Exoplanet Detection Using Machine Learning

Key Points

Achieving an accuracy of 99.29% for detecting confirmed exoplanets shows significant improvement in identification.
The model utilizes a Random Forest classifier to differentiate between confirmed exoplanets and false positives effectively.
Data preprocessing and feature selection are critical steps in enhancing the model's performance for accurate detection.
This automated approach may enable more efficient exoplanet detection, reducing reliance on manual verification methods.

Abstract

The detection of exoplanets plays a crucial role in understanding planetary systems beyond our solar system. Traditional detection techniques often require extensive manual verification, making automated solutions desirable. In this study, a machine learning–based approach for exoplanet detection is proposed using data from the NASA Kepler mission. After preprocessing and feature selection, a Random Forest classifier is trained to distinguish confirmed exoplanets from false positives. Experimental results demonstrate that the proposed model achieves an accuracy of 99.29

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Exoplanet Detection Using Machine Learning

Key Points

Abstract

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