Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides

TL;DR

This paper introduces a data-driven, stable, and accurate collective dynamics model for celestial motion that outperforms classical physics models and accurately reproduces key orbital features using ephemerides data.

Contribution

It develops a novel machine learning approach to derive interaction kernels between celestial bodies, surpassing traditional gravitational models in accuracy and predictive capability.

Findings

Model accurately reproduces perihelion precession of planets and Moon.

Outperforms Newtonian gravity in all tested cases.

Matches or exceeds predictions of Einstein's relativity for lunar motion.

Abstract

Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics. Candidate theories seek to explain observations and predict future positions of planets, stars, and other astronomical bodies as faithfully as possible. We use a data-driven learning approach, extending that developed in Lu et al. ($2019$) and extended in Zhong et al. ($2020$), to a derive stable and accurate model for the motion of celestial bodies in our Solar System. Our model is based on a collective dynamics framework, and is learned from the NASA Jet Propulsion Lab's development ephemerides. By modeling the major astronomical bodies in the Solar System as pairwise interacting agents, our learned model generate extremely accurate dynamics that preserve not only intrinsic geometric properties of the orbits, but also highly sensitive features of the dynamics, such as perihelion precession rates. Our learned model can provide a unified explanation to the observation data, especially in terms of reproducing the perihelion precession of Mars, Mercury, and the Moon. Moreover, Our model outperforms Newton's Law of Universal Gravitation in all cases and performs similarly to, and exceeds on the Moon, the Einstein-Infeld-Hoffman equations derived from Einstein's theory of general relativity.