Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders
arXiv:2511.05439
Abstract
We apply variational autoencoders to automatically discover galaxy populations using publicly available high-redshift \textit{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.
5 Pages, 2 Figures, Accepted at the Machine Learning and the Physical Sciences Workshop at Neural Information Processing Systems 2025 (NeurIPS 2025)