Machine-Learning

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 paper published in the Journal of Cosmology and Astroparticle Physics on June 10 asks a deceptively simple question: if you train a neural network on the standard cosmological model and then ask it to look for physics beyond that model, does the pretraining help or hurt? The answer, from a Princeton and Flatiron Institute team, is both. Transfer learning — pretraining on cheap ΛCDM simulations, then fine-tuning on expensive beyond-ΛCDM runs — cut the number of simulations needed by more than a factor of 10 in some scenarios. But it also introduced a failure mode the authors call negative transfer: the network learned the standard model’s patterns so thoroughly that it confused genuinely new physics with familiar old parameters. ...

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. ...

On February 25, 2026, the Vera C. Rubin Observatory in Chile sent its first batch of real-time alerts — 800,000 of them in a single night. Each one flagged something that changed in the sky: a new point of light, a brightening star, a moving dot that might be an asteroid. The Legacy Survey of Space and Time (LSST) had officially started talking. That was the gentle version. At full survey depth, Rubin will generate up to 10 million difference-image alerts every night. No research group, no observatory control room, no grad student with a caffeine problem can review that by hand. The only reason the alerts are useful at all is a network of nine community software platforms — called alert brokers — that run machine-learning classifiers on every packet before the Sun comes up. ...

The Sun is a bell. Not a metaphor — the entire solar interior resonates with acoustic waves, trapped pressure oscillations that bounce between the surface and the core roughly every five minutes. The field that studies these oscillations is called helioseismology, and for three decades a network of ground stations has been recording every pulse. A team at the University of Sheffield and the National Solar Observatory just ran 30 years of those oscillations through three different machine learning architectures. All three converge on the same prediction: Solar Cycle 25 peaked in early 2025, and the next minimum falls around 2030–2031. The paper, published this month in Solar Physics, is one of the first to treat the Sun’s acoustic frequency shifts as a forecasting signal for the solar cycle — not just a diagnostic one. ...

A team at the University of Warwick pointed a machine-learning pipeline at four years of NASA TESS full-frame images — 2.2 million stars — and pulled out 118 validated planets, roughly 1,000 new candidates, and the first direct measurement of how scarce Neptune-sized worlds are in tight orbits. The pipeline is called RAVEN (RAnking and Validation of ExoplaNets), and the paper landed in MNRAS this spring. I spend most of my telescope time on deep-sky imaging from my balcony in Nicosia, but I follow the exoplanet pipeline papers closely because they sit exactly at the intersection I care about: where does the ML end and the astrophysics begin? RAVEN is a clean case study. ...