📝 SummarXiv

Abstract

Sunspot numbers constitute the longest and most widely used record of solar activity, with direct implications for space weather forecasting and heliophysical research. Traditional sunspot counting relies on visual inspection or algorithmic feature detection, both of which are limited by subjectivity, image quality, and methodological inconsistencies. Recent advances in deep learning, particularly convolutional neural networks (CNNs), enable the direct use of solar imagery for automated prediction tasks, reducing reliance on manual feature engineering. In this work, we present a supervised vision-based regression framework to estimate daily sunspot numbers from full-disk continuum images acquired by the Helioseismic and Magnetic Imager (HMI) onboard NASA Solar Dynamics Observatory (SDO). Images from 2011-2024 were paired with daily sunspot numbers from the SILSO Version 2.0 dataset of the Royal Observatory of Belgium. After preprocessing and augmentation, a CNN was trained to predict scalar sunspot counts directly from pixel data. The proposed model achieved strong predictive performance, with R2 = 0.986 and RMSE = 6.25 on the test set, indicating close agreement with SILSO reference values. Comparative evaluation against prior studies shows that our approach performs competitively with, and in several cases outperforms, statistical and hybrid machine learning methods, while offering the novel advantage of bypassing explicit detection and manual feature extraction. Interpretability analyses using Grad-CAM and Integrated Gradients confirmed that the network consistently attends to sunspot regions when forming predictions. These results highlight the potential of deep vision-based approaches for operational solar monitoring, providing a scalable and automated pathway for real-time estimation of classical heliophysical indices