We present quantified visual morphologies of approximately 48,000 galaxies observed in
three Hubble Space Telescope legacy fields by the Cosmic And Near-infrared Deep …

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

We present in this paper one of the largest galaxy morphological classification catalogues to
date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data …

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

Using the k-means cluster analysis algorithm, we carry out an unsupervised classification of
all galaxy spectra in the seventh and final Sloan Digital Sky Survey data release …

We use the W Hα versus [N ii]/Hα (WHAN) diagram introduced by us in previous work to
provide a comprehensive emission-line classification of Sloan Digital Sky Survey galaxies …

Astronomers have typically set out to solve supervised machine learning problems by
creating their own representations from scratch. We show that deep learning models trained …

We investigate the dependence of galaxy structure on a variety of galactic and
environmental parameters for∼ 500 000 galaxies at z< 0.2, taken from the Sloan Digital Sky …

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 …

We present the morphological catalogue of galaxies in nearby clusters of the WIde-field
Nearby Galaxy-clusters Survey (WINGS). The catalogue contains a total number of 39 923 …