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 and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector m...

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Veröffentlicht in:The Astrophysical journal Jg. 798; H. 2; S. 135 - 11
Hauptverfasser: Bobra, M. G., Couvidat, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 10.01.2015
Schlagworte:
ALGORITHMS
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
Construction
MAGNETIC FIELDS
Mathematical analysis
METRICS
PHOTOSPHERE
SOLAR FLARES
Solar observatories
SPACE
SUN
Support vector machines
Vectors (mathematics)
ISSN:1538-4357, 0004-637X, 1538-4357
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Zusammenfassung: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 data set 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 2071 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.
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ISSN:1538-4357
0004-637X
1538-4357
DOI:10.1088/0004-637X/798/2/135