Research Article

Leveraging the mathematics of shape for solar magnetic eruption prediction

¹ Department of Computer Science, University of Colorado 430 UCB, Boulder, 80309-0430 CO, USA
² Space Weather Technology, Research, and Education Center (SWx TREC), University of Colorado 429 UCB, Boulder, 80309-0429 CO, USA
³ Santa Fe Institute, Santa Fe, 87501 NM, USA
⁴ Department of Applied Mathematics, University of Colorado 526 UCB, Boulder, 80309-0526 CO, USA

^* Corresponding author: varad.deshmukh@colorado.edu

Received: 30 September 2019
Accepted: 9 March 2020

Abstract

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 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.