morphological peculiarities

Multidimensional mathematical analysis, like Machine Learning techniques, determines the different features of objects, which is difficult for the human mind. We create a machine learning model to predict galaxies' detailed morphology (∼ 300000 SDSS-galaxies with z < 0.1) and train it on a labeled dataset defined within the Galaxy Zoo 2 (GZ2). We use convolutional neural networks (CNNs) to classify the galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral) and 34 morphological classes attaining >94% of accuracy for five-class morphology prediction except for the cigar-shaped (∼ 87%) galaxies.