Towards a Machine Learning-Based Approach to Predict Space Object
  Density Distributions
Victor Rodriguez-Fernandez Sumiyajav Sarangerel Peng Mun Siew Pablo Machuca Daniel Jang Richard Linares
With the rapid increase in the number of Anthropogenic Space Objects (ASOs),
Low Earth Orbit (LEO) is facing significant congestion, thereby posing
challenges to space operators and risking the viability of the space
environment for varied uses. Current models for examining this evolution, while
detailed, are computationally demanding. To address these issues, we propose a
novel machine learning-based model, as an extension of the MIT Orbital Capacity
Tool (MOCAT). This advanced model is designed to accelerate the propagation of
ASO density distributions, and it is trained on hundreds of simulations
generated by an established and accurate model of the space environment
evolution. We study how different deep learning-based solutions can potentially
be good candidates for ASO propagation and manage the high-dimensionality of
the data. To assess the model's capabilities, we conduct experiments in long
term forecasting scenarios (around 100 years), analyze how and why the
performance degrades over time, and discuss potential solutions to make this
solution better.