Feature Engineering for False Positive Exoplanet Prediction: A Deep Learning Approach
Keywords:
: Artificial Neural Networks, Deep Learning, Exoplanet, Stellar Parameters, Transit PropertiesAbstract
The growing number of satellites has improved our understanding of exoplanets, but it has also increased false positive detections. These errors can mislead research and allocation of resources. To address this, we introduce ArtAe, an AI model that employs Artificial Neural Networks and AutoEncoders to validate exoplanet data. ArtAe processes Kepler and TESS datasets, achieving 93.67% and 92.10% accuracy respectively in distinguishing genuine exoplanets from false positives. Moreover, this model has unique algorithm that reduces overfitting of the model. It also lowers dataset dimensionality, saving time and resources. This accuracy aids in informed resource allocation for future studies and enables automated, accurate data validation and analysis.
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