GANexo v2.0 extends our previous framework for exoplanet transit analysis by moving from synthetic signal generation to direct application on real observational data. The new version combines autoencoder-based noise reduction with Generative Adversarial Network (GAN) models, specifically a conditional GAN (cGAN) used for regression, to extract key transit parameters from noisy light curves. This integration improves robustness against observational noise, enhances parameter estimation accuracy, and provides a reliable AI-driven tool specially for ground-based exoplanet studies.
