Morphological classification is a key piece of information to define samples of galaxies
aiming to study the large-scale structure of the universe. In essence, the challenge is to build …

Measuring the morphological parameters of galaxies is a key requirement for studying their
formation and evolution. Surveys such as the Sloan Digital Sky Survey have resulted in the …

Classifying the morphologies of galaxies is an important step in understanding their physical
properties and evolutionary histories. The advent of large-scale surveys has hastened the …

There are several supervised machine learning methods used for the application of
automated morphological classification of galaxies; however, there has not yet been a clear …

In this paper, we apply five different machine learning algorithms to classify samples into
four categories—spirals, ellipticals, mergers and stars (don't know) using data from the …

Deep learning (DL) algorithms for morphological classification of galaxies have proven very
successful, mimicking (or even improving) visual classifications. However, these algorithms …

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 propose a variant of residual networks (ResNets) for galaxy morphology classification.
The variant, together with other popular convolutional neural networks (CNNs), is applied to …

We present morphological classifications obtained using machine learning for objects in the
Sloan Digital Sky Survey DR6 that have been classified by Galaxy Zoo into three classes …

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the
scale of data continues to increase with upcoming surveys, traditional classification methods …