Claude Code helps an amateur spot a planet candidate hidden in NASA TESS data
Pavel Rabtsevich used Claude Code to find a 1.4 Earth radius planet candidate around TIC 4206066 in old TESS data. Here is what was found, what could be wrong and the preregistered test that decides it.
An independent researcher says a coding agent helped him spot something that had been sitting unnoticed in NASA telescope data for about seven years: a possible small planet circling a nearby star. On 7 October 2026, Pavel Rabtsevich posted that he had used Claude Code to find a planet candidate around the star TIC 4206066, roughly 116 light years from Earth. Within a day the post had hundreds of thousands of views, a busy thread on r/ClaudeAI and a lively discussion on Hacker News.
It is a great story, and it is also an easy one to get wrong. The word that matters is candidate. Rabtsevich himself says he is not claiming a confirmed or even statistically validated planet. What he has is a repeating dip in starlight that looks like a planet passing in front of its star, a set of checks that the dip survived, and a public, preregistered test that could still prove the whole thing wrong. This article explains what was found, how an AI agent fits into the work, what could still go wrong, and why the way he published it may be the most useful lesson of all.
The main write up we relied on is the detailed explainer from explainx.ai, which links the author's preprint, his code and the official observing program. The preprint itself, titled "Two Transit like Signals in TESS Photometry of the Nearby K Dwarf TIC 4206066", sits on Zenodo and has not been peer reviewed.
What exactly was found
TESS, NASA's Transiting Exoplanet Survey Satellite, stares at large patches of sky and records how bright thousands of stars are over time. When a planet crosses in front of its star from our point of view, the star dims by a tiny, regular amount. Find that pattern and you have a transit signal.
According to the preprint, Rabtsevich first noticed the signal in TESS Sector 98 and then traced it back through older data in Sectors 6 and 32. The main signal repeats every 3.18 days and shows 23 transits, each dimming the star by about 500 parts per million. If the signal really comes from the target star, that points to a world about 1.4 times the radius of Earth. That is in the range astronomers call super Earths, rocky or nearly rocky planets slightly bigger than our own.
A second, weaker signal appears every 11.13 days and would correspond to a planet about 2.2 Earth radii across. The author labels this one tentative. In a test run after the fact it reaches only about 3 sigma, which in astronomy is interesting but far from convincing.
The star itself is a late K dwarf, a type of star that is smaller, cooler and dimmer than the Sun. Small stars are good hunting grounds for small planets, because a planet blocks a larger share of their light and the dip is easier to see.
Why "candidate" is the right word
Astronomers use a ladder of terms, and each rung needs more evidence than the one below it.
- Signal: a dip in brightness that repeats.
- Candidate: the dip looks like a real transit and survives basic checks.
- Validated: a statistical argument shows that a planet is far more likely than any false positive.
- Confirmed: independent measurements, often of the planet's mass using radial velocity, show it is real.
TIC 4206066 sits around the second rung. The preprint reports a false positive probability of 0.03 to 0.04 from TRICERATOPS, a standard tool for estimating how likely a signal is to be something other than a planet. That sounds low, but the author is careful to note that it does not close the case, because it depends on assumptions about companion stars that his data only partly rule out. So he explicitly claims no statistical validation. That honesty is a big part of why serious readers took the post seriously.
Where the AI agent comes in
The headline says Claude Code found a planet. A more accurate version is that one person used an AI coding agent to build and run a pipeline that would normally take a small team.
According to a summary of the workflow, Claude Code did much of the heavy lifting. It downloaded and parsed TESS light curves from NASA's public MAST archive, wrote the transit search code, fitted the dips, checked whether nearby stars could be contaminating the signal, ran false positive tests, made the plots and reran analyses when something changed. Reports say the work used the Opus 5.5 and Fable 5.1 models and produced more than 1,000 scripts across 74 analysis runs over about two weeks. Those numbers come from the author's own account and have not been independently audited.
The most interesting detail is how the work was checked. Rabtsevich did not just trust the agent that built the analysis. He used Codex and separate, clean agent sessions to review the results. That matters because a single long session tends to defend its own earlier claims, while a fresh session that only sees the output is free to attack it. The summary says this audit caught at least one statistical claim that was too strong, and the author removed it.
That is reassuring and worrying at the same time. It shows the review step works. It also shows that without it, an overconfident claim would have gone out into the world with an AI's polish on it.
Was it really unknown?
