Leveraging the Mathematics of Shape for Solar Magnetic Eruption Prediction

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 \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.

References in corpus (35)

• Adam: A Method for Stochastic Optimization
• Scikit-learn: Machine Learning in Python
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• Statistical topological data analysis using persistence landscapes
• The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• A Critical Assessment of Nonlinear Force-Free Field Modeling of the Solar Corona for Active Region 10953
• Non-linear force-free field modeling of a solar active region around the time of a major flare and coronal mass ejection
• A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
• Topological Data Analysis of Biological Aggregation Models
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Deep Flare Net (DeFN) model for solar flare prediction
• High-Velocity Bipolar Molecular Emission from an AGN Torus
• A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics and Performance Results for Operational Solar Flare Forecasting Systems
• Persistence weighted Gaussian kernel for topological data analysis
• A Bayesian Approach to Solar Flare Prediction
• Flare Prediction Using Photospheric and Coronal Image Data
• Sliced Wasserstein Kernel for Persistence Diagrams
• A hybrid supervised/unsupervised machine learning approach to solar flare prediction
• PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
• Turbulence, Complexity, and Solar Flares
• Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence
• A Comparison of Flare Forecasting Methods. III. Systematic Behaviors of Operational Solar Flare Forecasting Systems
• Finding cosmic voids and filament loops using topological data analysis
• Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
• Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression
• The nonpotentiality of coronae of solar active regions, the dynamics of the surface magnetic field, and the potential for large flares
• Testing and Improving a Set of Morphological Predictors of Flaring Activity
• Magnetic Nonpotentiality in Photospheric Active Regions as a Predictor of Solar Flares
• Calculating Separate Magnetic Free Energy Estimates for Active Regions Producing Multiple Flares: NOAA AR11158
• Murchison Widefield Array Limits on Radio Emission from ANTARES Neutrino Events
• Active Region Photospheric Magnetic Properties Derived from Line-of-sight and Radial Fields
• Calculating energy storage due to topological changes in emerging active region NOAA AR 11112

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• Identifying preflare spectral features using explainable artificial intelligence
• Machine Learning Approaches to Solar-Flare Forecasting: Is Complex Better?
• The characteristics of flare- and CME-productive solar active regions
• A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission