The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques. (arXiv:2206.06165v2 [cs.LG] UPDATED)

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 …

arxiv classification unsupervised unsupervised learning