CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena

TL;DR

This paper explores adapting foundation models trained on solar imagery to classify solar wind phenomena using in situ measurements, demonstrating initial feasibility despite current limitations.

Contribution

It introduces a novel approach combining foundation model embeddings with spacecraft data for solar wind classification, bridging remote sensing and in situ observations.

Findings

Modest classification performance due to data limitations.

Feasibility shown for using foundation models in solar wind analysis.

Provides a foundation for future space weather prediction improvements.

Abstract

Space weather at Earth, driven by the solar activity, poses growing risks to satellites around our planet as well as to critical ground-based technological infrastructure. Major space weather contributors are the solar wind and coronal mass ejections whose variable density, speed, temperature, and magnetic field make the automated classification of those structures challenging. In this work, we adapt a foundation model for solar physics, originally trained on Solar Dynamics Observatory imagery, to create embeddings suitable for solar wind structure analysis. These embeddings are concatenated with the spacecraft position and solar magnetic connectivity encoded using Fourier features which generates a neural field-based model. The full deep learning architecture is fine-tuned bridging the gap between remote sensing and in situ observations. Labels are derived from Parker Solar Probe measurements, forming a downstream classification task that maps plasma properties to solar wind structures. Although overall classification performance is modest, likely due to coarse labeling, class imbalance, and limited transferability of the pretrained model, this study demonstrates the feasibility of leveraging foundation model embeddings for in situ solar wind tasks. As a first proof-of-concept, it lays the groundwork for future improvements toward more reliable space weather predictions. The code and configuration files used in this study are publicly available to support reproducibility.