Parameters Evolution in Source-Sink Space Population Evolutionary Models

TL;DR

This paper introduces a new Source-Sink model for predicting Low Earth Orbit space populations, estimating parameters through stochastic analysis to improve fit with detailed Monte Carlo simulations.

Contribution

It proposes a novel parameter estimation method for Source-Sink models using stochastic analysis of space population data, enhancing predictive accuracy.

Findings

Improved model fit to Monte Carlo simulations

Effective stochastic parameter estimation method

Enhanced prediction accuracy for space population models

Abstract

MOCAT-SSEM is a Source-Sink model that predicts the Low Earth Orbit (LEO) space population divided into families using a predefined set of interaction parameters. Thanks to data from the Monte Carlo version of the model (MOCAT-MC), which propagates singularly every object, it is possible to estimate such parameters, assumed as additional stochastic variables. Thus, this paper proposed a new set of parameters so that the new Source-Sink model prediction better fits the computationally expensive and accurate MOCAT-MC simulation. Estimation is performed by extracting stochastic quantities from the space population, which has been analyzed to fit common probability density functions.