Analysing AIA Flare Observations using Convolutional Neural Networks

TL;DR

This paper demonstrates the use of a convolutional neural network to classify solar flare observations from AIA data into four categories with high accuracy, aiding automated solar event analysis.

Contribution

It introduces a CNN-based method for classifying solar flare ribbons in AIA images into four distinct classes, achieving 94% accuracy, which is a novel application in this context.

Findings

Achieved 94% classification accuracy on flare ribbon images.

Most classes are correctly identified, with some misclassification between limb and other flares.

The method automates analysis of large solar datasets efficiently.

Abstract

In order to efficiently analyse the vast amount of data generated by solar space missions and ground-base 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 1600A wavelength. The data is pre-processed to locate flaring regions where 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 each image 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 images are correctly classified with the compact flare class being the only class where accuracy drops below 90% ad some observations are wrongly classified as belonging to the limb class.