Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders

We apply variational autoencoders to automatically discover galaxy populations using publicly available high-redshift $\mathit{\text{JWST}}$ 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.