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Completed TRL 3 (started at 3, targeting 6)
Project Objective
To create a modern numerical model that uses solar system dynamics to predict solar activity (proxied as sunspot count) as a function of time.
Project Description
Solar activity prediction is important for prediction of satellite lifetimes in Low Earth Orbit (LEO). This is increasingly true as LEO satellites proliferate due to Starlink, Kepler, and other constellations. Starlink alone currently has 5874 satellites in orbit as of April 30, 2024, and the number is projected to grow as high as 42,000. Some State of the Art (SOA) techniques of solar forecasting are limited to statistical averaging techniques fitting curves to past activity, with results that are less than ideal. For instance, the prediction of Solar Cycle 24 was unexpectedly low and the prediction of the current cycle is unexpectedly high. In both cases, the amplitude of the solar maxima was outside the bounds of the prediction.
A minority opinion in the literature suggests that the dynamics of the solar system (including position, velocity, acceleration of the sun; position of the planets, angular momentum of the solar system; tidal forces on the sun; and other such quantities) may influence solar activity. This project attempts to characterize a relationship between these solar system dynamics quantities and solar activity, based primarily off the 1969 work by Pimm & Bjorn.
Project Results and Conclusions
A neural network model was produced that provides a mean, 5% percentile, and 95% percentile estimate of the number of sunspots over a range of dates. Software was written in MATLAB to access and use the model, which itself is stored as a MATLAB MAT file. The final model's root mean square (RMS) error over the test set of data is 45.82. The model is ready for delivery and improves on Pimm & Bjorn's work, which had an RMS error of 56.72 over the test set.
Solar activity forecasting techniques are consistently inaccurate, particularly at forecasting peak activity. We propose to significantly improve those techniques which will improve forecasting of satellite lifetimes.
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