Pavel Rabtsevich is a product manager in Spain. He has no formal astronomy training and no telescope. What he did have was free public data from TESS, NASA’s planet-hunting space telescope, and a few AI coding tools.
TESS finds planets by catching tiny, regular dips in a star’s light as a world crosses in front of it. Rabtsevich pointed AI agents, including Claude Code and Codex, at TESS light curves for 126,246 stars. One star, TIC 4206066, about 116 light-years away, kept dimming for about two hours every 3.18 days.
The clever bit
The clever bit is the public bet. The models wrote the code, ran it and checked each other’s work, while he set the rules and made the calls. Then he froze his code and posted the exact times the star should dim during the next TESS look, before any of that data exists. Anyone can check him against it.
Going back through TESS data from 2018 and 2020, he found the same dip arriving on schedule: 23 transits in all. If it is a planet, it is probably rocky, about 1.4 times Earth’s width, on a three-day orbit hot enough to reach roughly 1,000°F. The TESS team approved his request to watch the star every two minutes from October 31 to November 26.
There’s every reason to expect that this candidate super-Earth will pass further tests and make it into the official census.
— Greg Laughlin, astronomer, Yale University, via scientificamerican.com
Hooray
Hooray: a hobbyist with AI helpers turned free telescope data into a planet candidate good enough to win a closer TESS watch, and he put his predictions on the record before the test.
What comes next
If the dips show up on time and at the right depth between October 31 and November 26, the case gets much stronger. Even then, calling it a planet would still take sharper imaging and other measurements to rule out a hidden companion star. Rabtsevich says he will post the result either way.