Tara Grice | Searcharxiv

arXiv2025

An Open Benchmark of One Million High-Fidelity Cislunar Trajectories

Cislunar space spans from geosynchronous altitudes to beyond the Moon and will underpin future exploration, science, and security operations. We describe and release an open dataset of one million numerically propagated cislunar trajectories generated with the open-source Space Situational Awareness Python package (SSAPy). The model includes high-degree Earth/Moon gravity, solar gravity, and Earth/Sun radiation pressure; other planetary gravities are omitted by design for computational efficiency. Initial conditions uniformly sample commonly used osculating-element ranges, and each trajectory is propagated for up to six years under a single, fixed start epoch. The dataset is intended as a reusable benchmark for method development (e.g., space domain awareness, navigation, and machine learning pipelines), a reference library for statistical studies of orbit families, and a starting point for community-driven extensions (e.g., alternative epochs). We report empirically observed stability trends (e.g., a band near 5 GEO and persistence of some co-orbital classes including L4/L5 librators) as dataset descriptors rather than new dynamical results. The chief contribution is the scale, fidelity, organization (CSV/HDF5 with full state time series and metadata), and open availability, which together lower the barrier to comparative and data-driven studies in the cislunar regime.

astro-ph.EP↗

arXiv2025

Predicting Cislunar Orbit Lifetimes from Initial Orbital Elements

Cislunar space is the volume between Earth's geosynchronous orbit and beyond the Moon, including the lunar Lagrange points. Understanding the stability of orbits within this space is crucial for the successful planning and execution of space missions. Orbits in cislunar space are influenced by the gravitational forces of the Sun, Earth, Moon, and other Solar System planets leading to typically unpredictable and chaotic behavior. It is therefore difficult to predict the stability of an orbit from a set of initial orbital elements. We simulate one million cislunar orbits and use a self-organizing map (SOM) to cluster the orbits into families based on how long they remain stable within the cislunar regime. Utilizing Lawrence Livermore National Laboratory's (LLNL) High Performance Computers (HPC) we develop a highly adaptable SOM capable of efficiently characterizing observations from individual events. We are able to predict the lifetime from the initial three line element (TLE) to within 10 percent for 8 percent of the test dataset, within 50 percent for 43 percent of the dataset, and within 100 percent for 75 percent of the dataset. The fractional absolute deviation peaks at 1 for all lifetimes. Multi-modal clustering in the SOM suggests that a variety of orbital morphologies have similar lifetimes. The trained SOMs use an average of 2.73 milliseconds of computational time to produce an orbital stability prediction. The outcomes of this research enhance our understanding of cislunar orbital dynamics and also provide insights for mission planning, enabling the rapid identification of stable orbital regions and pathways for future space exploration. As demonstrated in this study, an SOM can generate orbital lifetime estimates from minimal observational data, such as a single TLE, making it essential for early warning systems and large-scale sensor network operations.

astro-ph.EP↗