Exoplanet Detection Using CNN

Detection of exoplanets, which are planets orbiting stars other than our Sun, is very important in understanding planetary systems and their habitability. This research examines the applicability of Convolutional Neural Networks (CNNs) in the detection of exoplanets using light curve data obtained from space telescopes like Kepler and TESS. The main aim is to automate and improve the accuracy in the identification of exoplanetary transits in noisy datasets. A CNN-based model was developed and trained on a labeled dataset comprised of light curves, which were labeled with binary data that indicated the presence or absence of an exoplanet. The architecture was optimized for feature extraction from time-series data, capturing subtle variations in brightness indicative of planetary transits. The proposed CNN achieved 98% accuracy, significantly outperforming traditional methods such as manual vetting and classical machine learning models. Results show that the model is indeed robust in distinguishing transit signals from stellar variability and instrumental noise. The analysis further indicated that the model generalizes well to unseen datasets, reducing false positives and negatives significantly. This work concludes that CNNs are a powerful, scalable approach to exoplanet detection: faster and more reliable. Future work would be the extension of the model to multiple planet systems and the consideration of other astrophysical parameters for greater precision.