A universal method for solar filament detection from H-alpha observations using semi-supervised deep learning

Abstract

o he Observatory for Solar and Environmental Research (KSO; Austria) provide regular full-disk observations of the Sun in the core of the chromospheric Halpha absorption line. In this paper, we present a deep learning method that provides reliable extractions of solar filaments from Halpha filtergrams. First, we trained the object detection algorithm YOLOv5 with labeled filament data of ChroTel Halpha filtergrams. We used the trained model to obtain bounding boxes from the full GONG archive. In a second step, we applied a semi-supervised training approach where we used the bounding boxes of filaments to train the algorithm on a pixel-wise classification of solar filaments with u-net. We made use of the increased data set size, which avoids overfitting of spurious artifacts from the generated training masks. Filaments were predicted with an accuracy of 92<!PCT!>. With the resulting filament segmentations, physical parameters such as the area or tilt angle could be easily determined and studied. We demonstrated this in an example where we determined the rush-to-the pole for Solar Cycle 24 from the segmented GONG images. In a last step, we applied the filament detection to Halpha observations from KSO and demonstrated the general applicability of our method to Halpha filtergrams.