Identifying Important Features For Exoplanet Detection: A Machine Learning Approach

The study and discovery of exoplanets (planets outside the solar system) have been a major focus in astronomy. Many efforts have been made to discover exoplanets using ground based and space based observatory, NASA’s Exoplanet Exploration Program being one of them. It has developed modern satellites like Kepler which are capable of collecting large array of data to help researchers with these objects. With the increasing number of exoplanet candidates, identifying and verifying their existence becomes a challenging task. In this research, we propose a statistical and machine learning approach to identify important features for exoplanet identification. For this purpose, we use the Kepler Cumulative Object of Interest (KCOI) dataset. After pre-processing the data we utilize statistical methods namely ANOVA F-test, Mutual Information Gain (MIG), Recursive Feature Elimination (RFE) to select the most significant features and have trained 10 state-of-the-art classifiers on them recursively to identify the features that leads to best performance. According to the results of our investigation, classifiers trained on features chosen by Recursive Feature Elimination with Random Forest as estimator produces superior results, with CatBoost classifier being the best with an accuracy of 99.61%. Our findings demonstrate the potential of machine learning in helping astronomers to efficiently and accurately verify exoplanet candidates in large astronomical datasets