  Current operational forecasts of solar eruptions are made by human experts
using a combination of qualitative shape-based classification systems and
historical data about flaring frequencies. In the past decade, there has been a
great deal of interest in crafting machine-learning (ML) flare-prediction
methods to extract underlying patterns from a training set---e.g., a set of
solar magnetogram images, each characterized by features derived from the
magnetic field and labeled as to whether it was an eruption precursor. These
patterns, captured by various methods (neural nets, support vector machines,
etc.), can then be used to classify new images. A major challenge with any ML
method is the \textit{featurization} of the data: pre-processing the raw images
to extract higher-level properties, such as characteristics of the magnetic
field, that can streamline the training and use of these methods. It is key to
choose features that are informative, from the standpoint of the task at hand.
To date, the majority of ML-based solar eruption methods have used
physics-based magnetic and electric field features such as the total unsigned
magnetic flux, the gradients of the fields, the vertical current density, etc.
In this paper, we extend the relevant feature set to include characteristics of
the magnetic field that are based purely on the geometry and topology of 2D
magnetogram images and show that this improves the prediction accuracy of a
neural-net based flare-prediction method.
