An approach of filtering simulated magnetospheric X-ray images based on self-supervised network and random forest

Abstract

Imaging is an important method for observing the Earth’s space environment. Future missions, such as the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE), aim to explore the interaction between the solar wind and the Earth’s magnetosphere via soft x-ray imaging. With the advent of these missions, a large number of magnetospheric images may be acquired. However, as the viewing geometry and solar wind conditions change, satellites sometimes fail to capture the magnetopause inside the field of view. We propose an approach that blends machine learning and deep learning to filter the simulated x-ray images for the SMILE mission, aiming to achieve automatic classification of the detected images. First, we performed magnetohydrodynamic simulations to derive the predicted SMILE x-ray images. Then, we used a self-supervised contrast feature extraction network to study the features of the images. Using this network, the random forest classifier can distinguish whether the subsolar point at the magnetopause has been detected. Finally, we designed the magnetopause filter to obtain the subsolar magnetopause images with observation positions outside the magnetosphere. As a result, the prediction accuracy of the classifier is up to 93%. And the F1 score is up to 95.5%. The stratified predictions allow an automatic screening of whether satellite magnetospheric images cover the subsolar magnetopause. These images, which have observation positions outside the magnetosphere, can be used to invert the three-dimensional magnetopause.