Scientists have made an exciting discovery: over 10,000 new exoplanet candidates! This breakthrough could nearly triple the number of known alien worlds. It was made possible using a new algorithm that analyzed light from more than 80 million stars, picking up details we couldn’t see before.
The search for exoplanets began in 1995, and since then, technology has advanced quickly. Telescopes like the James Webb Space Telescope have helped astronomers find these distant planets. As of September 2025, over 6,000 exoplanets were confirmed, with nearly 300 added just since then, according to NASA.
A new study, uploaded on April 20 to the preprint server arXiv, reports that researchers have identified 11,554 exoplanet candidates at once. If confirmed, this would bring the total to nearly 18,000! However, this study hasn’t been peer-reviewed yet.
Researchers used a machine learning algorithm to analyze data collected by NASA’s Transiting Exoplanet Survey Satellite (TESS). This satellite has been orbiting Earth since 2018, focusing on dim light fluctuations from stars. By spotting small dips in brightness, scientists can infer the presence of planets passing in front of these stars.
The study revealed 10,052 completely new candidates, while previous efforts had only confirmed a few of them. About 87% of these candidates were seen transiting multiple times, allowing researchers to calculate orbital periods ranging from 0.5 to 27 days, according to StellarCatalog.com.
To verify its findings, the team used a telescope in Chile. They confirmed a “hot Jupiter” exoplanet, named TIC 183374187 b, located about 3,950 light-years away, precisely where the algorithm indicated. This gives hope that some other candidates can also be confirmed.
TESS has already uncovered 882 confirmed exoplanets, which is about 14% of all known exoplanets today. Many researchers focus on the brightest stars because those are easier to study. This study, however, looked at stars that were much fainter, which is why they found so many new candidates.
The researchers called their approach the T16 project. It focused on dim stars, which typically go unnoticed in most studies. The light from these stars can be extremely faint, making it hard to detect planets. The new algorithm helped separate the noise from genuine signals, a task too huge for humans alone.
However, the candidates discovered are likely too close to their stars to support life as we know it. Closer planets orbit more frequently, making them easier to spot but less suitable for life.
This study showcases the potential of machine learning in astronomy. As technology advances, we may continue to uncover more hidden worlds waiting to be explored.
