Galaxy morphology reflects structural properties that contribute to the understanding of the
formation and evolution of galaxies. Deep convolutional networks have proven to be very …

The ongoing and forthcoming surveys will result in an unprecedented increase in the
number of observed galaxies. As a result, data-driven techniques are now the primary …

Context. The classification of galaxy morphology is among the most active fields in
astronomical research today. With the development of artificial intelligence technology, deep …

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 …

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 …

Classification of galaxy morphology is a hot issue in astronomical research. Although
significant progress has been made in the last decade in classifying galaxy morphology …

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 …

Quantifying the morphology of galaxies has been an important task in astrophysics to
understand the formation and evolution of galaxies. In recent years, the data size has been …

Understanding morphological types of galaxies is a key parameter for studying their
formation and evolution. Neural networks that have been used previously for galaxy …

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 …