A natural question is whether professional astronomers had already flagged this star. Rabtsevich reports searching 36 catalogs and literature sources plus 340,505 automated TESS alerts and finding nothing matching his signal. We have not rerun that search ourselves, so treat it as the author's claim. It is still a meaningful check, because one of the most common ways amateur discoveries fall apart is that the object was already listed somewhere.
How it could still be wrong
The preprint names its own biggest risk. TIC 4206066 could actually be two stars so close together that TESS cannot separate them. In that case the planet might be orbiting the fainter companion rather than the main star. The dip would then mean something different, and the estimate of 1.4 Earth radii would no longer apply to the target.
There are also the classic traps that catch many TESS signals:
- A background eclipsing binary: two distant stars orbiting each other can dim the same pixels and mimic a transit.
- Instrument effects: spacecraft pointing jitter and scattered light can create fake dips.
- Post hoc selection: if you search many stars at many periods, some random patterns will look real by chance. That is exactly why the weaker 11.13 day signal is treated with caution.
High resolution imaging and radial velocity measurements would help rule out the companion scenario. Those are the kinds of follow ups that move a candidate up the ladder.
The part worth copying: a test set in advance
The strongest part of this story may not be the AI at all. It is the preregistration.
NASA's TESS mission approved a Director's Discretionary Time program, listed as Program 100 with Rabtsevich as principal investigator, to watch the star at two minute cadence during Sector 110, from 31 October to 26 November 2026. Before that data exists, he has published fixed predictions: nine expected transits for the main signal and three for the weaker one, with expected depths, the exact processing steps and four possible outcomes, from recovered to excluded. The code is frozen and published with checksums, and the test plan is on Zenodo alongside the supporting code and data.
This setup stops goalpost moving. If the new data do not show the dips at the predicted times, he cannot quietly adjust the period to save the claim. Either the signal shows up where he said it would, or it does not. Getting telescope time is not a verdict on the planet, but it does mean the claim will face a fair, public test within weeks.
What the skeptics are saying
The Hacker News thread was full of useful pushback. Several commenters said only peer review and the wider astronomy community, not a Reddit post, can settle whether the planet is real. One warned about "noise fitting noise", the risk that a powerful tool finds structure in random data. Others asked how often coding agents produce false positives when they handle raw astronomical data, and whether a model could quietly make mistakes in the data reduction that nobody notices.
One commenter suggested a simple set of controls that do not depend on the model's own report: inject fake planets into real data and see if the pipeline finds them, run the same pipeline on shuffled data and count the false alarms, and give a fresh session only the result and ask it to break it. Rabtsevich's approach already includes several of those ideas, which is part of why the discussion stayed constructive.
More claims are coming, and not all are equal
The story has already inspired copycats. A second X account claimed on 7 October that it used Opus 5.5 to scan 9,979 nearby red and orange dwarf stars and found 10 possible new exoplanets. As of 9 October, explainx.ai found no preprint, data or code behind that post. A list of possible planets from a sweep of nearly 10,000 stars is exactly where chance signals pile up. Until the author publishes periods, depths, controls and held out predictions, those are leads, not discoveries.
That contrast is the real lesson. The same tools that let a curious person run serious science also make it very easy to produce confident, wrong science. The difference between the two is not the model. It is whether the work is open, checked by someone other than its author and set up so that it can fail.
What this means for AI in science
Two things are true at once. A non specialist with an AI agent can now download public data from a space telescope, write a full analysis pipeline and produce a result worth real telescope time. That is a remarkable shift, and it fits a wider pattern of AI tools moving into research, from lab automation to large sky surveys.
At the same time, agents tend to agree with their own earlier work, can hide uncertainty behind clean charts and are very good at fitting noise. This case is valuable because it shows those risks being handled well: the author reported the most likely alternative explanation, held back from claiming validation and set up a test that can prove him wrong.
What to watch next
- Sector 110 data: observed from 31 October to 26 November 2026. If the predicted transits appear at the right times and depths, the candidate gains real weight.
- Journal review: the preprint targets the Research Notes of the AAS. Acceptance is not validation, but it adds expert eyes.
- Follow up observations: imaging and radial velocity work would address the hidden companion risk.
- Independent reanalysis: the data and code are public, so another team can try to break the result.
If the new data come back empty, the story still has value as a clear example of how to publish a bold claim responsibly. If they come back positive, a planet that hid in NASA's archive for seven years may owe its discovery to one patient person and a coding agent.
Sources
Source: explainx.ai