Researchers Identify Over 11,000 New Exoplanet Candidates Using Machine Learning
A team of researchers has identified more than 11,000 potential exoplanets using a machine learning algorithm to analyze data from NASA's Transiting Exoplanet Survey Satellite (TESS). This discovery could potentially triple the number of known exoplanets. The algorithm examined the light curves of over 83 million stars, detecting subtle dips in brightness that suggest a planet transiting its star. Of the 11,554 candidates identified, 10,052 are new discoveries. The study, which has not yet been peer-reviewed, suggests that if all candidates are confirmed, the total number of known exoplanets could reach nearly 18,000. The team also confirmed one candidate, a 'hot Jupiter' exoplanet, using the Magellan telescopes in Chile.