Analyzing AIA flare observations using convolutional neural networks

In order to efficiently analyse the vast amount of data generated by solar space missions and ground-based instruments, modern machine learning techniques such as decision trees, support vector machines (SVMs) and neural networks can be very useful. In this paper we present initial results from using a convolutional neural network (CNN) to analyse observations from the Atmospheric Imaging Assembly (AIA) in the 1600 A wavelength. The data is pre-processed to locate flaring regions where Hα flare ribbons are visible in the observations. The CNN is created and trained to automatically analyse the shape and position of the flare ribbons, by identifying whether a data set belongs into one of four classes: two-ribbon flare, compact/circular ribbon flare, limb flare or quiet Sun, with the final class acting as a control for any data included in the training or test sets where flaring regions are not present. The network created can classify flare ribbon observations into any of the four classes with a final accuracy of 94%. Initial results show that most of the flares are correctly classified with the limb flare class being the only class where accuracy drops and some observations are wrongly classified