RAVEN found 118 planets in NASA's TESS data — here's how the algorithm works
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. ...