Deep learning based solar flare forecasting model. II. influence of image resolution

Due to the accumulation of solar observational data and the development of data-driven algorithms, deep learning methods are widely applied to build a solar flare forecasting model. Most of the works focus on how to design or select proper deep networks for the forecasting task. Nevertheless, the influence of image resolution on the learning based solar flare forecasting model has not been analyzed and discussed. In this Paper, we investigate the influence of the resolution of magnetograms on the accuracy of solar flare forecasting. We study the active regions by the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) magnetograms from 2010 to 2019. Then, we downsample them to get a database containing active regions with several resolutions. Afterwards, three deep neural networks (i) AlexNet, (ii) ResNet-18, and (iii) SqueezeNet are implemented to evaluate the performance of solar flare forecasting compared to different resolutions of magnetogram. In experiments, we first did comparative experiments on our own simulated HMI database with different resolutions. Then we conducted experiments on two selected actual overlapping databases, Hinode–HMI and Michelson Doppler Imager–HMI, to reconfirm our conclusions. The experiment results show that all the selected deep learning networks are insensitive to the resolution to a certain extent. We visualized the regions of interest of the network from an interpretable perspective and found that the deep learning network pays more attention to the global features extracted from active regions that are not sensitive to local information in magnetograms.