This is a tutorial for using a deep neural network (DNN) to predict the orbital stability of circumbinary planets. The DNN was trained on one million simulations run on REBOUND, a numerical integrator by Hanno Rein et al. Our paper is forthcoming, but for now you can use our code to generate stability predictions for the circumbinary planetary system of your choice. As a proof of concept, this model is simplified - one can imagine introducing additional parameters, such as orbit inclination.
To run predictions on the stability of a circumbinary system given binary eccentricity, binary mass ratio, and binary and planet semi-major axes, simply run 'python tatooine.py -a ____ -e ____ -m ____', where the quantity following the -a flag is the ratio of the planet's semi-major axis to the binary semi-major axis; the quantity following -e is the binary eccentricity; and the quantity following -m is the mass ratio. For more fully detailed information, please see our paper, "A Machine Learns to Predict the Stability of Circumbinary Planets", coming soon.
In the near future, we'll also provide the scripts and a tutorial for training your own DNN.