JW-SSD: A Multimodal Benchmark Dataset for Fine-Grained Sunspot Classification

Abstract

Accurate sunspot classification is essential for assessing the eruptive potential of solar active regions and forecasting space weather. We present JW-SSD, a high-quality multimodal benchmark dataset for fine-grained magnetic-type classification of sunspots. Constructed from SDO/HMI SHARP 720s data (2010-2023, Solar Cycles 24 and 25), JW-SSD comprises 36,553 co-registered magnetogram-continuum pairs from 2,507 active regions. Unlike conventional three-class schemes, JW-SSD refines the Mount Wilson classification into five physically meaningful categories ({\alpha}, \b{eta}, \b{eta}-{\delta}, \b{eta}-{\gamma}, \b{eta}-{\gamma}-{\delta}), enabling finer characterization of magnetic complexity. Rigorous quality control-including central meridian distance restriction, saturation filtering, and sharpness screening-ensures high data validity. The dataset is provided in both FITS and PNG formats, with standard training (29,243) and test (7,310) splits. Benchmark experiments with four representative architectures (U-Net, ResNet-50, EfficientNet-B0, and ViT-Small) yield high accuracy across all models (89.43%-94.78% on the three-class task), confirming that the dataset is reliably learnable across diverse modeling paradigms. JW-SSD has further been employed to train JW-SunSpot, a multimodal large language model that achieves the highest classification accuracy, demonstrating the dataset's broad applicability to both conventional networks and large-language-model-based approaches.