AI-Based Coronal Hole Detection and Solar Wind Model Validation

The properties and the spatial distribution of the large-scale structures of the Solar Corona (SC) determine the observed solar wind structure at 1 AU. Coronal Holes (CHs) are the primary source of the fast solar wind, which is the most geoeffective component of solar wind, and they appear as large dark patches in the Extreme Ultraviolet (EUV) images from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamic Observatory (SDO) and the Extreme Ultraviolet Imaging Telescope (EIT) on the Solar and Heliospheric Observatory (SoHO). These observatories provide images of the SC at different wavelengths, which enables the identification of CH morphology and other large-scale structures along a given line of sight. It is crucial to understand the CH regions and their properties for effective space weather forecasting. This work is part of the WindTRUST project, with the primary goal of improving the reliability of solar wind models for space weather forecasting. Here, we aim to develop an automatic threshold-based CH detection tool for predictions across solar cycles 23, 24, and 25. We also plan to integrate this CH detection tool into a solar wind model validation pipeline, creating a fully automated validation system that provides a quantitative assessment of predictions. We categorized the large-scale features of the SC, such as active regions, solar flares, coronal mass ejections (CMEs), and filaments, based on their spatial distribution, phase of the solar cycle, and additional properties, including the GOES solar flare class. A Sequential Neural Network (NN) model was then trained by optimizing the architecture of the hidden layers to achieve higher predictive accuracy. The resulting model estimates the threshold required for integration into the Coronal Hole (CH) detection scheme, thereby enabling automated, consistent identification of CH boundaries in EUV images across solar cycles 23, 24, and 25. To interpret the performance of our NN model, we divided the predicted CH results into solar minimum and maximum cases across the solar cycles 23, 24, and 25. We also provide a comparison of our CH detection results with those obtained from other detection tools. Once we identify CH contours from our model, we validate them using a diagnostic test against CH contours from the Potential Field Source Surface (PFSS) model (non-MHD) and the WindPredict (WP) model (Polytropic and Alfven Wave) (MHD). Finally, we couple the CH detection tool with the validation pipeline to develop an automation tool for solar wind predictions.