Year: 2026
Type: article
Abstract: Accurate sunspot number estimation is essential for understanding the long-term evolution of solar activity and its impact on space weather. Sunspot numbers have been manually determined, leading to inconsistencies and observer-dependent biases. To address this, the World Data Center Sunspot Index and Long-term Solar Observations (WDC-SILSO) aggregates data from a global network of observatories to estimate the daily total sunspot number, enabling cross-validation and calibration across simultaneous observations. This study proposes a novel deep learning framework for automated total sunspot number calculation using solar full-disk continuum images from the Solar Dynamics Observatory. The method integrates U-Net for sunspot segmentation, K-means clustering for distinguishing umbrae from penumbrae, and You Only Look Once model for sunspot group detection. The selection of image-processing thresholds and neural network hyperparameters is optimized with respect to WDC-SILSO reference values during training. The results demonstrate a high correlation of 0.97 between the estimated and the WDC-SILSO daily total sunspot numbers. Furthermore, the framework offers a scalable approach suitable for future high-resolution solar observations.
Source: The Astrophysical Journal
Institutions: Eötvös Loránd University, University of Sheffield
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
Awards: G104587016, G134384773, G1580936446, G1594289875, G3569666997, G4109898726, G468960872, G4696173189, G4909751417, G5256511709, Application to STFC for Consolidated Grant Support for the Solar Physics and Space Plasma Research Centre (SP2RC) at The University of Sheffield, G5555375138, G5788995006, G6488906540, G6572658055, G7448167679, G8061683924, G8687263898, G993751968
OpenAlex ID: W7124887503