Neural Network Discovers New Galaxies Without Any Guidance ⚡ экспресс
Using variational autoencoders (neural networks that find hidden patterns), the spectra of distant galaxies from the James Webb Space Telescope have been analyzed. Without preliminary labeling, the algorithm independently identified several clear astrophysical classes. Among them are unique types of galaxies, previously undescribed. This shows how unsupervised learning methods can automatically make discoveries in large datasets. As if the telescope itself learned to ask the question: 'What else is hidden in these spectra?'
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.
🎯 The James Webb is so sensitive in the infrared that it could detect the heat of a bumblebee on the surface of the Moon.