In the most recent decade, over a million stars were monitored to identify transiting planets. Manual interpretation of potential exoplanet candidates is labor-intensive and subject to human error, making the evaluation complex and challenging. Convolutional neural networks are well-suited for identifying Earth-like exoplanets in noisy time-series data with greater accuracy compared to traditional least-squares methods.
Advanced space missions like Kepler have provided astronomers with vast data to analyze, leading to significant discoveries and fueling hope for finding habitable worlds beyond Earth.