This paper provides a step-by-step guide to go from a given future number of launches to a complete picture in terms of orbital region and physical parameters. This paper only describes non-constellation launches. For these non-constellation launches distributions are built using historical data from DISCOS. Here, the orbital parameter distribution is built using Gaussian-Mixture Models. The object class is determined from a simple probability distribution. A special focus is given to the mass of the objects, since this is an important quantity to be considered when talking about the evolution of the space environment. For the mass, an XGBoost algorithm is employed, which uses the other object characteristics as an input. Finally, the area of the object is determined based on the mass.

Forecasting a Complete Picture of Future Launches in Terms of Orbital Region and Physical Parameters

Retagne, Wiebke;Colombo, Camilla
2026-01-01

Abstract

This paper provides a step-by-step guide to go from a given future number of launches to a complete picture in terms of orbital region and physical parameters. This paper only describes non-constellation launches. For these non-constellation launches distributions are built using historical data from DISCOS. Here, the orbital parameter distribution is built using Gaussian-Mixture Models. The object class is determined from a simple probability distribution. A special focus is given to the mass of the objects, since this is an important quantity to be considered when talking about the evolution of the space environment. For the mass, an XGBoost algorithm is employed, which uses the other object characteristics as an input. Finally, the area of the object is determined based on the mass.
2026
CEAS - AIDAA Conference 2025
978-1-64490-424-4
Historical Sampling
Launch Traffic
Launch Trends
Machine Learning
Space Debris Evolution
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1324806
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