Habitability Classification of Exoplanets: A Machine Learning Insight

TL;DR

This paper demonstrates how machine learning techniques can effectively classify exoplanets into habitability categories using data from the PHL-EC, proposing best practices and new methods for automated exoplanet classification.

Contribution

It introduces a comprehensive ML-based framework for exoplanet classification, including new methods, accuracy assessments, and best practices for data analysis.

Findings

ML algorithms achieved high classification accuracy

Proposed a best paradigm for automating exoplanet categorization

Developed general data analysis methodologies for exoplanet data

Abstract

We explore the efficacy of machine learning (ML) in characterizing exoplanets into different classes. The source of the data used in this work is University of Puerto Rico's Planetary Habitability Laboratory's Exoplanets Catalog (PHL-EC). We perform a detailed analysis of the structure of the data and propose methods that can be used to effectively categorize new exoplanet samples. Our contributions are two fold. We elaborate on the results obtained by using ML algorithms by stating the accuracy of each method used and propose the best paradigm to automate the task of exoplanet classification. The exploration led to the development of new methods fundamental and relevant to the context of the problem and beyond. Data exploration and experimentation methods also result in the development of a general data methodology and a set of best practices which can be used for exploratory data analysis experiments.