Analysis of Kepler Objects of Interest using Machine Learning for Exoplanet Identification

Abstract

For several decades, planet identification has only been performed by astronomical experts and researchers with the help of specialized equipment. With the advent of computational methods and access to satellite data from space missions, this trend has changed. For instance, NASA’s Exoplanet Exploration program has provided us vast amounts of data on celestial objects to assist in space exploration. One such mission of interest is the Kepler mission. Over 4000 such transiting exoplanets have been identified since the mission commenced in 2007. It has provided us with an extensive database of discoveries that help in computing planet occurrence rates as a function of an object’s parameters such as the size, insolation flux, star type and orbital period. This information is catalogued in the Cumulative Kepler Object of Information dataset. Four basic models have been compared. Namely, Support Vector Machines, Random Forest Classifiers, AdaBoost and Deep Neural Networks. The AdaBoost classifier was selected as the optimum machine learning model and returned an F-1 score of 0.98.