AI-driven analysis of dangerous space weather: finding dominant modes in space-based measurements

A specific aspect of space weather that remains poorly understood is the exchange of information from space to the ground through the ionosphere. A central component of this process involves understanding how current systems such as field-aligned currents transfer energy and momentum between the magnetosphere and the ionosphere. However, the non-linear behaviour of these current systems poses significant challenges for identifying the drivers of ionospheric currents and understanding the inner dynamics of the ionosphere itself.
To tackle these complexities and their effects on the ground, we adopt a data-driven approach using space-based observations from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). Specifically, we focus on gaining insights into what drives these current systems by finding underlying statistical patterns (dominant modes) in the data using unsupervised machine learning methods. We employ techniques such as β - Variational Autoencoders (β-VAEs), which have been proven useful in identifying patterns in unlabelled observational data.
We extract dominant modes and connect them to physical drivers of the system with a two-step approach. First, we quantify model performance using a physically motivated goodness-of-fit metric to ensure that the learned model reconstructions capture the essential dynamics in the current system. Second, we analyse the model’s latent space, representing a compressed representation of the high dimensional input data. We then analyse the latent space and connect the influence of the individual latents to physical drivers of the system through the usage of the OMNI dataset. This approach enables a systematic interpretation of the model’s internal representations in terms of underlying physical processes.