HUNTING FOR
EXOPLANETS
Decoding signals from distant stars. Utilizing advanced machine learning architectures (XGBoost & Decision Trees) to identify high-probability exoplanet candidates from NASA's interstellar datasets.
Batch Uplink
Process massive orbital datasets (.csv) through our classification pipelines for high-throughput detection.
Access UplinkDirect Signal
Manually calibrate planetary parameters to get instant probability scores for isolated candidate sources.
Initialize InputProject Protocol
Deep dive into the models, feature engineering, and scientific methodology behind the EXO-AI initiative.
Review DataMission Intelligence
Developed for the NASA International Space Apps Challenge, this node leverages the K2 catalog. Our models currently demonstrate over 99% accuracy in differentiating confirmed exoplanets from false positives.