Detecting gravity anomalies in asteroids: application to Hera's radio science

Student: Amelia Samuel
Supervisors: Stefania Soldini (UoL), Monica D’Onofrio (UoL)
Institution: University of Liverpool

An accurate estimation of the density distribution of asteroids is of great interest for both planetary science and defense. Knowing the internal density of an asteroid will unlock our understanding of their formation, improve our knowledge of the gravity environment around them and enhance the safety of spacecraft-asteroid proximity operations. Asteroids are the result of impacts between celestial objects, thus, gravity anomalies in asteroids can occur. Indeed, the Moon is characterised by such anomalies (mascons) which can affect the orbit stability of a spacecraft.

The scope of this project is to investigate a novel approach in detecting gravity anomalies in asteroids through the inverse gravity problem. A combination between traditional measurements of gravity-induced accelerations, experienced by a spacecraft, with the information of the dynamical system theory is yet to be explored. Traditional methods to estimate the asteroids’ internal density distribution include the fitting of gravity coefficients from a chosen gravity model combined with the gravity accelerations experienced by a spacecraft. Several inverse constrained optimisation methods have been explored such as least-squares, Bayesian, neural network, and genetic algorithms.

This project will investigate novel machine learning approaches to the inverse problem with constraints from the information of the dynamical systems theory (e.g., inverse problem from equilibrium points detection) which differ from traditional approach that makes use of solely gravity-induced accelerations. The aim of the thesis is to develop a strategy for autonomous radio science operations, thus developing the onboard software that enables a spacecraft or CubeSats (a 10x10x10cm small size spacecraft) to self-estimate the internal grain density distribution of asteroids. This study will enable, for example, a generalised strategy for determining sequences of spacecraft’s close-proximity ascending operations (e.g., hovering, orbit) for estimating asteroids' internal structure as a function of their physical properties (e.g., class, size, spin-ratio). Ultimately, the project should answer the research question “Which generalised radio science methodology will enable a self-driven spacecraft to perform onboard autonomous detection of asteroids’ gravity anomalies and reconstruction of their internal structure for several asteroids’ class?”