RAVEN machine-learning pipeline validates 118 new planets in TESS data
Warwick's RAVEN pipeline analysed 2.2 million stars observed by TESS and validated 118 new planets and over 2,000 vetted candidates (nearly 1,000 of them new), including ultra-short-period planets and planets in the 'Neptunian desert' (MNRAS, 2026).
Key facts
- 2.2M stars from TESS's first four years; 118 newly validated planets; >2,000 vetted candidates, nearly 1,000 new
- ~9–10% of Sun-like stars host a close-in (<16-day) planet, with uncertainties up to 10× smaller than Kepler's; Neptunian-desert planets occur around ~0.08% of Sun-like stars
- Paper arXiv 2603.22597; Warwick press release Mar 2026 (day approximate); MNRAS
Science result
- Field
- astronomy / exoplanets
- Problem
- Vetting TESS transit candidates at scale
- Result
- 118 statistically validated planets and a large vetted candidate catalogue.
- AI system
- RAVEN
- Human role
- Human-designed pipeline
- Verification
- Peer-reviewed in MNRAS; statistical validation
- Status
- confirmed
What happened
An ML vetting pipeline processed millions of TESS light curves and validated over a hundred planets.
Why it matters
It continues AI's role as the main filter for exoplanet surveys.
Changelog
- 2026-09-29: created
Related events
Sources (3)
- officialWarwick: AI approach uncovers dozens of hidden planets in TESS data
- paperRAVEN TESS paper (arXiv 2603.22597)
- pressScienceDaily: RAVEN validates 118 new planets
id: 2026-03-25-raven-tess-118-new-planets · updated 2026-09-29 · open in the interactive timeline