Abstract We describe the Dark Energy Survey (DES) photometric data set assembled from
the first three years of science operations to support DES Year 3 cosmologic analyses, and …

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

Elastic light scattering (ELS) from single micron-sized particles has been used as a fast, non-
destructive diagnostic tool in life science, physics, chemistry, climatology, and astrophysics …

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 …

A fundamental feature of galaxies is their structure, yet we are just now understanding the
evolution of structural properties in quantitative ways. As such, we explore the quantitative …

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 …

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 …

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 …

Interactions between galaxies leave distinguishable imprints in the form of tidal features,
which hold important clues about their mass assembly. Unfortunately, these structures are …

In this paper, we develop a new unsupervised machine learning technique comprised of a
feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a …