Exoplanets detection using lite convolutional neural networks (LCNN)

Abstract

This research comprehensively reviews the historical trajectory of exoplanet discovery. Delving into significant milestones, we highlight the pivotal role in shaping our understanding of exoplanetary systems. Building upon this historical foundation, we propose an innovative approach employing a Lite Convolutional Neural Networks (LCNN) model for exoplanet detection utilizing the Kepler Dataset. By fusing advancements in machine learning, particularly CNNs, with the rich domain of exoplanetary studies, this research represents a pivotal stride towards automated, efficient, and precise exoplanet detection. Our LCNN model demonstrates exceptional performance, achieving a training accuracy of 76.92% and an outstanding testing accuracy of 99.12%. This notable accuracy differential indicates successful model generalization and promises reliable exoplanet identification. The study not only enriches our grasp of exoplanetary history but also underscores the transformative potential of machine learning in furthering our cosmic exploration.