RAVEN mines TESS data, finds 118 planets

The real win here isn’t a bigger model; it’s a trustworthy scientific pipeline that turns noisy telescope light curves into population-level results.

• –RAVEN combines detection, ML vetting, and statistical validation, so astronomers can move from “maybe a signal” to a defensible planet sample in one workflow.
• –Training on simulated planets plus realistic false positives is exactly the right move for a domain where edge cases and mislabeled signals dominate the error budget.
• –The output is scientifically useful, not just technically clever: it helps map close-in planet populations and quantify the Neptunian desert around sun-like stars.
• –A cloud-hosted release makes this more than a paper result; it becomes reusable infrastructure for other exoplanet teams.