Results 131 to 140 of about 11,134,879 (212)

Mid-infrared galaxy morphology from the Spitzer survey of stellar structure in galaxies (S^4G): the imprint of the De Vaucouleurs revised Hubble-Sandage classification system at 3.6 μm

open access: yes, 2010
Spitzer Space Telescope Infrared Array Camera imaging provides an opportunity to study all known morphological types of galaxies in the mid-IR at a depth significantly better than ground-based near-infrared and optical images. The goal of this study is to examine the imprint of the de Vaucouleurs classification volume in the 3.6 μm band, which is the ...
openaire   +1 more source

Galaxy Morphology Classification Using Deep Learning

open access: yes
Galaxy morphology is the study of the shape and visual appearance of galaxies, such as spiral, smooth, edge-on, and other morphological types. Morphological classification plays an important role in understanding how galaxies form and evolve over cosmic ...
Kundu Roy, Dipanwita
core   +1 more source

Galaxy morphology classification based on Convolutional vision Transformer (CvT)

open access: yes
Context. The classification of galaxy morphology is among the most active fields in astronomical research today. With the development of artificial intelligence technology, deep learning is a useful tool in the classification of the morphology of ...
Zhijing Liu   +6 more
core   +1 more source

A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification [PDF]

open access: yes
This paper introduces a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18).
Agarwal, Akshay   +2 more
core   +1 more source

Galaxy Morphology Classification System

open access: yes, 2017
Citizen scientists are becoming more and more important in helping professionals working through big data. An example in astronomy is crowd sourced galaxy classification. But how reliable are these classifications for studies of galaxy evolution? We have
Francis, Lennox
core  

The training-discovery paradox: Evidence from citizen science. [PDF]

open access: yesPLoS One
Ogunseye S   +3 more
europepmc   +1 more source

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