JW-VL: A Vision-language Model for Solar Physics

Abstract Vision-language models (VLMs) have achieved breakthrough progress in general knowledge domains, yet adaptation to specialized scientific fields remains challenging due to multimodal representation shifts and the limited integration of domain-specific knowledge. To address the limitations of general-purpose VLMs when applied to solar physics image recognition, analysis, and reasoning, we propose JinWu Vision-Language (JW-VL), a fine-tuned foundation model tailored for solar physics. The model integrates multi-wavelength observational data from both space-based and ground-based telescopes, encompassing representative spectral bands spanning the photosphere, chromosphere, and corona. Built upon a cross-modal alignment knowledge distillation framework, JW-VL learns a joint visual-semantic embedding that enables end-to-end modeling from raw solar observational data to downstream tasks, including solar image recognition, solar activity analysis via image-based question answering, and optical character recognition, while also supporting the construction of a multi-band, cross-instrument solar image benchmark data set. Furthermore, as a demonstration of interdisciplinary applicability, we developed a “Daily Solar Activity Reports” agent comprising core modules for solar activity level assessment, significant active region characterization, magnetic field complexity analysis, potential space weather impact assessment, and identifying active regions for targeted observation. While JW-VL may not yet meet the rigorous, high-precision demands of operational solar physics, it bridges raw observations and diverse downstream tasks, establishing a valuable methodological framework for applying multimodal deep learning to the field.