University of Oulu

Automated morphological classification of galaxies

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Author: Lassi, Elina1
Organizations: 1University of Oulu, Faculty of Science, Physics
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Pages: 43
Persistent link:
Language: English
Published: Oulu : E. Lassi, 2022
Publish Date: 2022-01-20
Thesis type: Bachelor's thesis


Galaxies are systems of dark matter, stars, gas and dust orbiting around a central concentration of mass. They span a wide variety of appearances, based on which they can be classified. Several classification schemes have been developed during the last century, with an attempt to relate the different types of galaxies to each other based on their key physical features and use the received information to further the understanding of both the composition as well as the evolution of galaxies. In the past, the small amount of galaxies in survey image data allowed for the classification process to be completed visually, but with the ever-growing size and depth of survey image data, making classifications in this way nowadays is near to impossible.

Various machine learning algorithms specialized in image recognition and classification can outperform human classifiers in terms of speed and likely soon also in accuracy. The advent of the development of neural network tools for astronomy was in early 1990’s, and ever since especially convolutional neural networks have been applied to classification problems in galactic astronomy. Applications of unsupervised learning have also shown promise in being able to produce self-organizing classification results. The rapid development of machine learning algorithms and hardware suited to perform automated large-scale classification tasks with astronomical survey data holds promise for machine learning methods being able to eventually fully replace humans in most classification tasks.

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Copyright information: © Elina Lassi, 2022. Except otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 4.0 International (CC-BY 4.0) licence ( This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of elements that are not owned by the author(s), permission may need to be directly from the respective right holders.