An AI that taught itself what noise looks like found three times more galaxies in JWST data
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. ...