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Predicting Coronal Mass Ejections Using Machine Learning Methods  

Monica Bobra   Submitted: 2016-03-23 12:26

Of all the activity observed on the Sun, two of the most energetic events are flares and Coronal Mass Ejections (CMEs). Usually, solar active regions that produce large flares will also produce a CME, but this is not always true (Yashiro et al., 2005). Despite advances in numerical modeling, it is still unclear which circumstances will produce a CME (Webb & Howard, 2012). Therefore, it is worthwhile to empirically determine which features distinguish flares associated with CMEs from flares that are not. At this time, no extensive study has used physically meaningful features of active regions to distinguish between these two populations. As such, we attempt to do so by using features derived from [1] photospheric vector magnetic field data taken by the Solar Dynamics Observatory's Helioseismic and Magnetic Imager instrument and [2] X-ray flux data from the Geostationary Operational Environmental Satellite's X-ray Flux instrument. We build a catalog of active regions that either produced both a flare and a CME (the positive class) or simply a flare (the negative class). We then use machine-learning algorithms to [1] determine which features distinguish these two populations, and [2] forecast whether an active region that produces an M- or X-class flare will also produce a CME. We compute the True Skill Statistic, a forecast verification metric, and find that it is a relatively high value of approximately 0.8 plus or minus 0.2. We conclude that a combination of six parameters, which are all intensive in nature, will capture most of the relevant information contained in the photospheric magnetic field.

Authors: Monica G. Bobra and Stathis Ilondis
Projects: GOES X-rays ,SDO-HMI,STEREO

Publication Status: Accepted for publication in the Astrophysical Journal
Last Modified: 2016-03-30 20:57
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Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm  

Monica Bobra   Submitted: 2014-11-11 20:43

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 and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large dataset of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a database of 2,071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the True Skill Statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.

Authors: Monica G. Bobra and Sebastien Couvidat
Projects: SDO-HMI

Publication Status: Accepted for publication in the Astrophysical Journal
Last Modified: 2014-11-12 12:36
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Predicting Coronal Mass Ejections Using Machine Learning Methods
Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm

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