A gravity assist mapping for the circular restricted three-body problem using Gaussian processes

Inspired by the Keplerian Map and the Flyby Map, a Gravity Assist Mapping using Gaussian Process Regression for the fully spatial Circular Restricted Three-Body Problem is developed. A mapping function for quantifying the flyby effects over one orbital period is defined. The Gaussian Process Regression model is established by proper mean and covariance functions. The model learns the dynamics of flyby's from training samples, which are generated by numerical propagation. To improve the efficiency of this method, a new criterion is proposed to determine the optimal size of the training dataset. We discuss its robustness to show the quality of practical usage. The influence of different input elements on the flyby effects is studied. The accuracy and efficiency of the proposed model have been inves-tigated for different energy levels, ranging from representative high-to low-energy cases. It shows improvements over the Kick Map, an independent semi-analytical method available in literature. The accuracy and efficiency of predicting the variation of the semi-major axis are improved by factors of 3.3, and 1:27 x 10(4), respectively. (C) 2021 COSPAR. Published by Elsevier B.V.

Automated summary

Inspired by existing maps, a Gravity Assist Mapping using Gaussian Process Regression for the Circular Restricted Three-Body Problem is developed, with a defined mapping function to quantify flyby effects. The model learns dynamics from training samples and a new criterion is proposed for optimal dataset size. The study shows improvements in accuracy and efficiency over existing methods, with factors of 3.3 and 1:27 x 10(4) for predicting semi-major axis variations, respectively.