RAVEN machine-learning pipeline validates 118 new planets in TESS data
Result confirmed
The takeaway
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).
Status
- Claim
Result confirmed
- Our reporting
- Medium confidence
- Verification
- Peer-reviewed
- Importance
- 2 of 5
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
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
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.
Sources
3 sources from 3 sites. Numbers match the chips in the text.
3 sources: 2 primary, 1 press
Primary
- Warwick: AI approach uncovers dozens of hidden planets in TESS datawarwick.ac.uk, official
- RAVEN TESS paper (arXiv 2603.22597)arxiv.org, paper
Press
- ScienceDaily: RAVEN validates 118 new planetssciencedaily.com, press
Changes
- Filed