Pr0-C0der/Exoplanet-Detection-using-CNN
The project aims to leverage machine learning techniques to analyse the flux data and accurately classify stars as either exoplanet-hosting or non-exoplanet-hosting. By training a model on the provided dataset, we seek to uncover patterns and features indicative of exoplanet presence, enabling the model to make predictions on unseen data.
This project helps astronomers and astrophysicists analyze stellar light data to efficiently identify stars that host exoplanets. By feeding in flux data, which measures changes in a star's light intensity over time, it predicts whether an exoplanet is likely orbiting that star. This tool is designed for researchers who process large volumes of astronomical observations to find new exoplanets.
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Use this if you need an automated way to classify stars as exoplanet-hosting or not, based on their light curve data obtained via the transit method.
Not ideal if your exoplanet detection relies on methods other than the transit method or if you need to characterize specific exoplanet properties like size or orbital period.
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Jupyter Notebook
License
CC0-1.0
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Last pushed
Jul 22, 2023
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