Data

Tools

Indexes

Applications

Experiments

Open Source Science

galaxy-cluster

Clustering methods for galaxy morphology classification

https://github.com/ezrafielding/galaxy-cluster

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Clustering methods for galaxy morphology classification

Basic Info
  • Host: GitHub
  • Owner: ezrafielding
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 157 KB
Statistics
  • Stars: 5
  • Watchers: 1
  • Forks: 9
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques

GitHub DOI arXiv

Abstract

In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount of data generated by modern surveys. This has brought into question the viability of human-based methods for classifying galaxy morphology. While supervised learning methods require datasets with existing labels, unsupervised learning techniques do not. Therefore, this paper implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS dataset. A convolutional autoencoder feature extractor was trained and implemented. The resulting features were then clustered via k-means, fuzzy c-means and agglomerative clustering. These clusters were compared against the true volunteer classifications provided by the Galaxy Zoo DECaLS project. The best results, in general, were produced by the agglomerate clustering method. However, the increase in performance compared to k-means clustering was not significant considering the increase in clustering time. After undergoing the appropriate clustering algorithm optimizations, this approach could prove useful for classifying the better performing questions and could serve as the basis for a novel approach to generating more "human-like" galaxy morphology classifications from unsupervised techniques.

Citation

E. Fielding, C. N. Nyirenda and M. Vaccari, "The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques," 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022, pp. 1-6, doi: 10.1109/ICECET55527.2022.9872611.

Owner

  • Name: Ezra Fielding
  • Login: ezrafielding
  • Kind: user
  • Location: Kitakyushu, Japan
  • Company: Kyushu Institute of Technology

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  The Classification of Optical Galaxy Morphology Using
  Unsupervised Learning Techniques
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ezra
    family-names: Fielding
    email: 3869003@myuwc.ac.za
    affiliation: University of the Western Cape
    orcid: 'https://orcid.org/0000-0002-7936-0222'
  - given-names: Clement N.
    family-names: Nyirenda
    email: cnyirenda@uwc.ac.za
    affiliation: University of the Western Cape
    orcid: 'https://orcid.org/0000-0002-4181-0478'
  - given-names: Mattia
    family-names: Vaccari
    email: mvaccari@uwc.ac.za
    affiliation: University of the Western Cape
    orcid: 'https://orcid.org/0000-0002-6748-0577'
identifiers:
  - type: doi
    value: 10.1109/ICECET55527.2022.9872611
    description: The DOI of the work.
  - type: doi
    value: 10.48550/arXiv.2206.06165
    description: The ArXiv deposit of the encompassing paper.
repository-code: 'https://github.com/ezrafielding/galaxy-cluster'
url: 'https://sites.google.com/myuwc.ac.za/galaxy-classification'
abstract: >-
  In recent years, large scale data intensive astronomical
  surveys have resulted in more detailed images being
  produced than scientists can manually classify. Even
  attempts to crowd-source this work will soon be outpaced
  by the large amount of data generated by modern surveys.
  This has brought into question the viability of
  human-based methods for classifying galaxy morphology.
  While supervised learning methods require datasets with
  existing labels, unsupervised learning techniques do not.
  Therefore, this paper implements unsupervised learning
  techniques to classify the Galaxy Zoo DECaLS dataset. A
  convolutional autoencoder feature extractor was trained
  and implemented. The resulting features were then
  clustered via k-means, fuzzy c-means and agglomerative
  clustering. These clusters were compared against the true
  volunteer classifications provided by the Galaxy Zoo
  DECaLS project. The best results, in general, were
  produced by the agglomerate clustering method. However,
  the increase in performance compared to k-means clustering
  was not significant considering the increase in clustering
  time. After undergoing the appropriate clustering
  algorithm optimizations, this approach could prove useful
  for classifying the better performing questions and could
  serve as the basis for a novel approach to generating more
  "human-like" galaxy morphology classifications from
  unsupervised techniques.
license: MIT
date-released: '2022-07-22'
preferred-citation:
  type: conference-paper
  authors:
    - given-names: Ezra
      family-names: Fielding
      email: 3869003@myuwc.ac.za
      affiliation: University of the Western Cape
      orcid: 'https://orcid.org/0000-0002-7936-0222'
    - given-names: Clement N.
      family-names: Nyirenda
      email: cnyirenda@uwc.ac.za
      affiliation: University of the Western Cape
      orcid: 'https://orcid.org/0000-0002-4181-0478'
    - given-names: Mattia
      family-names: Vaccari
      email: mvaccari@uwc.ac.za
      affiliation: University of the Western Cape
      orcid: 'https://orcid.org/0000-0002-6748-0577'
  title: The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques
  doi: 10.1109/ICECET55527.2022.9872611
  pages: 1-6
  year: '2022'
  conference:
    name: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)
    date-start: "2022-07-20"
    date-end: "2022-07-22"

GitHub Events

Total
Last Year