We successfully demonstrate the use of explainable artificial intelligence (XAI) techniques
on astronomical data sets in the context of measuring galactic bar lengths. The method …

Galaxy morphologies and their relation with physical properties have been a relevant
subject of study in the past. Most galaxy morphology catalogues have been labelled by …

Galaxy morphology reflects structural properties that contribute to the understanding of the
formation and evolution of galaxies. Deep convolutional networks have proven to be very …

We propose a variant of residual networks (ResNets) for galaxy morphology classification.
The variant, together with other popular convolutional neural networks (CNNs), is applied to …

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 …

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

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 …

With increased adoption of supervised deep learning methods for work with cosmological
survey data, the assessment of data perturbation effects (that can naturally occur in the data …

With increased adoption of supervised deep learning methods for work with cosmological
survey data, the assessment of data perturbation effects (that can naturally occur in the data …

We present a morphological catalogue for∼ 670 000 galaxies in the Sloan Digital Sky
Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 …