Machine learning to predict the solar flux and geomagnetic indices to model density and Drag in Satellites

TL;DR

This paper proposes using machine learning-based time series forecasting to predict solar flux and geomagnetic indices, aiming to improve atmospheric density models and reduce orbital drag uncertainties for satellites.

Contribution

It introduces a novel application of time series forecasting models to predict space weather parameters, enhancing satellite orbit decay modeling.

Findings

Forecasting accuracy for solar flux and geomagnetic indices.

Improved orbital decay predictions for LEO satellites.

Potential reduction in satellite collision risks.

Abstract

In recent years (2000-2021), human-space activities have been increasing faster than ever. More than 36000 Earth' orbiting objects, all larger than 10 cm, in orbit around the Earth, are currently tracked by the European Space Agency (ESA). Around 70\% of all cataloged objects are in Low-Earth Orbit (LEO). Aerodynamic drag provides one of the main sources of perturbations in this population, gradually decreasing the semi-major axis and period of the LEO satellites. Usually, an empirical atmosphere model as a function of solar radio flux and geomagnetic data is used to calculate the orbital decay and lifetimes of LEO satellites. In this respect, a good forecast for the space weather data could be a key tool to improve the model of drag. In this work, we propose using Time Series Forecasting Model to predict the future behavior of the solar flux and to calculate the atmospheric density, to improve the analytical models and reduce the drag uncertainty.