Overview
- A Princeton-led team used machine learning on TESS’s first-year sky survey to identify 10,091 new multi-transit planet candidates.
- The search scanned light curves from 83 million faint stars with Random Forest classifiers, then human reviewers checked the signals.
- The study lists 11,554 candidates in total, including 411 single-transit events that need more passes to pin down orbits.
- The results appear in an arXiv preprint and have not been peer reviewed, so each object remains a candidate until follow-up confirms a true planet.
- NASA separately released a full-sky TESS mosaic through September 2025 that charts 679 confirmed planets and 5,165 earlier candidates for context.