Kyle W. Willett

TL;DR: A deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry is developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project.

TL;DR: In this article, the authors use SDSS+GALEX+Galaxy Zoo data to study the quenching of star formation in low-redshift galaxies and conclude that the green valley between the blue cloud of star-forming galaxies and the red sequence of quiescent galaxies in the colour-mass diagram is not a single transitional state through which most blue galaxies evolve into red galaxies.

TL;DR: Galaxy Zoo 2 (GZ2) as discussed by the authors is a citizen science project with more than 16 million morphological classifications of 304 122 galaxies drawn from the Sloan Digital Sky Survey (SDSS).

TL;DR: The Galaxy Zoo 2 data set was used to study the behavior of bars in disk galaxies as a function of specific star formation rate (SSFR) and bulge prominence as mentioned in this paper.

TL;DR: In this article, the first twelve months of operation of Radio Galaxy Zoo are presented, which upon completion will enable visual inspection of over 170,000 radio sources to determine the host galaxy of the radio emission and the radio morphology.