English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The classification of galaxy morphologies is an important step in theinvestigation of theories of hierarchical structure formation. While humanexpert visual classification remains quite effective and accurate, it cannotkeep up with the massive influx of data from emerging sky surveys. A variety ofapproaches have been proposed to classify large numbers of galaxies; theseapproaches include crowdsourced visual classification, and automated andcomputational methods, such as machine learning methods based on designedmorphology statistics and deep learning. In this work, we develop two novelgalaxy morphology statistics, descent average and descent variance, which canbe efficiently extracted from telescope galaxy images. We further proposesimplified versions of the existing image statistics concentration, asymmetry,and clumpiness, which have been widely used in the literature of galaxymorphologies. We utilize the galaxy image data from the Sloan Digital SkySurvey to demonstrate the effective performance of our proposed imagestatistics at accurately detecting spiral and elliptical galaxies when used asfeatures of a random forest classifier.

Spiral-Elliptical automated galaxy morphology classification from telescope images