Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders
Abstract
We apply variational autoencoders to automatically discover galaxy populations using publicly available high-redshift JWST spectra without prior classification knowledge. Our unsupervised method identifies distinct astrophysical classes of unique and exciting galaxy types, demonstrating automated discovery capabilities for large spectroscopic surveys.
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