Geodesy of irregular small bodies via neural density fields

Asteroids’ and comets’ geodesy is a challenging yet important task for planetary science and spacecraft operations, such as ESA’s Hera mission tasked to look at the aftermath of the recent NASA DART spacecraft’s impact on Dimorphos. Here we present a machine learning approach based on so-called geodesyNets which learns accurate density models of irregular bodies using minimal prior information. geodesyNets are a three-dimensional, differentiable function representing the density of a target irregular body. We investigate six different bodies, including the asteroids Bennu, Eros, and Itokawa and the comet Churyumov-Gerasimenko, and validate on heterogeneous and homogeneous ground-truth density distributions. Induced gravitational accelerations and inferred body shape are accurate, resulting in a relative acceleration error of less than 1%, also close to the surface. With a shape model, geodesyNets can even learn heterogeneous density fields and thus provide insight into the body’s internal structure. This adds a powerful tool to consolidated approaches like spherical harmonics, mascon models, and polyhedral gravity. Izzo and Gómez present a machine learning-based method for obtaining accurate density models of even irregular celestial bodies using minimal prior information. The work is validated on uniform and non-uniform density models of several visited asteroids.