Flare Prediction Using Photospheric and Coronal Image Data

  title={Flare Prediction Using Photospheric and Coronal Image Data},
  author={Eric Jonas and Monica G. Bobra and Vaishaal Shankar and J. Todd Hoeksema and Benjamin Recht},
  journal={Solar Physics},
The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and… 
Real-time Flare Prediction Based on Distinctions between Flaring and Non-flaring Active Region Spectra
With machine learning entering into the awareness of the heliophysics community, solar flare prediction has become a topic of increased interest. Although machine learning models have advanced with
Reliable Probability Forecast of Solar Flares: Deep Flare Net-Reliable (DeFN-R)
We developed a reliable probabilistic solar flare forecasting model using a deep neural network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum classes of flares that occur
Measuring the Magnetic Origins of Solar Flares, Coronal Mass Ejections, and Space Weather
We take a broad look at the problem of identifying the magnetic solar causes of space weather. With the lackluster performance of extrapolations based upon magnetic field measurements in the
Differential Emission Measure Evolution as a Precursor of Solar Flares
We analyse the temporal evolution of the Differential Emission Measure (DEM) of solar active regions and explore its usage in solar flare prediction. The DEM maps are provided by the Gaussian
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space‐Weather HMI‐Active Region Patch (SHARP) data files.
Non-LTE inversions of a confined X2.2 flare
Obtaining the magnetic field vector accurately in the solar atmosphere is essential for studying changes in field topology during flares and to reliably model space weather. We tackle this problem by
Shape-based Feature Engineering for Solar Flare Prediction
This work describes a suite of novel shape-based features extracted from magnetogram images of the Sun using the tools of computational topology and computational geometry and shows that these abstract shape- based features outperform the features chosen by the human experts, and that a combination of the two feature sets improves the forecasting capability even further.
Flare-productive active regions
This review focuses on the formation and evolution of flare-productive ARs from both observational and theoretical points of view and shows that the improvement of observational instruments and modeling capabilities has significantly advanced understanding in the last decades.
Solar Flare Prediction Based on the Fusion of Multiple Deep-learning Models
The test results clearly show that this fusion model can make full use of the information related to solar flares and combine the advantages of each independent model to capture the evolution characteristics of solar flares, which is a much better performance than traditional statistical prediction models or any single machine-learning method.
Global Energetics of Solar Flares. XI. Flare Magnitude Predictions of the GOES Class
In this study we determine scaling relationships of observed solar flares that can be used to predict upper limits of the GOES-class magnitude of solar flares. The flare prediction scheme is based on


Statistical Assessment of Photospheric Magnetic Features in Imminent Solar Flare Predictions
In this study we use the ordinal logistic regression method to establish a prediction model, which estimates the probability for each solar active region to produce X-, M-, or C-class flares during
Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. II. Discriminant Analysis
We apply statistical tests based on discriminant analysis to the wide range of photospheric magnetic parameters described in a companion paper by Leka & Barnes, with the goal of identifying those
The Statistical Relationship between the Photospheric Magnetic Parameters and the Flare Productivity of Active Regions
Using line-of-sight Michelson Doppler Imager (MDI) magnetograms of 89 active regions and Solar Geophysical Data (SGD) flare reports, we explored, for the first time, the magnitude scaling
Statistical tests based on linear discriminant analysis are applied to numerous photospheric magnetic parameters, continuing toward the goal of identifying properties important for the production of
Flare occurrence is statistically associated with changes in several characteristics of the line-of-sight magnetic field in solar active regions (ARs). We calculated magnetic measures throughout the
Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression
We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to
Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms
A flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 h, was developed and it was found that k-NN has the highest performance among the three algorithms.
The number of published approaches to solar flare forecasting using photospheric magnetic field observations has proliferated recently, with widely varying claims about how well each works. As
Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called Support Vector Machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic
A Bayesian Approach to Solar Flare Prediction
A number of methods of flare prediction rely on classification of physical characteristics of an active region, in particular optical classification of sunspots, and historical rates of flaring for a