A Multiresolution Machine Learning Technique to Identify Exoplanets

Abstract

The discovery of planets outside our Solar System, called exoplanets, allows us to study the feasibility of life outside Earth. Different techniques such as the transit method have been employed to detect and identify exoplanets. The amount of time and effort required to perform such a task, hinder the manual examination of the existing data. Several machine learning approaches have been proposed to deal with this matter, though they are not yet unerring. Therefore, new models continue to be proposed. In this work, we present experimental results using the K-Nearest Neighbors, Random Forests, Convolutional Neural Network and the Ridge classifier models to identify simulated transit signals. Furthermore, we propose a methodology based on the Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition techniques for light curve preprocessing. Following this methodology we prove that multiresolution analysis can be used to improve the robustness of the presented models.