In this work, we introduce the curvature of a galaxy brightness profile to identify its structural
subcomponents in a non-parametrically fashion. Bulges, bars, discs, lens, rings, and spiral …

In this paper we present an experimental study of machine learning and image analysis for
performing automated morphological galaxy classification. We used a neural network, and a …

We examine the capability of generative models to produce realistic galaxy images. We
show that mixing generated data with the original data improves the robustness in …

Aims. This work investigates the potential of using the wavelength-dependence of galaxy
structural parameters (Sérsic index, n, and effective radius, R e) to separate galaxies into …

We explore the capability of deep learning to classify cosmic structures. In cosmological
simulations, cosmic volumes are segmented into voids, sheets, filaments, and knots …

We present a new exploratory framework to model galaxy formation and evolution in a
hierarchical Universe by using machine learning (ML). Our motivations are two-fold:(1) …

The large number of galaxies imaged by digital sky surveys reinforces the need for
computational methods for analyzing galaxy morphology. While the morphology of most …

We applied the image-based approach with a convolutional neural network model to the
sample of low-redshifts galaxies with $-24^{m}< M_ {r}<-19.4^{m} $ from the SDSS DR9. We …

We investigate the utility of a variety of features in performing morphological galaxy
classification using back-propagation neural network classifiers based on a sample of 805 …

Among the many celestial objects in the universe, galaxies offer insights as to how the
universe was formed and is continuing to develop. The morphological classification of …