Researchers have developed a new AI framework called Solar-CDC that can identify the origin of the slow solar wind. The framework uses self-supervised contrastive learning to analyze data from the Solar Orbiter spacecraft and separate two distinct populations of plasma. This is important because it helps scientists understand the composition of the solar wind, which is crucial for predicting space weather. The researchers claim that their method outperforms other approaches in identifying the correct clusters and recovering physical populations. --- Why it matters: This matters to AI researchers because it demonstrates the potential of self-supervised learning in solving complex problems in physics. By developing a framework that can analyze data from the Solar Orbiter, scientists can gain insights into the behavior of the solar wind and improve their understanding of space weather. Source: https://arxiv.org/abs/2608.22065