Ephemeris accuracy improvement for moons of gas giants: a deep learning based method

Abstract

Ephemerides accuracy of gas giant planetary system is of paramount importance in astronomical research, planetary exploration missions, and space navigation. Traditional mathematical methods face challenges when processing with data of different observation precisions, which may lead to extra noise and reduce the ephemeris accuracy. This paper proposes an Denoising Autoencoder based Method for Ephemeris Improvement (DAMEI) to improve the accuracy of ephemerides for moons in gas giant planetary system. Utilizing multiple sources of data efficiently, the DAMEI method can mitigate the impact of noise and uncertainty introduced by observational data with different precisions. Based on the symmetrical structure, the DAMEI method learns to encode essential motion features of gas giant planetary system into a latent space and captures the intricate patterns in planetary motion from observational data, subsequently decodes it to improve the ephemeris accuracy. The experimental results show that, for Jupiter’s major satellites (Galilean moons), the DAMEI method achieves more accurate ephemeris of up to 91.65% compared with current mathematical method. The proposed method is also assessed with satellites of Saturn, Uranus, and Neptune. It is shown that the DAMEI method also presents a better performance of up to 95.37%. The promising performance of DAMEI method can reduce the uncertainty introduced by low-accuracy data and improve ephemeris accuracy making the utmost of observational data with different precisions.