Galaxy morphology classification using VGG16

Research output: Contribution to journalConference articlepeer-review

Abstract

There are billions of galaxies in the universe, each with millions or billions of stars. Galaxies vary in shape and size, and they interact with one another, sometimes colliding and merging, sometimes tearing apart, resulting in different features such as bars, rings, pseudo-pillars, and so on. These morphological properties play an important role in the study of galaxy evolution, so the classification of galaxy morphology has always been crucial in astrophysics. Earlier, using traditional methods or manual inspection to accomplish the classification task was a great challenge for astronomers. Today, however, machine learning is widely used for galaxy morphology classification because of its evolving efficiency and accuracy. In this paper, a popular convolutional neural network model (VGG16) will be used to classify galaxy morphology with an accuracy of 81%, which means that VGG16 can be used for correct classification.

Original languageEnglish
Article number012023
JournalJournal of Physics: Conference Series
Volume2580
Issue number1
DOIs
StatePublished - 2023
Event3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023 - Hybrid, Oxford, United Kingdom
Duration: 25 Feb 2023 → …

Keywords

  • VGG16
  • convolutional neural network
  • galaxy morphology classification

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