Classification of galaxies is traditionally associated with their morphologies through visual
inspection of images. The amount of data to come render this task, inhuman and Machine …

We apply four statistical learning methods to a sample of 7941 galaxies (z< 0.06) from the
Galaxy And Mass Assembly survey to test the feasibility of using automated algorithms to …

We explore unsupervised machine learning for galaxy morphology analyses using a
combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) …

Context. The huge and still rapidly growing amount of galaxies in modern sky surveys raises
the need for an automated and objective classification method. Unsupervised learning …

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

Galaxy morphology offers significant insights into the evolutionary pathways and underlying
physics of galaxies. As astronomical data grow with surveys such as Euclid and Vera C …

We present an unsupervised machine learning technique that automatically segments and
labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous …

Given the enormous galaxy data bases of modern sky surveys, parametrizing galaxy
morphologies is a very challenging task due to the huge number and variety of objects. We …

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 …

Galaxy morphology is a fundamental quantity, which is essential not only for the full
spectrum of galaxy-evolution studies, but also for a plethora of science in observational …