Deep-learning approach for McIntosh-based classification of solar active regions using HMI and MDI images

Abstract

Solar active regions (ARs) are the primary source of solar flares. There are plenty of studies where the statistical relationship between ARs magnetic field complexity and solar flares are shown. Usually, the complexity of ARs described with different numerical magnetic field parameters and characteristics calculated on top of them. Also, there is well known and widely adapted McIntosh classification scheme of sunspot groups, consists of three letters abbreviation. Solar Monitor’s flare prediction system’s based on this classification. Up to date, the classification is done manual once a day by the specialist. In this paper, we describe an automatic system based on convolutional neural networks. For neural network training, we used images from two big magnetogram databases (HMI and MDI images) covered together period from 1996 to the 2018 years. Our results show that the automated classification of Solar ARs is possible with a moderate success rate, which allows to use it in practical tasks.