SHINE: Understanding the Physical Connection of the in-situ Properties and Coronal Origins of the Solar Wind with a Novel Artificial Intelligence Investigation

Understanding the solar wind is crucial to space weather science and forecasting because the properties of the solar wind plasma affect the local conditions in the space environment around Earth. These conditions are largely the result of the speed, structure, and magnetic fields carried by the solar wind plasma. This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar wind, through research that utilizes modern Artificial Intelligence (AI), Machine Learning (ML) and big data analysis algorithms to analyze space-based and NSF-funded ground based coronagraph observations.

The project is led by an early career female scientist and is cross-disciplinary, building a collaboration between solar physicists and data scientists. Graduate and undergraduate student researchers from under-represented groups in STEM will be supported.

The project is a four-year research program that applies state-of-the-art AI/ML technology to in-situ solar wind measurements from past, current, and future missions. The goal is to classify solar wind types and to determine their coronal source regions, to understand the physical connection between the solar wind’s in-situ properties and their coronal origins.

The team will use available observations from NASA space-based missions including the Advanced Composition Explorer, Wind, Parker Solar Probe, Ulysses and when available, Solar Orbiter (SO) 1. Spectroscopic data from the Solar and Heliospheric Observatory, Solar Terrestrial Relations Observatory, Hinode, Solar Dynamics Observatory and SO will provide magnetic field geometry and basic plasma diagnostics of the solar wind source regions.

Furthermore, NSF-funded ground based coronagraphs such as CoMP (2011-2018), MK4 (1998-2013), KCor (2013-today) and, when available, UCoMP2 will be used to provide additional solar context data and plasma diagnostics.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.