ABSTRACT We present Galaxy Zoo DECaLS: detailed visual morphological classifications
for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint …

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 …

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 …

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 …

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

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 …

We use Bayesian convolutional neural networks and a novel generative model of Galaxy
Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian …

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 detailed morphology measurements for 8.67 million galaxies in the DESI Legacy
Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated …

We present morphological classifications of∼ 27 million galaxies from the Dark Energy
Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The …