On the Value of Coronal Magnetic Field Data from NLFFF Extrapolations for Predicting Solar Energetic Particle Events Using Machine Learning Methods

Abstract

Solar energetic particle (SEP) prediction approaches relying on solar active region physical characteristics have predominantly relied on proxies of the free magnetic energy of a region calculated from photospheric magnetograms despite SEPs forming higher in the corona, with this data mismatch largely being due to limited availability of magnetic vector data in the corona. In this work, we generate approximations of the coronal magnetic field of solar active regions by employing a non-linear force-free field (NLFFF) method that extrapolates magnetic field data from photospheric vector magnetograms upward into the corona. To help make the analysis of these coronal volumes more tractable for lower-complexity models, we develop an approach that estimates the most relevant areas of the volume for SEP prediction purposes and extracts the corresponding cutouts; in this research, we mainly focus on analyzing the mini-cube transversely centered at the polarity inversion region of the volume. Moreover, we parameterize the volumes in a multitude of ways, e.g., by generating several proxies of the free magnetic energy in the corona. To determine the value of this coronal data data compared to the typically-used photospheric data, we conduct several rounds of grid searches that attempt to find the highest-performing ML models and their hyperparameters for each subset of data. We find that the numeric coronal proxies generated from the volumes don’t improve SEP prediction for the models we test compared to numeric photospheric proxies, even when used in combination. We also find evidence suggesting that the coronal volume mini-cubes themselves don’t provide enough signal for the convolutional models used in our experiments. Thus, we emphasize the importance of future work that explores different approaches that both numerically parameterize and natively process coronal volumes for SEP prediction and furthermore suggest the usage of such data in a wide variety of other space weather modeling and prediction tasks (like flare and CME prediction) that may be able to utilize the signal provided by these coronal volumes more efficiently and robustly than the super-rare event prediction task of SEP forecasting.