This research explores the discovery and habitability assessment of exoplanets using advanced computational techniques. A combination of machine learning algorithms, Generative Adversarial Networks (GANs), and MongoDB is employed to process and manage extensive datasets related to exoplanet characteristics. Detection methods such as direct imaging, transit photometry, and radial velocity are integrated, with a particular focus on radial velocity, which identifies exoplanets by measuring Doppler shifts in stellar light and analyzing flux luminosity from space-based signals. Through spectral analysis of these signals, the study forecasts biosignatures—chemical markers vital for evaluating the potential for life. The GAN model generates spectral images that enable predictions of current atmospheric compositions, supporting the estimation of environmental conditions conducive to habitability. A user-friendly web interface presents these findings in an accessible graphical format, making the system both intuitive and informative for researchers and general users. The trained classification model achieved an accuracy of 93.7% in distinguishing habitable from non-habitable exoplanets, while ExoGAN exhibited high fidelity in synthesizing biosignature images across diverse atmospheric profiles. This approach offers a scalable, instrument-agnostic framework suitable for application in upcoming space missions, aiding in the prioritization of potentially Earth-like worlds. The framework also supports real-time updates as new observational data becomes available. Moreover, its modular design allows integration with future astronomical databases and APIs. The methodology ensures adaptability across telescopic platforms and observational conditions. Ultimately, this study bridges the gap between AI-driven modeling and astrobiological discovery, pushing the frontier of life detection beyond the solar system.
Kumar Rohan (Wed,) studied this question.