Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

In this review, we explore the historical development and future prospects of artificial
intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in …

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky
Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the …

The Sloan Digital Sky Surveys (SDSS) have been almost continuously observing the skies
for over 20 yr, since the project began with a first phase in 1998 (SDSS-I; York et al. 2000) …

Prediction-powered inference is a framework for performing valid statistical inference when
an experimental dataset is supplemented with predictions from a machine-learning system …

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

The use of citizen science for scientific discovery relies on the acceptance of this method by
the scientific community. Using the Web of Science and Scopus as the source of peer …

Many classes of images exhibit rotational symmetry. Convolutional neural networks are
sometimes trained using data augmentation to exploit this, but they are still required to learn …

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 …

We identify stellar structures in the PHANGS sample of 74 nearby galaxies and construct
morphological masks of sub-galactic environments based on Spitzer 3.6 μm images. At the …