Pavel Rabtsevich, a 28-year-old product manager with no formal astronomy training, used Claude Code and other AI tools to search public data from NASA’s TESS space telescope for possible planets. The models helped analyze measurements from more than 126,000 stars, but Rabtsevich directed the investigation, checked results across systems, and ran tests designed to challenge his hypothesis. He identified a repeating, slight dip in the brightness of TIC 4206066, a star about 116 light-years away. The signal recurs every 3.18 days and appears in TESS observations from 2018, 2020, and 2025. Rabtsevich estimates that a planet causing it would be about 1.4 times Earth’s size, with a scorching surface temperature near 1,000°F. A second possible signal, recurring every 11.13 days, is less persuasive.
The planet remains unconfirmed: similar brightness changes can arise from binary stars or other false positives. Rabtsevich says he ran 74 tests, including asking Claude to predict dimming times in data it had not analyzed; its predictions matched the observations. MIT approved a new TESS observation campaign for October 31 to November 26, with measurements every two minutes. If the star dims at the predicted times, the case will strengthen; otherwise, Rabtsevich says he will report that too. The episode illustrates how AI can make large-scale searches accessible to amateur researchers and help identify candidates quickly, while professional follow-up remains essential to verify them. TESS has already produced thousands of candidates, and NASA’s own AI analysis has identified about 7,000.
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