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 [ 1 ] 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 May 2014 , [ 2 ] combines these features with other features based on flaring history and a physical understanding of putative flaring processes , and [ 3 ] classifies these features to predict whether a solar active region will flare within a time period of T hours , where T = 2 and 24 .
This type of machine-learning algorithm is conceptually similar to a single-layer Convolutional Neural Network ( CNN ) with pre-specified filters that is trained using a linear classifier .
Such an approach may be useful since , at the present time , there are no physical models of flares available for real-time prediction .
We find that when optimizing for the True Skill Score ( TSS ) , photospheric vector magnetic field data combined with flaring history yields the best performance , and when optimizing for the area under the precision-recall curve , all the data are helpful .
Our model performance yields a 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 area under the precision-recall curve in the T = 2 and 24 hour cases , respectively .
These relatively high scores are similar to , but not greater than , other attempts to predict solar flares .
Given the similar values of algorithm performance across various types of models reported in the literature , we conclude that we can expect a certain baseline predictive capacity using these data .
This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere , transition region , and corona .