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The precise physical process that triggers solar flares is not currentlyunderstood. Here we attempt to capture the signature of this mechanism in solarimage data of various wavelengths and use these signatures to predict flaringactivity. We do this by developing an algorithm that [1] automaticallygenerates features in 5.5 TB of image data taken by the Solar DynamicsObservatory of the solar photosphere, chromosphere, transition region, andcorona during the time period between May 2010 and May 2014, [2] combines thesefeatures with other features based on flaring history and a physicalunderstanding of putative flaring processes, and [3] classifies these featuresto predict whether a solar active region will flare within a time period of $T$hours, where $T$ = 2 and 24. We find that when optimizing for the True SkillScore (TSS), photospheric vector magnetic field data combined with flaringhistory yields the best performance, and when optimizing for the area under theprecision-recall curve, all the data are helpful. Our model performance yieldsa TSS of $0.84 \pm 0.03$ and $0.81 \pm 0.03$ in the $T$ = 2 and 24 hour cases,respectively, and a value of $0.13 \pm 0.07$ and $0.43 \pm 0.08$ for the areaunder the precision-recall curve in the $T$ = 2 and 24 hour cases,respectively. These relatively high scores are similar to, but not greaterthan, other attempts to predict solar flares. Given the similar values ofalgorithm performance across various types of models reported in theliterature, we conclude that we can expect a certain baseline predictivecapacity using these data. This is the first attempt to predict solar flaresusing photospheric vector magnetic field data as well as multiple wavelengthsof image data from the chromosphere, transition region, and corona.

Flare Prediction Using Photospheric and Coronal Image Data