Self-Supervised

Here’s the number that made me put my coffee down. A model trained on Euclid galaxy images matched a fully supervised network at estimating galaxy redshifts and stellar masses — using 1% of the labeled examples. Ninety-nine percent of the labels thrown away, same accuracy. That result is from the Euclid Quick Data Release paper by Siudek et al., published in Astronomy & Astrophysics on 30 June 2026. It’s one of three papers from the last two years that convinced me astronomy has quietly acquired what the machine-learning world calls foundation models: a single large model pretrained once on a mountain of unlabeled data, then adapted cheaply to many different tasks. ...

A team at Tsinghua University built a neural network called ASTERIS that strips structured noise from telescope images, without ever seeing a clean reference frame. Applied to JWST deep-field data, it recovered galaxies one full apparent magnitude fainter than previous pipelines could detect, and tripled the number of galaxy candidates at redshifts above 9. The paper landed in Science in April 2026, and the code is on GitHub. One magnitude sounds modest until you remember the scale is logarithmic. A gain of 1.0 mag means detecting objects 2.5× dimmer. That’s roughly equivalent to doubling the mirror area of the telescope that took the data. ASTERIS achieves it with software alone. ...