  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 400 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
and multi-star systems and provides motivation to explore their habitability.
In this study, we analyze orbital stability of exoplanets in non-coplanar
circumbinary systems using a numerical simulation method, with which a large
number of circumbinary planet samples are generated in order to quantify the
effects of various orbital parameters on orbital stability. We also train a
machine learning model that can quickly determine the stability of the
circumbinary planetary systems. Our results indicate that larger inclinations
of the planet tend to increase the stability of its orbit, but change in the
planet's mass range between Earth and Jupiter has little effect on the
stability of the system. In addition, we find that Deep Neural Networks (DNNs)
have higher accuracy and precision than other machine learning algorithms.

Earth and Planetary Astrophysics;Machine Learning