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The James Webb Space Telescope gathered thousands of spectra from distant galaxies — colorful fingerprints of their light. To avoid drowning in this diversity, astronomers tasked a program with sorting through the finds. But it acted like a naturalist on an unfamiliar shore: not knowing beforehand what species might appear, it arranged objects by their outward similarities. Instead of shells, it used spectral patterns. The algorithm itself identified families: the usual spirals and ellipticals, and among them, rare “blue pearls.”

Such an “automated biologist” will speed up archival sorting by hundreds of times and fish out anomalies worth a closer look. And so far, the first catch from Webb proves: even in cosmic routine, wonders are hiding.

Original abstract

as written by the authors arXiv:2511.05439

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.