machine learning for exoplanet detection: a comparative analysis using kepler data

The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. in this study, we present a machine learning (ml) framework for exoplanet identification using a time-series photometric dataset from the kepler space telescope, comprising 3,198 flux measurements across 5,074 stars. we investigate the performance of four supervised classification algorithms, namely random forest, k-nearest neighbors (knn), decision tree, and logistic regression, using a comprehensive set of evaluation metrics such as accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve (auc-roc), confusion matrices, and learning curves. among the models, random forest achieves the highest accuracy (99.8%) and near-perfect f1-scores, demonstrating superior generalization and robustness. knn also performs strongly, achieving 99.3% accuracy, while decision tree demonstrates moderate performance with 97.1% accuracy, and logistic regression trails behind with the lowest accuracy and generalization at 95.8%. notably, the application of the synthetic minority over-sampling technique (smote) significantly improves performance across all models by addressing class imbalance. these findings underscore the effectiveness of ensemble-based machine learning techniques, particularly random forest, in handling large volumes of photometric data for automated exoplanet detection. this approach holds significant potential for implementation at ground-based facilities, such as the iranian national observatory (ino), where such extensive and precise datasets can further advance exoplanet discovery and characterization efforts.