This project presents an AI-driven framework to accelerate the discovery and habitability assessment of exoplanets, bridging the gap between cutting-edge research and public participation. Leveraging light curve data from Kepler and TESS missions, the system employs a novel hybrid neural architecture that integrates Spiking Neural Networks (SNNs) with Long Short-Term Memory (LSTM) models for efficient and accurate detection of planetary transits, even in noisy data environments. The detection pipeline is followed by a habitability scoring module that computes key physical parameters such as planetary radius, orbital characteristics, and equilibrium temperature. The platform is designed with accessibility at its core, featuring a no-code, user-friendly interface that allows users to search stellar data, visualize light curves, and confirm exoplanet candidates. By combining state-of-the-art machine learning with usability and outreach, this project advances intelligent exoplanet detection and opens new pathways for collaborative scientific discovery, aligning closely with NASA’s mission to explore habitable worlds beyond Earth.
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