Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions

This escalating trend is projected to continue as multiple companies, including SpaceX, Amazon, and Astra Space, plan to launch large constellations of hundreds to thousands of satellites. The resulting dense and complex operating environment elevates the risk of collisions and debris generation, posing substantial challenges for space operators. Not only does this situation threaten the safety of flight and mission success in the short run, but it also jeopardizes the long-term viability of the LEO environment for scientific, commercial, and national security uses. Hence, understanding and modeling the evolution of the space environment is crucial for ensuring its sustainability and informing strategies for effective space traffic management. A variety of models have emerged to examine this evolution and calculate the orbital capacity, which is referred to as the number of satellites that can feasibly be situated in LEO [1]. Traditional proprietary models, developed by organizations like NASA's LEGEND [2], ESA's DELTA [3], JAXA's IMPACT [4] among others [5, 6], have been complemented by newer open-source initiatives such as the MIT Orbital Capacity Tool (MOCAT) and its various versions [7, 8]. Most of these models operate by propagating all the ASOs forward in time, utilizing physical models of spacecraft dynamics. This methodology incorporates factors such as atmospheric drag, solar radiation pressure, third-body perturbations, and space weather, in addition to simulated collisions and explosions.