Automatic analysis of magnetograms for identification and classification of active regions using Deep Learning

Abstract

Some phenomena that occur in the Sun have consequences on Earth. Among these phenomena, solar flares release large amounts of radiation and energy that impact on Earth's life and technological systems. These flares usually come from sunspots, which derive from solar magnetic activities. One strategy to predict solar flares is to identify active regions, i. e., a group of sunspots with a high potential to cause solar flares. This paper reports the use of the deep learning technique to identify and classify active regions from magnetogram analysis. To achieve these tasks, we created a dataset with magnetograms and performed tests to choose the best deep learning models for the identification and classification of active regions. The results of the best models reached accuracies higher than 80% for both the identification and classification tasks. Based on these results, we implemented a system in Python to automate the complete identification and classification process, also reported in this paper.