Developing an Automated Detection, Tracking, and Analysis Method for Solar Filaments Observed by CHASE via Machine Learning

Abstract

Studies on the dynamics of solar filaments have significant implications for understanding their formation, evolution, and eruption, which are of great importance for space weather warning and forecasting. The H α Imaging Spectrograph (HIS) on board the recently launched Chinese H α Solar Explorer (CHASE) can provide full-disk solar H α spectroscopic observations, which bring us an opportunity to systematically explore and analyze the plasma dynamics of filaments. The dramatically increased observation data require automated processing and analysis, which are impossible if dealt with manually. In this paper, we utilize the U-Net model to identify filaments and implement the Channel and Spatial Reliability Tracking algorithm for automated filament tracking. In addition, we use the cloud model to invert the line-of-sight velocity of filaments and employ the graph theory algorithm to extract the filament spine, which can advance our understanding of the dynamics of filaments. The favorable test performance confirms the validity of our method, which will be implemented in the following statistical analyses of filament features and dynamics of CHASE/HIS observations.

Keywords

• Solar filaments
• Astronomy image processing
• Convolutional neural networks