Improved and Interpretable Solar Flare Predictions with Spatial &
Topological Features of the Polarity-Inversion-Line Masked Magnetograms
Abstract
Many current research efforts undertake the solar flare classification
task using the Space-weather HMI Active Region Patch (SHARP) parameters
as the predictors. The SHARP parameters are scalar quantities based on
spatial average or integration of physical quantities derived from the
vector magnetic field, which loses information of the two-dimensional
spatial distribution of the field and related quantities. In this paper,
we construct two new sets of spatial features to expand the feature set
used for the flare classification task. The first set uses the idea of
topological data analysis to summarize the geometric information of the
distributions of various SHARP parameters. The second set utilizes tools
coming from spatial statistics to analyze the vertical magnetic field
component Br and summarize its spatial variations and clustering
patterns. All features are constructed within regions near the polarity
inversion lines (PILs) and classification performances using the new
features are compared against the SHARP parameters (also along PIL). We
found that using the new features can improve the skill of the flare
classification model and new features tend to have higher feature
importance, especially the spatial statistics features. This potentially
suggests that even using a single channel Br, instead of all SHARP
parameters, one can still derive strongly predictive features for flare
classification.