nasa/ExoMiner
Automating the vetting and validation of planet candidates from photometry survey missions - Kepler and TESS - using deep learning methods
This project helps astronomers and exoplanet researchers efficiently identify new planet candidates from massive amounts of telescope data. It takes raw photometric light curve data from missions like Kepler and TESS, processes it, and then classifies transit signals to produce vetted catalogs of potential exoplanets. The primary users are subject matter experts in exoplanet discovery and astrophysics who need to sift through extensive observation data.
Use this if you are an exoplanet researcher who needs to automatically classify transit signals and validate potential planets from Kepler and TESS photometric data.
Not ideal if you are analyzing astronomical data unrelated to exoplanet transit detection or prefer manual, visual inspection over automated classification.
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62
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16
Language
Python
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Category
Last pushed
Feb 03, 2026
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