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. ...

Every time I tap a target on the Seestar app, my phone tells the telescope what to find, but not where it is. The S50 figures that part out itself: it slews in the rough direction, takes a calibration frame, asks “where on the sky was this taken?”, and adjusts. Three seconds, one re-pointing nudge, target centered. Plate solving is the answer to that one question: given an arbitrary image of stars, recover the pointing, scale, and rotation. The dominant open-source approach, astrometry.net, was published by Lang, Hogg, and collaborators in 2010 and has quietly become the unsung backbone of consumer astrophotography. Every smart telescope on the market today, from the Seestar S50 to the Vespera Pro, the DWARF 3, and the Celestron Origin, runs some variant of this in real time. ...