Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning

TL;DR

This paper uses machine learning to classify the habitability of circumbinary planets based on their trajectories and binary star characteristics, enhancing understanding of complex exoplanet systems.

Contribution

It introduces a machine learning approach to rapidly classify the habitability of planets in binary systems considering dynamic and orbital factors.

Findings

Mass ratio and eccentricity influence habitability.

Planetary trajectories help categorize habitability.

Machine learning enables efficient classification.

Abstract

Exoplanet detection in the past decade by efforts including NASA's Kepler and TESS missions has discovered many worlds that differ substantially from planets in our own Solar System, including more than 150 exoplanets orbiting binary or multi-star systems. This not only broadens our understanding of the diversity of exoplanets, but also promotes our study of exoplanets in the complex binary systems and provides motivation to explore their habitability. In this study, we investigate the Habitable Zones of circumbinary planets based on planetary trajectory and dynamically informed habitable zones. Our results indicate that the mass ratio and orbital eccentricity of binary stars are important factors affecting the orbital stability and habitability of planetary systems. Moreover, planetary trajectory and dynamically informed habitable zones divide planetary habitability into three categories: habitable, part-habitable and uninhabitable. Therefore, we train a machine learning model to quickly and efficiently classify these planetary systems.