Study Identifies Over 10,000 New Exoplanet Candidates, Potentially Tripling Known Count

By edisonreed // 2026-05-14

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A study uploaded to the preprint server arXiv on April 20 reports the identification of 11,554 exoplanet candidates from data captured by the National Aeronautics and Space Administration's (NASA) Transiting Exoplanet Survey Satellite (TESS). According to the researchers, 10,052 of these candidates had not been previously cataloged. If all candidates are confirmed through follow-up observations, the total number of known exoplanets would rise from roughly 6,000 to nearly 18,000, potentially tripling the current count. The study has not yet undergone peer review. The research analyzed light curves from 83,717,159 stars observed during TESS' first cycle of wide-field imaging. The team employed a machine learning algorithm to detect subtle dips in brightness caused by planets transiting across their host stars. Such transits are difficult to identify in faint stars, but the algorithm was able to process data at a scale described by the authors as "impossible" for manual analysis. Previous surveys using TESS have already discovered hundreds of confirmed exoplanets, as noted by NaturalNews.com in a report highlighting TESS' early success in finding an Earth-sized world [1].

Machine Learning Analysis of Faint Stars

The team analyzed the light curves of precisely 83,717,159 stars from TESS Cycle 1 using a machine learning algorithm, according to the paper. The algorithm detected transit signals in stars up to 16 magnitudes dimmer than the typical threshold for transit studies. The researchers named this project T16. A computer program was required to sort through the enormous dataset, a task that would “be impossible” for humans to perform manually, as reported by Universe Today. The transit method relies on detecting periodic dips in a star’s brightness, an approach that has been foundational in exoplanet discovery. As noted in the book "Searching for Habitable Worlds," the Kepler telescope similarly monitored over 145,000 stars to detect transiting planets, and the time between transits helps determine orbital periods [4]. The new algorithm extends this technique to far fainter stars, greatly expanding the potential planetary census. The paper states that "large-scale, machine-learning-assisted transit searches can significantly expand the census of transiting planet candidates, particularly around faint stars."

Confirmation of a Hot Jupiter Candidate

To test the accuracy of their algorithm, the researchers confirmed one candidate using the Magellan telescopes in Chile's Atacama Desert. They identified TIC 183374187 b, a hot Jupiter located approximately 3,950 light-years from Earth. The planet orbits its star at a distance consistent with the algorithm’s prediction, providing a validation of the machine learning model. "The confirmation of TIC 183374187 b hints that at least a few of the other exoplanet candidates will also end up being confirmed," the authors wrote. However, they noted that independent surveys and additional detailed study will be required to verify the remaining candidates, a process that can take months or years. The radial velocity method, which measures stellar wobble, is commonly used for such confirmation, as described in “A World Without Einstein” [5].

Orbital Periods and Habitability Constraints

Approximately 87% of the candidates were observed transiting their host stars two or more times, allowing researchers to calculate orbital periods ranging from 0.5 to 27 days, according to a data catalog on StellarCatalog.com. Such short orbital periods indicate that these planets orbit very close to their stars, placing them well outside the habitable zone where liquid water could exist on the surface. The authors wrote that these worlds are "probably too close to their home stars to support life as we know it." In contrast, some other exoplanet discoveries have identified potentially habitable worlds. For example, the super-Earth GJ 251c, located 18.2 light-years away, orbits within its star’s habitable zone and is considered a prime candidate for liquid water [3]. The new candidates, while numerous, do not exhibit such favorable conditions for life.

Broader Implications for Exoplanet Surveys

The paper concludes that machine-learning-assisted transit searches can significantly expand the census of transiting planet candidates, especially around faint stars that are often overlooked. The researchers emphasized that future surveys and further analysis are needed to confirm the candidates and to explore the full potential of such automated searches. This approach may accelerate the discovery of planetary systems and inform the design of future missions. Previous studies have shown that TESS and ground-based telescopes working together can rapidly identify exoplanets, as reported by NaturalNews.com in an article noting more than 100 exoplanets found in three months [2]. The current study demonstrates that the application of advanced algorithms to existing data can yield substantial new findings without requiring additional observational resources.

References

NASA’s exoplanet hunter has discovered an Earth-sized alien world. - NaturalNews.com. November 26, 2019.
Groundbreaking telescopes have now found more than 100 exoplanets in only 3 months. - NaturalNews.com. February 20, 2019.
Super-Earth GJ 251c: A Prime Candidate for Alien Life Less Than 20 Light-Years Away. - NaturalNews.com. Kevin Hughes. October 26, 2025.
Searching for Habitable Worlds. - Abel Mendez and Wilson Gonzalez-Espada.
A World Without Einstein. - Simon.