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 review the expanding, internet-enabled, and rapidly evolving field of citizen astronomy,
focusing on research projects in stellar, extragalactic, and planetary science that have …

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 have applied computer analysis to classify the broad morphological types of∼ 3· 10 6
Sloan Digital Sky Survey (SDSS) galaxies. For each galaxy, the catalog provides the DR8 …

Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large
number of galactic and extragalactic objects, and the large and complex nature of the image …

The increasing importance of digital sky surveys collecting many millions of galaxy images
has reinforced the need for robust methods that can perform morphological analysis of large …

Many research fields are now faced with huge volumes of data automatically generated by
specialised equipment. Astronomy is a discipline that deals with large collections of images …

This review summarizes popular unsupervised learning methods, and gives an overview of
their past, current, and future uses in astronomy. Unsupervised learning aims to organise the …

Structural properties poses valuable information about the formation and evolution of
galaxies, and are important for understanding the past, present, and future universe. Here …

The morphological diversity of galaxies is a relevant probe of galaxy evolution and
cosmological structure formation, but the classification of galaxies in large sky surveys is …