# Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning

arXiv: https://arxiv.org/abs/2109.08735 · HTML: https://mlsanity.com/paper/2109.08735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2021.

## Abstract (arXiv)

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.