Scientists identify 10,000 ‘impossible’ exoplanet candidates, potentially tripling the number of known alien worlds
Scientists may have detected more than 10,000, never seen before exoplanets in a single survey, potentially tripling the number of known alien worlds in one fell swoop. This record harvest was possible thanks to a new algorithm that helped researchers analyze more than 80 million stars, revealing subtle clues that would otherwise be “impossible” for us to see.
Since the the first alien planet was spotted in 1995the number of exoplanet discoveries has slowly increased with new technologies, such as James Webb Space Telescopewho are better equipped to spot them strange alien worlds. In September 2025, astronomers revealed that the number of confirmed exoplanets had exceeded 6,000and nearly 300 have been added to the list since, according to NASA.
Using a machine learning algorithm, the team analyzed the light curves of precisely 83,717,159 stars captured by NASA’s Transiting Exoplanet Survey Satellite (TESS), a car-sized space telescope that has been circling Earth since 2018. By looking for subtle dips in star brightness, astronomers can determine when a planet has likely passed in front of its home star or has transited by it.
This revealed more than 11,000 candidate exoplanets, 10,052 of which had never been seen before. (Other scientists had already identified the others, but it is not yet confirmed that they are exoplanets.) About 87 percent of the candidates were spotted transiting two or more times, which allowed researchers to calculate the planets’ orbital periods, which range from 0.5 to 27 days, depending on StellarCatalog.com.
TESS is designed to search for objects transiting in front of distant stars. This wide-field image was one of the first taken, shortly after its launch in 2018.
(Image credit: NASA/MIT/TESS)
But the researchers didn’t stop there. To test the validity of their model, they attempted to confirm one of the new candidates themselves.
Using one of Magellan’s 6.5-meter telescopes in Chile’s Atacama Desert, the team identified a “hot Jupiter” exoplanet, dubbed CIT 183374187bwhich orbits a star about 3,950 light years from Earth – exactly where the algorithm predicted.
The confirmation of TIC 183374187 b suggests that at least some of the other exoplanet candidates will eventually be confirmed as well. However, these planets must first be verified by independent surveys and studied in more detail, which may take months or years.
Finding “impossible” planets
TESS was specifically designed to detect transiting objects and has already discovered 882 confirmed exoplanets, about 14% of the current total. So it might seem strange that no one has seen most of the new contestants until now. However, there is a good reason for this.
Most researchers prioritize analyzing the light curves of the brightest stars in the TESS dataset because the transit events of these stars are much more visible and easier to confirm. But many fainter stars end up being captured in the telescope’s wide-field photos.
In the new study, the researchers looked at each star – up to 16 magnitudes fainter than the normal threshold for a transit study – from the first wide-field image from TESS. Researchers call this idea the Project T16.
The machine learning algorithm used in the new study looked for subtle fluctuations in the light curves of faint stars, which may be caused by planets “transiting” with alien suns.
(Image credit: NASA/JPL)
The extreme darkness of these light curves makes it extremely difficult to detect potential transit events, which is why they are usually overlooked. To overcome this obstacle, the team created a machine learning algorithm that learned to distinguish subtle cues that a transit had potentially occurred. (Machine learning is a subset of artificial intelligence where computers learn from data to make predictions, rather than being explicitly programmed.)
A computer program also allowed the team to analyze the huge data set, which would be “impossible” for humans to sort through on their own, Universe Today reported.
“This work shows that large-scale transit searches, aided by machine learning, can significantly expand the census of candidate transiting planets, particularly around faint stars,” the researchers wrote in the paper.
Unfortunately, the brief orbital periods of candidate exoplanets suggest that they are likely too close to their home stars to support life as we know it. (This is because more distant planets orbit their star less often and are less likely to align with an observer for a transit.)
Roth, JT, JD Hartman, G.Á. Bakos, Yee, SW, Bouma, LG, Galarza, JY, Teske, JK, Butler, RP, Crane, JD, Shectman, S., Osip, D., Vissapragada, S., Beletsky, Y., Kanodia, S., and Gaibor, Y. (2026). The hunt for planets T16: 10,000 new candidate planets from TESS cycle 1 and the confirmation of a hot Jupiter around TIC 183374187*. The Astrophysical Journal Supplement Series, 284(1), 19. https://doi.org/10.3847/1538-4365/ae5b6c
