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Found 4 papers, 4 papers with code
The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques
Therefore, this paper implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS dataset.
A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification
The predicted results are then used to generate accuracy metrics per decision tree question to determine architecture performance.
HELP: A catalogue of 170 million objects, selected at 0.36-4.5 $μ$m, from 1270 deg.$^{2}$ of prime extragalactic fields
This is the first attempt to systematically combine data from 23 of the premier extragalactic survey fields - the product of a vast investment of telescope time.
Astrophysics of Galaxies
The FIRST Classifier: Compact and Extended Radio Galaxy Classification using Deep Convolutional Neural Networks
The current version of the FIRST classifier is able to predict the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).
Astrophysics of Galaxies
