Data-driven segmentation of solar filaments based on ground-based instrument data

Abstract

The dynamic behavior of solar prominences and filaments is a precursor to coronal mass ejections (CMEs), which can disrupt Earth's magnetosphere and affect satellite communications. Systematic ground-based solar observations, conducted with high temporal resolution, are instrumental in monitoring these structures. Analysis of the morphological changes and destabilization processes of filaments and prominences captured in datasets can help identify early warning signs of potential eruptions. This capability is vital for developing reliable space weather forecasting systems, thereby mitigating the adverse effects of solar disturbances on Earth's technological infrastructure. Previously, we introduced hybrid algorithmic-data driven method which performance was shown on a set of Solar Dynamic Observatory (SDO) Atmospheric Imaging Assembly (AIA) instrument data performing segmentation of extreme ultraviolet (EUV) and shock waves. In this work, we extend certain aspects of this approach to image segmentation of solar features -- filaments and prominences using H-alpha Kanzelhohe Observatory data. We describe a leakage-aware, sparse multi-year sampling strategy. We present image segmentation models pre-trained on synoptic and normalized image data, followed by a discussion of how model performance depends on training-set engineering, data preprocessing techniques, and the choice of loss metrics. We perform a limited study, both analytically and numerically, of the effect of external, fixed normalization versus native input for compact U-Net-based segmentation of solar H-alpha filaments, also contributing to ongoing research on normalization in deep networks in general and compact models in particular.