EclipseNETs: a differentiable description of irregular eclipse conditions

TL;DR

This paper introduces a neural network-based, fully differentiable model for accurately determining eclipse regions around irregular celestial bodies, aiding spaceflight mission design.

Contribution

It presents a novel neural architecture that models eclipse regions for irregular bodies with high precision, enabling differentiable computations in spaceflight mechanics.

Findings

High-precision eclipse modeling for irregular bodies

Differentiable models facilitate advanced spaceflight calculations

Applicable to bodies like Eros, Itokawa, and Bennu

Abstract

In the field of spaceflight mechanics and astrodynamics, determining eclipse regions is a frequent and critical challenge. This determination impacts various factors, including the acceleration induced by solar radiation pressure, the spacecraft power input, and its thermal state all of which must be accounted for in various phases of the mission design. This study leverages recent advances in neural image processing to develop fully differentiable models of eclipse regions for highly irregular celestial bodies. By utilizing test cases involving Solar System bodies previously visited by spacecraft, such as 433 Eros, 25143 Itokawa, 67P/Churyumov--Gerasimenko, and 101955 Bennu, we propose and study an implicit neural architecture defining the shape of the eclipse cone based on the Sun's direction. Employing periodic activation functions, we achieve high precision in modeling eclipse conditions. Furthermore, we discuss the potential applications of these differentiable models in spaceflight mechanics computations.