Morphological Classification of Galaxies Using SpinalNet

Dim Shaiakhmetov, Remudin Reshid Mekuria, Ruslan Isaev, Fatma Unsal
5/3/2023
cs.LGcs.CV

No official code found. Best implementation: pteacher/galaxy_classification_spinalnet (3 stars, MIT, last commit 2023-05-18).

Abstract

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input segmentation in SpinalNet has enabled the intermediate layers to take some of the inputs as well as output of preceding layers thereby reducing the amount of the collected weights in the intermediate layers. As a result of these, the authors of SpinalNet reported to have achieved in most of the DNNs they tested, not only a remarkable cut in the error but also in the large reduction of the computational costs. Having applied it to the Galaxy Zoo dataset, we are able to classify the different classes and/or sub-classes of the galaxies. Thus, we have obtained higher classification accuracies of 98.2, 95 and 82 percents between elliptical and spirals, between these two and irregulars, and between 10 sub-classes of galaxies, respectively.

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Code Implementations(1)

pteacher/galaxy_classification_spinalnetCommunity100%
MIT

Python code for paper "Morphological Classification of Galaxies Using SpinalNet"

30Jupyter NotebookMay 18, 20233 years agoMIT

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Cite this paper

@article{shaiakhmetov2023morphological,
  title  = {Morphological Classification of Galaxies Using SpinalNet},
  author = {Dim Shaiakhmetov and Remudin Reshid Mekuria and Ruslan Isaev and Fatma Unsal},
  year   = {2023},
  eprint = {2305.01873},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.01873v1}
}

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