EclipseNETs: a differentiable description of irregular eclipse conditions
This addresses a critical problem in astrodynamics for mission design, though it is incremental as it applies existing neural methods to a specific domain.
The study tackled the challenge of determining eclipse regions for irregular celestial bodies in spaceflight mechanics by developing differentiable models using neural image processing, achieving high precision in modeling eclipse conditions for bodies like 433 Eros and 67P/Churyumov-Gerasimenko.
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.