Exoplanet Detection Using Machine Learning : A Comparative Study Using Kepler Mission Data

Abstract

The search for habitable planets outside our solar system has captivated scientists throughout the centuries. Discovery and characterization of exoplanets have been one of the most important endeavors of modern astronomy. With various space missions, we have significantly expanded our observational capacity, resulting in an abundance of information about the universe. The influx of more data necessitates the development of techniques that can aid astronomers in processing all the information more efficiently and in an automated manner. Machine learning in recent years has become an indispensable paradigm to automate complex tasks that are possible only by humans. This work explores the application of machine learning to detect exoplanets from NASA’s Kepler mission. Our dataset comprises Kepler Objects of Interest (KOIs), encompassing their characteristic features and confirmed exoplanet status. We experiment with multiple supervised classification techniques including classical, tree-based, and neural methods. The best-performing model Histogram Gradient Boosting achieves a strong performance of 94.6% precision and 94.1% recall on a held-out dataset demonstrating the strong potential of integrating machine learning techniques into astronomy, potentially leading to new insights into planetary systems outside the solar system.