The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques

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.

arXiv comment6 pages, presented at the 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) 20-22 July 2022, Prague-Czech Republic

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Cited by in corpus (4)

• Data mining techniques on astronomical spectra data. I : Clustering Analysis
• Enabling Unsupervised Discovery in Astronomical Images through Self-Supervised Representations
• Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data
• Machine Learning and galaxy morphology: for what purpose?